bluuebunny/crossref_metadata_2025 huggingface dataset:
Outcomes and Prognostic Factors in Critical Patients with Hematologic Malignancies
Authors: Chen Chieh-Lung , Wang Sing-Ting , Cheng Wen-Chien , Wu Biing-Ru , Liao Wei-Chih , Hsu Wu-Huei
Published: January 2023

Patients with hematologic malignancies (HMs) have a significantly elevated risk of mortality compared to other cancer patients treated in the intensive care unit (ICU). The prognostic impact of numerous poor outcome indicators has changed, and research has yielded conflicting results. This study aims to determine the ICU and hospital outcomes and risk factors that predict the prognosis of critically ill patients with HMs. In this retrospective study, conducted at a referral hospital in Taiwan, 213 adult patients with HMs who were admitted to the medical ICU were evaluated. We collected clinical data upon hospital and ICU admission. Using a multivariate regression analysis, the predictors of ICU and hospital mortality were assessed. Then, a scoring system (Hospital outcome of critically ill patients with Hematological Malignancies (HHM)) was built to predict hospital outcomes. Most HMs (76.1%) were classified as high grade, and more than one-third of patients experienced a relapsed or refractory disease. The ICU and hospital mortality rates were 55.9% and 71.8%, respectively. Moreover, the disease severity was high (median Sequential Organ Failure Assessment (SOFA) score: 11 and Acute Physiology and Chronic Health Evaluation (APACHE II) score: 28). The multivariate analysis revealed that high-grade HMs, invasive mechanical ventilation requirement, renal replacement therapy initiation in the ICU, and a high SOFA score correlated with ICU mortality. Furthermore, a higher HHM score predicted hospital mortality. This study demonstrates that ICU mortality primarily correlates with the severity of organ dysfunction, whereas the disease status markedly influences hospital outcomes. Furthermore, the HHM score significantly predicts hospital mortality.
SARS-CoV-2 Vaccination and Clinical Presentation of COVID-19 in Patients Hospitalized during the Delta- and Omicron-Predominant Periods
Authors: Stupica Daša , Collinet-Adler Stefan , Kejžar Nataša , Poljak Mario , Štamol Tina
Published: January 2023

Evidence suggests that monovalent vaccine formulations are less effective against the Omicron SARS-CoV-2 than against previous variants. In this retrospective cohort study of hospitalized adults with PCR-confirmed COVID-19 during the Delta (October–November 2021) and Omicron (January–April 2022) variant predominant periods in Slovenia, we assessed the association between primary vaccination against SARS-CoV-2 and progression to critically severe disease (mechanical ventilation or death). Compared with the 529 patients hospitalized for acute COVID-19 during the Delta period (median age 65 years; 58.4% men), the 407 patients hospitalized during the Omicron period (median age 75 years; 50.6% men) were older, more often resided in long-term care facilities, and had higher Charlson comorbidity index scores. After adjusting for age, sex, the Charlson comorbidity index, the presence of immunocompromising conditions, and vaccination status, the patients admitted during the Omicron period had comparable odds of progressing to critically severe disease to those admitted during the Delta period. The 334/936 (35.7%) patients completing at least primary vaccination had lower odds of progression to critically severe disease and shorter hospital stay than unvaccinated patients; however, the protective effect of vaccination was less pronounced during the Omicron than during the Delta period. Although the Omicron variant appeared to better evade immunity induced by monovalent vaccines than the Delta variant, vaccination against SARS-CoV-2 remained an effective intervention to decrease morbidity and mortality in COVID-19 patients infected with the Omicron variant.
Systematic Review of Psychological Interventions for Quality of Life, Mental Health, and Hair Growth in Alopecia Areata and Scarring Alopecia
Authors: Maloh Jessica , Engel Tess , Natarelli Nicole , Nong Yvonne , Zufall Alina , Sivamani Raja K.
Published: January 2023

Alopecia is associated with significant psychological burden. There is limited evidence on the use of psychological interventions in conditions of hair loss. This manuscript systematically reviews the current state of literature on psychological treatments for quality of life, mental health, and hair growth in various forms of alopecia. PubMed and Embase were searched with predefined inclusion and exclusion criteria. Reference lists were also examined for relevant studies. Nine articles met our criteria and are included in this review. Eight of the articles related to alopecia areata and one related to scarring alopecia. Mindfulness-based stress reduction (MBSR) was found to improve quality of life-related subjective symptoms, relationship impacts, anxiety, phobia, distress, and psychological symptom intensity. Alopecia-specific collocated behavioral health (CLBH) treatment showed a trend for psychosocial improvement in areas such as appearance shame, activity avoidance, negative emotions, and coping. Hypnotherapy was found to improve anxiety and depression, quality of life measures, and alexithymia. There was also some evidence for significant hair growth with hypnosis, but the data are mixed. Psychotherapy combined with immunotherapy led to more hair growth, and supported self-confidence. Finally, coping strategies modulated the subjective burden of alopecia, and were associated with disease improvement. Further research will be necessary to better establish the efficacy and optimal administration of these interventions in alopecia.
Anti-Aging and Lightening Effects of Au-Decorated Zeolite-Based Biocompatible Nanocomposites in Epidermal Delivery Systems
Authors: Lee Seungyeon , Lee Geunjeong , Jeon Giyoung , Lee Hayeong , Park Suhyeon , Sohn Youngju , Park Youngkum , Ryu Seongwoo
Published: January 2023

The main challenges in developing zeolites as cosmetic drug delivery systems are their cytotoxicities and the formation of drug-loading pore structures. In this study, Au-decorated zeolite nanocomposites were synthesized as an epidermal delivery system. Thus, 50 nm-sized Au nanoparticles were successfully deposited on zeolite 13X (super cage (α) and sodalite (β) cage structures) using the Turkevich method. Various cosmetic drugs, such as niacinamide, sulforaphane, and adenosine, were loaded under in vitro and in vivo observations. The Au-decorated zeolite nanocomposites exhibited effective cosmetic drug-loading efficiencies of 3.5 to 22.5 wt% under various conditions. For in vitro cytotoxic observations, B16F10 cells were treated with various cosmetic drugs. Niacinamide, sulforaphane, and adenosine-loaded Au-decorated zeolite nanocomposites exhibited clear cell viability of over 80%. Wrinkle improvement and a reduction in melanin content on the skin surface were observed in vivo. The adenosine delivery system exhibited an enhanced wrinkle improvement of 203% compared to 0.04 wt% of the pure adenosine system. The niacinamide- and sulforaphane-loaded Au-decorated zeolite nanocomposites decreased the skin surface melanin content by 123% and 222%, respectively, compared to 2 and 0.01 wt% of pure niacinamide and sulforaphane systems, respectively. As a result, Au-decorated zeolite nanocomposites show great potential as cosmetic drug epidermal delivery systems for both anti-aging and lightening effects.
Differences between the Fittings of Dental Prostheses Produced by CAD-CAM and Laser Sintering Processes
Authors: Herrero-Climent Mariano , Punset Miquel , Molmeneu Meritxell , Brizuela Aritza , Gil Javier
Published: January 2023

Digital dentistry and new techniques for the dental protheses’ suprastructure fabrication have undergone a great evolution in recent years, revolutionizing the quality of dental prostheses. The aim of this work is to determine whether the best horizontal marginal fit is provided by the CAD-CAM technique or by laser sintering. These values have been compared with the traditional casting technique. A total of 30 CAD-CAM models, 30 laser sintering models, and 10 casting models (as control) were fabricated. The structures realized with chromium–cobalt (CrCo) have been made by six different companies, always with the same model. Scanning electron microscopy with a high-precision image analysis system was used, and 10,000 measurements were taken for each model on the gingival (external) and palatal (internal) side. Thus, a total of 1,400,000 images were measured. It was determined that the CAD-CAM technique is the one that allows the best adjustments in the manufacturing methods studied. The laser sintering technique presents less adjustment, showing the presence of porosities and volume contraction defects due to solidification processes and heterogeneities in the chemical composition (coring). The technique with the worst adjustments is the casting technique, containing numerous defects in the suprastructure. The statistical analysis of results reflected the presence of statistically significant gap differences between the three manufacturing methods analyzed (p < 0.05), with the samples manufactured by CAD-CAM and by traditional casting processes being the ones that showed lower and higher values, respectively. No statistically significant differences in fit were observed between the palatal and gingival fit values, regardless of the manufacturing method used. No statistically significant differences in adjustment between the different manufacturing centers were found, regardless of the process used.
Topological Optimization of an Offshore-Wind-Farm Power Collection System Based on a Hybrid Optimization Methodology
Authors: Song Dongran , Yan Jiaqi , Zeng Hongda , Deng Xiaofei , Yang Jian , Qu Xilong , Rizk-Allah Rizk M. , Snášel Václav , Joo Young Hoon
Published: January 2023

This paper proposes a hybrid optimization method to optimize the topological structure of an offshore-wind-farm power collection system, in which the cable connection, cable selection and substation location are optimally designed. Firstly, the optimization model was formulated, which integrates cable investment, energy loss and line construction. Then, the Prim algorithm was used to initialize the population. A novel hybrid optimization, named PSAO, based on the merits of the particle swarm optimization (PSO) and aquila optimization (AO) algorithms, was presented for topological structure optimization, in which the searching characteristics between PSO and AO are exploited to intensify the searching capability. Lastly, the proposed PSAO method was validated with a real case. The results showed that compared with GA, AO and PSO algorithms, the PSAO algorithm reduced the total cost by 4.8%, 3.3% and 2.6%, respectively, while achieving better optimization efficiency.
Numerical and Experimental Study on the Shutdown Transition Process of a Large Axial Flow Pump System Focusing on the Influence of Gate Control
Authors: Zhang Xiaowen , Hu Chongyang , Tang Fangping , Yang Fan , Song Xijie , Liu Chao , Shi Lijian
Published: January 2023

Large axial flow pump systems (LAPS) are widely used in coastal pump stations. In the actual operation of a LAPS, various accidents often occur during shutdown due to the unreasonable control of stop flow measures such as the gate. In this paper, based on the secondary development of Flowmaster numerical software, a numerical simulation study was conducted on the shutdown process of a LAPS with different gate control laws. It was found that the MBV of the shutdown process was greater if the gate was closed more slowly after the unit was powered off. When a 30 s shutdown scheme was used, the MBV during shutdown was 1.63Qr. When a 60s long shutdown scheme was used, the MBV during shutdown was 1.67Qr, an increase of 2.45%. When the 150s long shutdown scheme was used, the MVV during the stopping process reached 1.68Qr, which is an increase of 3.07%. The shutdown method of closing the gate in advance can significantly improve the violent fluctuations of the KCPs of a LAPS during the shutdown transition and will effectively reduce the backflow and the reverse speed of the pump during the shutdown process. Taking the total gate closing time of 120 s as an example, when the 25% gate was closed in advance, the MBV and MRS during the shutdown process were reduced by 14.31% and 1.93%. When the shutdown scenario of preclosing 100% of the gates was adopted, the MBV and MRS during shutdown were reduced by 96.31% and 100%.
Assessment of Tannin Tolerant Non-Saccharomyces Yeasts Isolated from Miang for Production of Health-Targeted Beverage Using Miang Processing Byproducts
Authors: Kodchasee Pratthana , Pharin Nattanicha , Suwannarach Nakarin , Unban Kridsada , Saenjum Chalermpong , Kanpiengjai Apinun , Sakar Dipayan , Shetty Kalidas , Zarnkow Martin , Khanongnuch Chartchai
Published: January 2023

This research demonstrated an excellent potential approach for utilizing Miang fermentation broth (MF-broth), a liquid residual byproduct from the Miang fermentation process as a health-targeted beverage. One hundred and twenty yeast strains isolated from Miang samples were screened for their potential to ferment MF-broth and four isolates, P2, P3, P7 and P9 were selected, based on the characteristics of low alcoholic production, probiotic properties, and tannin tolerance. Based on a D1/D2 rDNA sequence analysis, P2 and P7 were identified to be Wikerhamomyces anomalus, while P3 and P9 were Cyberlindnera rhodanensis. Based on the production of unique volatile organic compounds (VOCs), W. anomalus P2 and C. rhodanensis P3 were selected for evaluation of MF-broth fermentation via the single culture fermentation (SF) and co-fermentation (CF) in combination with Saccharomyces cerevisiae TISTR 5088. All selected yeasts showed a capability for growth with 6 to 7 log CFU/mL and the average pH value range of 3.91–4.09. The ethanol content of the fermented MF-broth ranged between 11.56 ± 0.00 and 24.91 ± 0.01 g/L after 120 h fermentation, which is categorized as a low alcoholic beverage. Acetic, citric, glucuronic, lactic, succinic, oxalic and gallic acids slightly increased from initial levels in MF-broth, whereas the bioactive compounds and antioxidant activity were retained. The fermented MF-broth showed distinct VOCs profiles between the yeast groups. High titer of isoamyl alcohol was found in all treatments fermented with S. cerevisiae TISTR 5088 and W. anomalus P2. Meanwhile, C. rhodanensis P3 fermented products showed a higher quantity of ester groups, ethyl acetate and isoamyl acetate in both SF and CF. The results of this study confirmed the high possibilities of utilizing MF-broth residual byproduct in for development of health-targeted beverages using the selected non-Saccharomyces yeast.
Impacts of Floods on Agriculture-Dependent Livelihoods in Sub-Saharan Africa: An Assessment from Multiple Geo-Ecological Zones
Authors: Balgah Roland Azibo , Ngwa Kester Azibo , Buchenrieder Gertrud Rosa , Kimengsi Jude Ndzifon
Published: January 2023

Surging extreme events, particularly floods, have stimulated growing research on their epidemiology, management, and effects on livelihoods in Sub-Saharan Africa (SSA), especially for agriculture-dependent households. Unfortunately, the topical literature is still characterized by independent, isolated cases, with limited relevance to understanding common flood effects across geographical space and time. We bridge this knowledge gap by analyzing the effects of multiple cases of flash, coastal and riverine-cum-pluvial (‘complex’) floods on agriculture-dependent livelihoods in three (Sudano Sahelian, Coastal and Western Highlands) geo-ecological zones in Cameroon. The analysis makes use of a sample of 2134 flood victims (1000 of them in the Sudano-Sahelian, 242 in the Coastal, and 892 in the Western Highlands zones) of 26 independent community floods: 11 in the Sudano-Sahelian, 3 in the Coastal, and 12 in the Western Highlands zone. Irrespective of flood type and geo-ecology, agriculture-dependent livelihoods were gravely impaired. However, the impacts on livelihoods and public goods (such as road or communication systems) significantly varied in the different geo-ecological zones. The study concludes with the need to include context-specificity in the flood impact assessment equation, while identifying common effects, as is the case with agriculture in this study. We emphasize the need to up-scale and comparatively analyze flood effects across space and time to better inform flood management policies across SSA.
A Bibliometric Analysis of Urban Ecosystem Services: Structure, Evolution, and Prospects
Authors: Shao Qilong , Peng Li , Liu Yichan , Li Yongchang
Published: January 2023

Urban areas are human–environmental systems that rely heavily on ecosystems for long-term sustainability, and ecosystems and their components are essential to city life. Previous reviews have drawn a range of conclusions relevant to the future analysis and implementation of ecological and facility assessments. Overall, however, there is still a need to develop more systematic methods to comprehensively assess and evaluate urban ecosystem services (UES) in a temporal dimension for use in both historical monitoring studies and prospective research. Using CiteSpace, this study assessed the history and current state of UES research from multiple angles, as well as identified research structures and emerging trends in this field. The main findings are as follows: (i) The study of UES can be divided into three distinct but interconnected stages: preparation, rise, and prosperity. There are substantial prospects for UES research to develop worldwide collaborative networks, since it is increasingly expanding from domains focused on ecological or environmental science to those that draw from a wider range of disciplines. (ii) UES has evolved slowly from large-scale research to more focused, smaller-scale studies. Research on the subject of UES focuses primarily on two primary areas: land-use change and the development of green infrastructure. (iii) Sustainability in cities is currently a hot issue, and an important area of this study is the role that urban ecosystem services plays in ensuring residents’ health and happiness.
Complicated Relationships between Anterior and Condylar Guidance and Their Clinical Implications—Comparison by Cone Beam Computed Tomography and Electronic Axiography—An Observational Cohort Cross-Sectional Study
Authors: Lassmann Łukasz , Nowak Zuzanna , Orthlieb Jean-Daniel , Żółtowska Agata
Published: January 2023

A complex prosthodontic treatment is believed to be more successful when the condylar path is replicated using the articulator. However, there is an ongoing major disagreement between the researchers as the exact relationship between the posterior and anterior determinants has not been clear. The purpose of this study was to investigate whether the protrusive movement of the mandible does correlate with the temporomandibular joint (TMJ) anatomy or with incised features. Subjects (15 males and 15 females) were qualified for this study based on an initial interview including the following criteria: age 21–23 (+/−1), no history of trauma, orthodontic treatment, or temporomandibular disorders (TMD). For each patient, the angle of the condylar path, incisal guidance angle (IGA), interincisal angle, as well as overbite and overjet were measured on cone beam computed tomography (CBCT). This was followed by the examination with the Modjaw® electronic axiograph recording and calculating the functional sagittal condylar guidance angle (SCGA) for the right and left TMJ during the protrusion. The results show that the mean functional axiographic measurement of SCGA in protrusion significantly correlates with the TMJ anatomy presented on CBCT. Moreover, a significant correlation was found between the values of SCGA in the functional and anatomical measurements in all its variants. It turned out that, statistically, the AB measurement was the most accurate. Finally, results showed that incisal relationships of permanent teeth such as overbite, overjet, incisal guidance angle and interincisal angle do not correlate with TMJ anatomy, and therefore, regarding an analyzed study group, do not affect the TMJ formation in young adults.
A Crack Defect Detection and Segmentation Method That Incorporates Attention Mechanism and Dimensional Decoupling
Authors: He Lixin , Liu Wangwei , Li Yiming , Wang Handong , Cao Shenjie , Zhou Chengying
Published: January 2023

In this work, we propose a new crack image detection and segmentation method for addressing the issues regarding the poor detection of crack structures in certain complex background conditions, such as the light and shadow, and the easy-to-lose details in segmentation. This method can be categorized into two phases, where the first one is the coding phase. In this phase, the channel attention mechanism and crack characteristics, using the correlation channel with different scales increasing the network robustness and ability of feature extraction, have been introduced to decouple the channel dimension and space dimension. It also avoids underfitting caused by information redundancy during the jumping connection. In the second stage, i.e., the decoding stage, the spatial attention mechanism has been introduced to capture the crack edge information through the global maximum pooling and global average pooling of the high-dimensional features. Then, the correlation between the space and channel has been recovered through multiscale image information fusion to achieve accurate crack positioning. Furthermore, the Dice loss function has been employed to solve the problem of pixel imbalance between the categories. Finally, the proposed method has been tested and compared with existing methods. The experimental results illustrate that our method has a higher crack segmentation accuracy than existing methods. Furthermore, the mean intersection over the union ratio reaches 87.2% on the public dataset and 83.9% on the self-built dataset, and it has a better segmentation effect and richer details. It can solve the problem of crack image detection and segmentation under a complex background.
An Effective Fuzzy Clustering of Crime Reports Embedded by a Universal Sentence Encoder Model
Authors: Pramanik Aparna , Das Asit , Pelusi Danilo , Nayak Janmenjoy
Published: January 2023

Crime reports clustering is crucial for identifying and preventing criminal activities that frequently happened in society. In the proposed work, named entities in a report are recognized to extract the crime-related phrases and subsequently, the phrases are preprocessed by applying stopword removal and lemmatization operations. Next, the module of the universal encoder model, called the transformer, is applied to extract phrases of the report to get a sentence embedding for each associated sentence, aggregation of which finally provides the vector representation of that report. An innovative and efficient graph-based clustering algorithm consisting of splitting and merging operations has been proposed to get the cluster of crime reports. The proposed clustering algorithm generates overlapping clusters, which indicates the existence of reports of multiple crime types. The fuzzy theory has been used to provide a score to the report for expressing its membership into different clusters, and accordingly, the reports are labelled by multiple categories. The efficiency of the proposed method has been assessed by taking into account different datasets and comparing them with other state-of-the-art approaches with the help of various performance measure metrics.
On the Approximate Polar Curves of Foliations
Authors: Fernández-Sánchez Percy , García Barroso Evelia R. , Saravia-Molina Nancy
Published: January 2023

We present a decomposition theorem of the generic polar curves of a generalized curve foliation with only one separatrix and the Hamiltonian foliations defined by the approximate roots of the generatrix. This is a generalization to foliations of the decomposition theorem of approximate Jacobians given by García Barroso and Gwoździewicz for plane branches.
Stochastic Configuration Based Fuzzy Inference System with Interpretable Fuzzy Rules and Intelligence Search Process
Authors: Zhou Wei , Li Hongxing , Bao Menghong
Published: January 2023

In this paper, a stochastic configuration based fuzzy inference system with interpretable fuzzy rules (SCFS-IFRs) is proposed to improve the interpretability and performance of the fuzzy inference system and determine autonomously an appropriate model structure. The proposed SCFS-IFR first accomplishes a fuzzy system through interpretable linguistic fuzzy rules (ILFRs), which endows the system with clear semantic interpretability. Meanwhile, using an incremental learning method based on stochastic configuration, the appropriate architecture of the system is determined by incremental generation of ILFRs under a supervision mechanism. In addition, the particle swarm optimization (PSO) algorithm, an intelligence search technique, is used in the incremental learning process of ILFRs to obtain better random parameters and improve approximation accuracy. The performance of SCFS-IFRs is verified by regression and classification benchmark datasets. Regression experiments show that the proposed SCFS-IFRs perform best on 10 of the 20 data sets, statistically significantly outperforming the other eight state-of-the-art algorithms. Classification experiments show that, compared with the other six fuzzy classifiers, SCFS-IFRs achieve higher classification accuracy and better interpretation with fewer rules.
Capital Structure Theory: Past, Present, Future
Authors: Brusov Peter , Filatova Tatiana
Published: January 2023

The purpose of this review is to analyze all existing theories of the capital structure (with their advantages and disadvantages) in order to understand all aspects of the problem and make correct management decisions in practice. The role of the capital structure is that the correct determination of the optimal capital structure allows the company’s management to maximize the capitalization of the company and the long-term goal of the function of any company. The review examines the state of the capital structure and capital cost theory from the middle of the last century, when the first quantitative theory was created, to the present. The two main theories, Modigliani–Miller (MM) and Brusov–Filatova–Orekhova (BFO), are discussed and analyzed, as well as their numerous modifications and generalizations. Additionally, discussed is the latest stage in the development of the theory of capital structure, which began a couple of years ago and is associated with the adaptation of the two main theories of capital structure (Brusov–Filatova–Orekhova and Modigliani–Miller) to establish the practice of the function of companies. This generalization takes into account the real conditions of the work of the companies. It was noted that taking into account some effects that are present in economic practice (such as variable income, frequent payments of tax on income, advance payments of tax on income, etc.) brings both theories closer, and even the Modigliani–Miller theory, with all its many limitations, becomes more applicable in economic practice. However, it should be remembered that the Modigliani–Miller theory is only true for perpetual companies, while the BFO theory is valid for companies of any age, and from this point of view, they never coincide.
A Novel Double-Layered Central Pattern Generator-Based Motion Controller for the Hexapod Robot
Authors: Zhang Ying , Qiao Guifang , Wan Qi , Tian Lei , Liu Di
Published: January 2023

To implement the various movement control of the hexapod robot, a motion controller based on the double-layered central pattern generator (CPG) is proposed in this paper. The novel CPG network is composed of a rhythm layer and a pattern layer. The CPG neurons are constructed based on Kuramoto nonlinear oscillator. The parameters including the frequency, coupling strength, and phase difference matrix of the CPG network for four typical gaits are planned. The mapping relationship between the signals of the CPG network and the joint trajectories of the hexapod robot is designed. The co-simulations and experiments have been conducted to verify the feasibility of the proposed CPG-based controller. The actual average velocities of the wave gait, the tetrapod gait, the tripod gait, and the self-turning gait are 10.8 mm/s, 25.5 mm/s, 37.8 mm/s and 26°/s, respectively. The results verify that the hexapod robot with the proposed double-layered CPG-based controller can perform stable and various movements.
A Switchable Chaotic Oscillator with Multiscale Amplitude/Frequency Control
Authors: Sheng Zhoubin , Li Chunbiao , Gao Yikai , Li Zhinan , Chai Lin
Published: January 2023

For the wide frequency spectrum of chaotic signals, it is difficult to realize chaotic signal conditioning. Therefore, researchers turn to the exploration of chaotic systems with independent non-bifurcation control for easy chaos modification. In this paper, a system with only one non-quadratic term is modified for providing multiscale amplitude/frequency control. By adjusting the feedback with an odd higher degree term, a switchable chaotic oscillator is obtained, which provides the different scales of amplitude/frequency control with the chaotic signal. Multisim-based circuit simulation shows the efficiency and convenience of chaotic signal control.
Mathematical Model Assisted Six-Sigma Approach for Reducing the Logistics Costs of a Pipe Manufacturing Company: A Novel Experimental Approach
Authors: Orbak Âli Yurdun , Küçük Metin , Akansel Mehmet , Sharma Shubham , Li Changhe , Kumar Raman , Singh Sunpreet , Di Bona Gianpaolo
Published: January 2023

This research addresses and analyzes the results of a six-sigma approach used to optimize the logistics costs of a pipe manufacturing company. Two mathematical models are developed for containers to control the company’s logistics. The Mathematical Programming Language (MPL) software is used to generate and solve these models. The results verify that the proposed mathematical models result in the company’s logistics improvement, especially in the DMAIC (define, measure, analyze, improve, and control) cycle by providing flexibility in choosing the most appropriate containers for logistics.
Unsupervised Representation Learning with Task-Agnostic Feature Masking for Robust End-to-End Speech Recognition
Authors: Kim June-Woo , Chung Hoon , Jung Ho-Young
Published: January 2023

Unsupervised learning-based approaches for training speech vector representations (SVR) have recently been widely applied. While pretrained SVR models excel in relatively clean automatic speech recognition (ASR) tasks, such as those recorded in laboratory environments, they are still insufficient for practical applications with various types of noise, intonation, and dialects. To cope with this problem, we present a novel unsupervised SVR learning method for practical end-to-end ASR models. Our approach involves designing a speech feature masking method to stabilize SVR model learning and improve the performance of the ASR model in a downstream task. By introducing a noise masking strategy into diverse combinations of the time and frequency regions of the spectrogram, the SVR model becomes a robust representation extractor for the ASR model in practical scenarios. In pretraining experiments, we train the SVR model using approximately 18,000 h of Korean speech datasets that included diverse speakers and were recorded in environments with various amounts of noise. The weights of the pretrained SVR extractor are then frozen, and the extracted speech representations are used for ASR model training in a downstream task. The experimental results show that the ASR model using our proposed SVR extractor significantly outperforms conventional methods.
A Cross-Level Requirement Trace Link Update Model Based on Bidirectional Encoder Representations from Transformers
Authors: Tian Jiahao , Zhang Li , Lian Xiaoli
Published: January 2023

Cross-level requirement trace links (i.e., links between high-level requirements (HLRs) and low-level requirements (LLRs)) record the top-down decomposition process of requirements and support various development and management activities (e.g., requirement validation). Undoubtedly, updating trace links synchronously with requirement changes is critical for their constant availability. However, large-scale open-source software that is rapidly iterative and continually released has numerous requirements that are dynamic. These requirements render timely update of trace links challenging. To address these problems, in this study, a novel deep-learning-based method, deep requirement trace analyzer fusing heterogeneous features (DRAFT), was proposed for updating trace links between various levels of requirements. Considering both the semantic information of requirement text descriptions and the process features based on metadata, trace link data accumulated in the early stage are comprehensively used to train the trace link identification model. Particularly, first, we performed second-phase pre-training for the bidirectional encoder representations from transformers (BERT) language model based on the project document corpus to realize project-related knowledge transfer, which yields superior text embedding. Second, we designed 11 heuristic features based on the requirement metadata in the open-source system. Based on these features and semantic similarity between HLRs and LLRs, we designed a cross-level requirement tracing model for new requirements. The superiority of DRAFT was verified based on the requirement datasets of eight open-source projects. The average F1 and F2 scores of DRAFT were 69.3% and 76.9%, respectively, which were 16.5% and 22.3% higher than baselines. An ablation experiment proved the positive role of two key steps in trace link construction.
Computational Model of Noradrenaline Modulation of Astrocyte Responses to Synaptic Activity
Authors: Verisokin Andrey , Verveyko Darya , Kirsanov Artem , Brazhe Alexey , Postnov Dmitry
Published: January 2023

The mathematical modeling of synaptically connected neuronal networks is an established instrument for gaining insights into dynamics of neuronal ensembles and information processing in the nervous system. Recently, calcium signaling in astrocytes—glial cells controlling local tissue metabolism and synapse homeostasis—and its corresponding downstream effect on synaptic plasticity and neuromodulation appeared in the limelight of modeling studies. Here, we used mechanism-based mathematical modeling to disentangle signaling pathways and feedback loops in the astrocytic calcium response to noradrenaline, an important neuromodulator marking periods of heightened alertness and arousal. The proposed model is based on an experiment-based 2D representation of astrocyte morphology, discrete random glutamate synapses with placement probability defined by the morphology pattern, and spatially heterogeneous noradrenaline sources, reflecting axonal varicosities of the adrenergic axons. Both glutamate and noradrenaline drive Ca2+ dynamics in the astrocyte in an additive or synergistic manner. Our simulations replicate the global activation of astrocytes by noradrenaline and predict the generation of high-frequency Ca2+ waves in a dose-dependent manner and the preferred Ca2+ wave origination near noradrenaline release sites if they colocalise with high-density clusters of glutamate synapses. We tested positive feedback loops between noradrenaline release and glutamate spillover directly or mediated by gliotransmitter release from the activated astrocyte. The simulations suggest that the coupled stochastic drive by glutamate and noradrenaline release converges on the graded modulation of the IP3 level, which is translated into whole-cell Ca2+ waves of different frequencies. Thus, the proposed approach is supported by experimental data and can be used to address situations inaccessible directly by experiment, and is a starting point for a more detailed model that includes other signaling mechanisms providing negative feedback.
A Weights Direct Determination Neural Network for International Standard Classification of Occupations
Authors: Lagios Dimitris , Mourtas Spyridon D. , Zervas Panagiotis , Tzimas Giannis
Published: January 2023

Multiclass classification is one of the most popular machine learning tasks. The main focus of this paper is to classify occupations according to the International Standard Classification of Occupations (ISCO) using a weights and structure determination (WASD)-based neural network. In general, WASD-trained neural networks are known to overcome the drawbacks of conventional back-propagation trained neural networks, such as slow training speed and local minimum. However, WASD-based neural networks have not yet been applied to address the challenges of multiclass classification. As a result, a novel WASD for multiclass classification (WASDMC)-based neural network is introduced in this paper. When applied to two publicly accessible ISCO datasets, the WASDMC-based neural network displayed superior performance across all measures, compared to some of the best-performing classification models that the MATLAB classification learner app has to offer.
Multi-Criteria Decision-Making for Machine Selection in Manufacturing and Construction: Recent Trends
Authors: Hagag Asmaa M. , Yousef Laila S. , Abdelmaguid Tamer F.
Published: January 2023

As the number of alternative machines has increased and their technology has been continuously developed, the machine selection problem has attracted many researchers. This article reviews recent developments in applying multi-criteria decision-making (MCDM) methods for selecting machines in the manufacturing and construction industries. Selected articles are classified according to the application area and the applied MCDM method. By focusing on the last five years, this paper identifies recent trends in developing and using these methods. Results suggest that there has been a noticeable growth in the utilization of MCDM techniques for machine selection problems in both sectors. It is also noted that several decision-support tools and methods have been developed and successfully applied during this period. Accordingly, needs and directions for future research are discussed.
A Cloud-Based Software Defect Prediction System Using Data and Decision-Level Machine Learning Fusion
Authors: Aftab Shabib , Abbas Sagheer , Ghazal Taher M. , Ahmad Munir , Hamadi Hussam Al , Yeun Chan Yeob , Khan Muhammad Adnan
Published: January 2023

This research contributes an intelligent cloud-based software defect prediction system using data and decision-level machine learning fusion techniques. The proposed system detects the defective modules using a two-step prediction method. In the first step, the prediction is performed using three supervised machine learning techniques, including naïve Bayes, artificial neural network, and decision tree. These classification techniques are iteratively tuned until the maximum accuracy is achieved. In the second step, the final prediction is performed by fusing the accuracy of the used classifiers with a fuzzy logic-based system. The proposed fuzzy logic technique integrates the predictive accuracy of the used classifiers using eight if–then fuzzy rules in order to achieve a higher performance. In the study, to implement the proposed fusion-based defect prediction system, five datasets were fused, which were collected from the NASA repository, including CM1, MW1, PC1, PC3, and PC4. It was observed that the proposed intelligent system achieved a 91.05% accuracy for the fused dataset and outperformed other defect prediction techniques, including base classifiers and state-of-the-art ensemble techniques.
Trans-Planckian Censorship and Spacetime Singularities
Authors: Cotsakis Spiros , Miritzis John
Published: January 2023

We study the effects of trans-Planckian censorship conjecture (TCC) bounds on geodesic completeness of spacetime and the associated existence for an infinite proper time. Using Gronwall’s lemma, TCC bounds can be derived directly, leading to a result about the absence of blowup solutions. We show that the TCC provides part of the required criteria for geodesic completeness, and we then provide the remaining ones, the norm of the extrinsic curvature being bounded away from zero. We also discuss the importance of these results for the classical evolution of Friedmann universes under the assumptions of global and regular hyperbolicity.
Anticoagulant Property of a Sulfated Polysaccharide with Unique Structural Characteristics from the Green Alga Chaetomorpha aerea
Authors: Qin Ling , Yang Yajing , Mao Wenjun
Published: January 2023

Sulfated polysaccharides from marine algae have attracted a great amount of attentions for the development of marine drugs due to their unique structural features, and they are great potential sources of naturally occurring anticoagulant agents. The genus Chaetomorpha is one of the largest genera in green algae and has a worldwide distribution. In the present study, a homogeneous polysaccharide from Chaetomorpha aerea, designated as PCA, was obtained by alkali extraction, anion-exchange and size-exclusion chromatography. Based on the results of chemical and spectroscopic analyses, PCA was a sulfated galactoarabinan which was mainly constituted of a backbone of →4)-β-l-Arap-(1→ unit, partially sulfated at C-3 of →4)-β-l-Arap-(1→ and C-4 of →6)-α-d-Galp-(1→. The side chains consisting of →6)-α-d-Galp-(1→ and →5)-α-l-Araf-(1→ residues were in C-2 of →4)-β-l-Arap-(1→ unit. PCA had a strong anticoagulant activity in vitro as evaluated by the assays of activated partial thromboplastin time, thrombin time and fibrinogen level. The obvious anticoagulant activity in vivo of PCA was also found. PCA significantly inhibited the activities of the intrinsic coagulation factors XII, XI, IX and VIII, and exhibited weak inhibition effects on the common coagulation factors II and X. The anticoagulant mechanism of PCA was attributed to strong thrombin inhibition potentiated by heparin cofactor II or antithrombin III, and it also possessed an apparent inhibition effect on coagulation factor Xa mediated by antithrombin III. The investigation demonstrated that PCA could be a promising anticoagulant agent for health promotion and the treatment of thrombotic diseases.
Marine Natural and Nature-Inspired Compounds Targeting Peroxisome Proliferator Activated Receptors (PPARs)
Authors: D’Aniello Enrico , Amodeo Pietro , Vitale Rosa
Published: January 2023

Peroxisome proliferator-activated receptors α, γ and β/δ (PPARα, PPARγ, and PPARβ/δ) are a family of ligand-activated transcriptional factors belonging to the superfamily of nuclear receptors regulating the expression of genes involved in lipid and carbohydrate metabolism, energy homeostasis, inflammation, and the immune response. For this reason, they represent attractive targets for the treatment of a variety of metabolic diseases and, more recently, for neurodegenerative disorders due to their emerging neuroprotective effects. The degree of activation, from partial to full, along with the selectivity toward the different isoforms, greatly affect the therapeutic efficacy and the safety profile of PPAR agonists. Thus, there is a high interest toward novel scaffolds with proper combinations of activity and selectivity. This review intends to provide an overview of the discovery, optimization, and structure–activity relationship studies on PPAR modulators from marine sources, along with the structural and computational studies that led to their identification and/or elucidation, and rationalization of their mechanisms of action.
Body Mass Index and Waist Circumference as Determinants of Hemostatic Factors in Participants of a Population-Based Study
Authors: Iglesias Morcillo Maximilian , Freuer Dennis , Peters Annette , Heier Margit , Meisinger Christine , Linseisen Jakob
Published: January 2023

Background: In contrast to studies in patients, an association between obesity and blood coagulation factors has not been established in the population. If confirmed it could become a target for primary prevention. Objective: To investigate the relationship between Body Mass Index (BMI) and waist circumference (WC) with plasma concentrations of antithrombin III, D-dimers, fibrinogen D, protein S, factor VIII, activated partial thromboplastin time (aPTT), quick value, and international normalized ratio (INR) in the general population. Materials and Methods: Participants of the Cooperative Health Research in the Region of Augsburg (KORA) S4 study who took part in the KORA Fit follow-up (2018–2019, aged 54–74 years) examination were eligible. Citrate plasma samples were collected in fasted participants. After the exclusion of participants with anticoagulative treatment, 776 participants (420 women and 356 men) with analytic data on hemostatic factors were included in the present analysis. Linear regression models were used to explore the association between BMI or WC with hemostatic markers, adjusted for sex, age, alcohol consumption, education, smoking status, and physical activity. In a second model, additional adjustments were made for the prevalence of stroke, hypertension, myocardial infarction, serum non-HDL cholesterol, and serum triglycerides. Results: In the multivariable models (with or without health conditions), significant positive associations with BMI were obtained for plasma concentrations of D-dimers, factor VIII, fibrinogen D, protein S, and quick value, while INR and antithrombin III were inversely associated. Similar to BMI, WC was significantly associated with all hemostatic factors, except for aPTT. Conclusion: In this population-based study, both increasing BMI and WC affect the blood coagulation system. Thus, modification of a prothrombotic coagulation profile emerged as a potential target for primary prevention in obese subjects.
Cardiac Computed Tomography Evaluation of Association of Left Ventricle Disfunction and Epicardial Adipose Tissue Density in Patients with Low to Intermediate Cardiovascular Risk
Authors: Chiocchi Marcello , Cavallo Armando , Pugliese Luca , Cesareni Matteo , Pasquali Daniela , Accardo Giacomo , De Stasio Vincenzo , Spiritigliozzi Luigi , Benelli Leonardo , D’Errico Francesca , Cerimele Cecilia , Floris Roberto , Garaci Francesco , Di Donna Carlo
Published: January 2023

Background and objectives: Epicardial adipose tissue density (EAD) has been associated with coronary arteries calcium score, a higher load of coronary artery disease (CAD) and plaque vulnerability. This effect can be related to endocrine and paracrine effect of molecules produced by epicardial adipose tissue (EAT), that may influence myocardial contractility. Using coronary computed tomography angiography (CCT) the evaluation of EAD is possible in basal scans. The aim of the study is to investigate possible associations between EAD and cardiac function. Material and Methods: 93 consecutive patients undergoing CCT without and with contrast medium for known or suspected coronary CAD were evaluated. EAD was measured on basal scans, at the level of the coronary ostia, the lateral free wall of the left ventricle, at the level of the cardiac apex, and at the origin of the posterior interventricular artery. Cardiac function was evaluated in post-contrast CT scans in order to calculate ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV), and stroke volume (SV). Results: A statistically significant positive correlation between EAD and ejection fraction (r = 0.29, p-value < 0.01) was found. Additionally, a statistically significant negative correlation between EAD and ESV (r = −0.25, p-value < 0.01) was present. Conclusion: EAD could be considered a new risk factor associated with reduced cardiac function. The evaluation of this parameter with cardiac CT in patients with low to intermediate cardiovascular risk is possible.