bluuebunny/crossref_metadata_2025 huggingface dataset:
Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
Authors: You Jie , Lee Joonwhoan
Published: August 2024

Pansori, a traditional Korean form of musical storytelling, is characterized by performances involving a vocalist and a drummer. It is well-known for the singer’s expressive narrative (aniri) and delicate gesture with fan in hand. The classical Pansori repertoires mostly tell love, satire, and humor, as well as some social lessons. These performances, which can extend from three to five hours, necessitate that the vocalist adheres to precise rhythmic structures. The distinctive rhythms of Pansori are crucial for conveying both the narrative and musical expression effectively. This paper explores the challenge of open-set recognition, aiming to efficiently identify unknown Pansori rhythm patterns while applying the methodology to diverse acoustic datasets, such as sound events and genres. We propose a lightweight deep learning-based encoder–decoder segmentation model, which employs a 2-D log-Mel spectrogram as input for the encoder and produces a frame-based 1-D decision along the temporal axis. This segmentation approach, processing 2-D inputs to classify frame-wise rhythm patterns, proves effective in detecting unknown patterns within time-varying sound streams encountered in daily life. Throughout the training phase, both center and supervised contrastive losses, along with cross-entropy loss, are minimized. This strategy aimed to create a compact cluster structure within the feature space for known classes, thereby facilitating the recognition of unknown rhythm patterns by allocating ample space for their placement within the embedded feature space. Comprehensive experiments utilizing various datasets—including Pansori rhythm patterns (91.8%), synthetic datasets of instrument sounds (95.1%), music genres (76.9%), and sound datasets from DCASE challenges (73.0%)—demonstrate the efficacy of our proposed method to detect unknown events, as evidenced by the AUROC metrics.
Efficient Detection of Irrelevant User Reviews Using Machine Learning
Authors: Kim Cheolgi , Kim Hyeon Gyu
Published: August 2024

User reviews such as SNS feeds and blog writings have been widely used to extract opinions, complains, and requirements about a given place or product from users’ perspective. However, during the process of collecting them, a lot of reviews that are irrelevant to a given search keyword can be included in the results. Such irrelevant reviews may lead to distorted results in data analysis. In this paper, we discuss a method to detect irrelevant user reviews efficiently by combining various oversampling and machine learning algorithms. About 35,000 user reviews collected from 25 restaurants and 33 tourist attractions in Ulsan Metropolitan City, South Korea, were used for learning, where the ratio of irrelevant reviews in the two kinds of data sets was 53.7% and 71.6%, respectively. To deal with skewness in the collected reviews, oversampling algorithms such as SMOTE, Borderline-SMOTE, and ADASYN were used. To build a model for the detection of irrelevant reviews, RNN, LSTM, GRU, and BERT were adopted and compared, as they are known to provide high accuracy in text processing. The performance of the detection models was examined through experiments, and the results showed that the BERT model presented the best performance, with an F1 score of 0.965.
Evaluation of Lipid Damage, Microbial Spoilage and Sensory Acceptance of Chilled Pouting (Trisopterus luscus), an Underutilized Lean Fish Species
Authors: Maroto Julio , Trigo Marcos , Miranda José M. , Aubourg Santiago P. , Barros-Velázquez Jorge
Published: August 2024

The present study focused on the use of pouting (Trisopterus luscus), an underutilized gadoid fish species, as a fresh product of potential commercial interest. Accordingly, non-degutted pouting specimens (145–195 g and 15–22 cm) were stored under chilling conditions (0 °C) for microbial, chemical and sensory analyses to evaluate their commercial quality and shelf life. A progressive quality loss (p < 0.05) was detected for this lean species (5.58 g lipids·kg−1 muscle) as the storage time increased, as determined through microbial (aerobes, psychrotrophs and Enterobacteriaceae counts), lipid hydrolysis (free fatty acid value), lipid oxidation (conjugated diene and triene, thiobarbituric acid reactive substance, and fluorescence values) and sensory acceptance assessment. A detailed comparison to related lean fish species revealed that the pouting exhibited a fast quality breakdown under refrigeration conditions. Thus, after 9 d of chilled storage, the psychrotroph counts exceeded the acceptable limits (8.54 log CFU·g−1), and the fish specimens were found to be rejectable, with the sensory panel, external odor and eye appearance being the limiting factors. In contrast, the pouting specimens exhibited high quality after 3 d of storage, with the quality being still acceptable after 6 d. According to the current search for novel, underutilized species, pouting is proposed as a promising source.
Genetic and Modifiable Risk Factors for Postoperative Complications of Total Joint Arthroplasty: A Genome-Wide Association and Mendelian Randomization Study
Authors: Guo Sijia , Zhang Jiping , Li Huiwu , Cheng Cheng-Kung , Zhang Jingwei
Published: August 2024

Background: Total joint arthroplasty (TJA) is an orthopedic procedure commonly used to treat damaged joints. Despite the efficacy of TJA, postoperative complications, including aseptic prosthesis loosening and infections, are common. Moreover, the effects of individual genetic susceptibility and modifiable risk factors on these complications are unclear. This study analyzed these effects to enhance patient prognosis and postoperative management. Methods: We conducted an extensive genome-wide association study (GWAS) and Mendelian randomization (MR) study using UK Biobank data. The cohort included 2964 patients with mechanical complications post-TJA, 957 with periprosthetic joint infection (PJI), and a control group of 398,708 individuals. Genetic loci associated with postoperative complications were identified by a GWAS analysis, and the causal relationships of 11 modifiable risk factors with complications were assessed using MR. Results: The GWAS analysis identified nine loci associated with post-TJA complications. Two loci near the PPP1R3B and RBM26 genes were significantly linked to mechanical complications and PJI, respectively. The MR analysis demonstrated that body mass index was positively associated with the risk of mechanical complications (odds ratio [OR]: 1.42; p < 0.001). Higher educational attainment was associated with a decreased risk of mechanical complications (OR: 0.55; p < 0.001) and PJI (OR: 0.43; p = 0.001). Type 2 diabetes was suggestively associated with mechanical complications (OR, 1.18, p = 0.02), and hypertension was suggestively associated with PJI (OR, 1.41, p = 0.008). Other lifestyle factors, including smoking and alcohol consumption, were not causally related to postoperative complications. Conclusions: The genetic loci near PPP1R3B and RBM26 influenced the risk of post-TJA mechanical complications and infections, respectively. The effects of genetic and modifiable risk factors, including body mass index and educational attainment, underscore the need to perform personalized preoperative assessments and the postoperative management of surgical patients. These results indicate that integrating genetic screening and lifestyle interventions into patient care can improve the outcomes of TJA and patient quality of life.
Natural Occurring Terpene Cyclic Anhydrides: Biosynthetic Origin and Biological Activities
Authors: Molina Inzunza Diego O. , Martín González Juan E. , Segura Navarro María José , Barrero Alejandro F. , Quílez del Moral José F.
Published: August 2024

Cyclic acid anhydride is a not very widespread structure in nature, but with a determining role in natural products possessing this functionality in their skeleton. To the best of our knowledge, no revision of terpenes containing cyclic anhydrides has been previously reported. The result was that more than 100 terpenic cyclic anhydrides and related compounds were found to be in need of being reported. This review has been systematically organized by terpene skeletons, from the smallest to largest, describing their sources and bioactivities. In addition, different biosynthetic pathways for their final oxidations, namely, routes A, B and C, leading to the formation of these heterocyclic natural products, have been proposed. We have also included the most plausible precursors of these natural products, which mostly happened to be present in the same natural source. Some molecules derived from terpene cyclic anhydrides, such as their natural imide derivatives, have also been described due to their significant biological activity. In this sense, special attention has been paid to cantharidin because of its historical relevance and its broad bioactivity. A plausible biosynthesis of cantharidin has been proposed for the first time. Finally, cyclic anhydride structures that were firstly assigned as anhydrides and later corrected have been also described.
Expression of Foxtail Millet bZIP Transcription Factor SibZIP67 Enhances Drought Tolerance in Arabidopsis
Authors: Jia Xinfeng , Gao Hanchi , Zhang Lingxin , Tang Wei , Wei Guo , Sun Juan , Xiong Wangdan
Published: August 2024

Foxtail millet is a drought-tolerant cereal and forage crop. The basic leucine zipper (bZIP) gene family plays important roles in regulating plant development and responding to stresses. However, the roles of bZIP genes in foxtail millet remain largely uninvestigated. In this study, 92 members of the bZIP transcription factors were identified in foxtail millet and clustered into ten clades. The expression levels of four SibZIP genes (SibZIP11, SibZIP12, SibZIP41, and SibZIP67) were significantly induced after PEG treatment, and SibZIP67 was chosen for further analysis. The studies showed that ectopic overexpression of SibZIP67 in Arabidopsis enhanced the plant drought tolerance. Detached leaves of SibZIP67 overexpressing plants had lower leaf water loss rates than those of wild-type plants. SibZIP67 overexpressing plants improved survival rates under drought conditions compared to wild-type plants. Additionally, overexpressing SibZIP67 in plants displayed reduced malondialdehyde (MDA) levels and enhanced activities of antioxidant enzymes, including catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD) under drought stress. Furthermore, the drought-related genes, such as AtRD29A, AtRD22, AtNCED3, AtABF3, AtABI1, and AtABI5, were found to be regulated in SibZIP67 transgenic plants than in wild-type Arabidopsis under drought conditions. These data suggested that SibZIP67 conferred drought tolerance in transgenic Arabidopsis by regulating antioxidant enzyme activities and the expression of stress-related genes. The study reveals that SibZIP67 plays a beneficial role in drought response in plants, offering a valuable genetic resource for agricultural improvement in arid environments.
Systematic Review of Left Ventricular Remodeling in Response to Hypoglycemic Medications: Assessing Changes in End-Systolic and End-Diastolic Diameters
Authors: Buz Bogdan-Flaviu , Negrean Rodica Anamaria , Caruntu Florina , Parvanescu Tudor , Slovenski Milena , Tomescu Mirela Cleopatra , Arnautu Diana-Aurora
Published: August 2024

Hypoglycemic medications are widely used in managing diabetes mellitus, with emerging evidence suggesting their role in cardiac reverse remodeling. This systematic review aims to quantitatively synthesize data regarding the impact of these medications on left ventricular end-diastolic diameter (LVEDD) and end-systolic diameter (LVESD), and to evaluate the clinical relevance of these changes in promoting favorable cardiac outcomes. We conducted a comprehensive search across PubMed, Scopus, and the Web of Science up to 22 April 2024, selecting studies based on inclusion criteria that focused on the impact of hypoglycemic medications on LVEDD and LVESD in patients with diabetes. Studies were selected through a rigorous process, adhering to PRISMA guidelines, and involving various designs including randomized controlled trials and observational studies. The main outcomes were changes in LVEDD and LVESD measured by validated cardiac imaging techniques. A total of ten studies met the inclusion criteria, involving a total of 1180 patients. Treatment durations ranged from 3 to 24 months. Significant improvements in cardiac dimensions were noted with some medications. For instance, Liraglutide treatment over three months significantly improved LVEF from 47.2% to 57.2% and reduced LVEDD and LVESD from 46.5 mm to 45.2 mm and 35.2 mm to 32.7 mm, respectively. In contrast, other medications like Sitagliptin showed minimal impact over 24 months. On average, hypoglycemic medications reduced LVEDD from 58.2 mm to 55.0 mm and LVESD from 48.3 mm to 44.3 mm, with a mean improvement in LVEF from 38.9% to 43.8%. Hypoglycemic medications contribute variably to cardiac reverse remodeling. Medications such as Liraglutide and Dapagliflozin demonstrate significant potential in improving cardiac dimensions and function, indicating their utility beyond glycemic control. This review highlights the need for tailored treatment approaches to maximize cardiac outcomes in patients with diabetes, suggesting a broader therapeutic role for these agents.
Radiotherapy and Systemic Treatment for Leptomeningeal Disease
Authors: Frechette Kelsey M. , Breen William G. , Brown Paul D. , Sener Ugur T. , Webb Lauren M. , Routman David M. , Laack Nadia N. , Mahajan Anita , Lehrer Eric J.
Published: August 2024

Leptomeningeal disease (LMD) is a devastating sequelae of metastatic spread that affects approximately 5% of cancer patients. The incidence of LMD is increasing due to advancements in systemic therapy and enhanced detection methods. The purpose of this review is to provide a detailed overview of the evidence in the detection, prognostication, and treatment of LMD. A comprehensive literature search of PUBMED was conducted to identify articles reporting on LMD including existing data and ongoing clinical trials. We found a wide array of treatment options available for LMD including chemotherapy, targeted agents, and immunotherapy as well as several choices for radiotherapy including whole brain radiotherapy (WBRT), stereotactic radiosurgery (SRS), and craniospinal irradiation (CSI). Despite treatment, the prognosis for patients with LMD is dismal, typically 2–4 months on average. Novel therapies and combination approaches are actively under investigation with the aim of improving outcomes and quality of life for patients with LMD. Recent prospective data on the use of proton CSI for patients with LMD have demonstrated its potential survival benefit with follow-up investigations underway. There is a need for validated metrics to predict prognosis and improve patient selection for patients with LMD in order to optimize treatment approaches.
Transcriptomic Signatures in Lung Allografts and Their Therapeutic Implications
Authors: Guinn Michael Tyler , Fernandez Ramiro , Lau Sean , Loor Gabriel
Published: August 2024

Ex vivo lung perfusion (EVLP) is a well-established method of lung preservation in clinical transplantation. Transcriptomic analyses of cells and tissues uncover gene expression patterns which reveal granular molecular pathways and cellular programs under various conditions. Coupling EVLP and transcriptomics may provide insights into lung allograft physiology at a molecular level with the potential to develop targeted therapies to enhance or repair the donor lung. This review examines the current landscape of transcriptional analysis of lung allografts in the context of state-of-the-art therapeutics that have been developed to optimize lung allograft function.
Multi-Task Intelligent Monitoring of Construction Safety Based on Computer Vision
Authors: Liu Lingfeng , Guo Zhigang , Liu Zhengxiong , Zhang Yaolin , Cai Ruying , Hu Xin , Yang Ran , Wang Gang
Published: August 2024

Effective safety management is vital for ensuring construction safety. Traditional safety inspections in construction heavily rely on manual labor, which is both time-consuming and labor-intensive. Extensive research has been conducted integrating computer-vision technologies to facilitate intelligent surveillance and improve safety measures. However, existing research predominantly focuses on singular tasks, while construction environments necessitate comprehensive analysis. This study introduces a multi-task computer vision technology approach for the enhanced monitoring of construction safety. The process begins with the collection and processing of multi-source video surveillance data. Subsequently, YOLOv8, a deep learning-based computer vision model, is adapted to meet specific task requirements by modifying the head component of the framework. This adaptation enables efficient detection and segmentation of construction elements, as well as the estimation of person and machine poses. Moreover, a tracking algorithm integrates these capabilities to continuously monitor detected elements, thereby facilitating the proactive identification of unsafe practices on construction sites. This paper also presents a novel Integrated Excavator Pose (IEP) dataset designed to address the common challenges associated with different single datasets, thereby ensuring accurate detection and robust application in practical scenarios.
Two Birds with One Stone: High-Quality Utilization of COVID-19 Waste Masks into Bio-Oil, Pyrolytic Gas, and Eco-Friendly Biochar with Adsorption Applications
Authors: Wang Tongtong , Zhang Di , Shi Hui , Wang Sen , Wu Bo , Jia Junchao , Feng Zhizhen , Zhao Wenjuan , Chang Zhangyue , Husein Dalal Z.
Published: August 2024

As a common necessity, masks have been used a lot in recent years, and the comprehensive utilization of waste masks has become a research priority in the post-COVID-19 pandemic era. However, traditional disposal methods suffer from a range of problems, including poor utilization and insecurity. To explore new solution ideas and efficiently utilize waste resources, waste masks and biomass wastes were used as raw materials to prepare mask-based biochar (WMB), bio-oil, and pyrolytic gas via oxygen-limited co-pyrolysis in this study. The obtained solid–liquid–gas product was systematically characterized to analyze the physicochemical properties, and the adsorption properties and mechanisms of WMB on the environmental endocrine bisphenol A (BPA) were investigated. The co-pyrolysis mechanisms were also studied in depth. Furthermore, the strengths and weaknesses of products prepared by co-pyrolysis and co-hydrothermal synthesis were discussed in comparison. The results indicated that the waste masks could shape the microsphere structure, leading to richer surface functional groups and stable mesoporous of WMB. Here, the risk of leaching of secondary pollutants was not detected. The theoretical maximum adsorption of BPA by WMB was 28.73 mg·g−1. The Langmuir and Pseudo-second-order models optimally simulated the isothermal and kinetic adsorption processes, which are a composite of physicochemical adsorption. Simultaneous pyrolysis of mask polymers with biomass polymers produces bio-oil and pyrolytic gas, which is rich in high-quality aliphatic and aromatic compounds. This could have potential as an energy source or chemical feedstock. The co-pyrolysis mechanisms may involve the depolymerization of waste masks to produce hydrocarbons and H radicals, which in turn undergo multi-step cleavage and oligomerization reactions with biomass derivatives. It is recommended to use the co-pyrolysis method to dispose of waste masks, as the products obtained are significantly better than those obtained by the co-hydrothermal method. This work provides a new contribution to the resourcing of waste masks into high-quality products.
A Diagnostic Case Study for Manufacturing Gas-Phase Chemical Sensors
Authors: Contreras Raquel Pimentel , Koch Dylan T. , Gibson Patrick , McCartney Mitchell M. , Chew Bradley S. , Chakraborty Pranay , Chevy Daniel A. , Honeycutt Reid , Haun Joseph , Griffin Thomas , Hicks Tristan L. , Davis Cristina E.
Published: August 2024

In this work, we describe the design, manufacturing development, and refinement of a chemical detection platform designed to identify specific odorants in the natural gas industry. As the demand for reliable and sensitive volatile organic compound (VOC) detection systems is growing, our project aimed to construct multiple prototypes to enhance our detection capabilities and provide portable detection platforms. Throughout the development process across nominally identical and duplicated instruments, various failure modes were encountered, which provided insight into the design and manufacturing challenges present when designing such platforms. We conducted a post hoc root cause analysis for each failure mode, leading to a series of design modifications and solutions. This paper details these design and manufacturing challenges, the analytical methods used to diagnose and address them, and the resulting improvements in system performance. In the end, a debugging flow chart is presented to aid future researchers in solving the possible issues that could be encountered. Our findings show the complexities of bespoke chemical sensor design for unique applications and highlight the critical importance of iterative testing and problem-solving in the development of industrial detection technologies. Achieving consistency across devices is essential for optimizing device-to-device efficiency. The work presented is the first step towards ensuring uniform performance across a production run of chemically sensitive devices. In the future, a universal device calibration model will be implemented, eliminating the need to collect data from each individual device.
Overview of the Treatment Goal, the Method of Evaluating Disease Activity/Physical Function, Activities of Daily Living, and Traditional Care for Systemic or Articular Juvenile Idiopathic Arthritis in Japan
Authors: Mori Masaaki
Published: August 2024

Juvenile idiopathic arthritis (JIA) is a chronic arthritis of unknown cause that develops in patients younger than 16 years of age and persists for at least 6 weeks. It is an important cause of short- and long-term physical and mental impairments in children. The goal of treatment for JIA is remission. A T2T (treatment-to-target) has been proposed and practiced as a means of achieving remission. The method of evaluating the disease activity of JIA depends on the disease type. For systemic JIA, disease activity is determined by comprehensively considering joint findings, systemic inflammatory findings, changes in inflammatory and synovitis markers, imaging findings, and other factors. For articular JIA other than systemic JIA, the Juvenile Arthritis Disease Activity Score (JADAS-27) is used to evaluate disease activity. The CHAQ (Childhood Health Assessment Questionnaire) and the Japanese version of the modified Rankin Scale (mRS) are mainly used to assess the physical function and ADL. The CHAQ is a global standard assessment method with the advantage that it can be transitioned to the HAQ used in adults, making it useful for international comparisons. The mRS is used to classify the severity of JIA as a chronic disease, and is an indispensable evaluation method in the specific disease procedure in Japan. It is necessary to have pediatric-specific knowledge of growth and development and routine childhood immunizations and to consider transition support tailored to the patient’s situation. Ultimately, the goal is to foster the patient’s independence and to provide an uninterrupted follow-up in the adult care department. Continuous follow-up will be provided during the schooling (and later, employment) period, and the relationship with the patient will be tailored to their developmental stage. It is also important to understand and communicate the importance of contraception and the drugs that cannot be used during pregnancy.
High Prevalence of Varus Knee Malalignment in Adolescent Football Players—Clinical Lower Leg Axis Measurements of Male Junior Football Players Aged 7 to 18 Years
Authors: Memmel Clemens , Sporrer Dominik , Szymski Dominik , Weber Johannes , Hanke Alexander , Denzinger Markus , Kerschbaum Maximilian , Alt Volker , Krutsch Werner , Koch Matthias
Published: August 2024

Background: Football, as the most popular sport worldwide, has long been under suspicion of causing varus knee alignment as early as adolescence. However, no causal relationship has yet been found. The first step to do so would be to determine the prevalence of lower leg malalignment among male junior football players depending on age, performance level and the number of active seasons played. Methods: Leg axis alignment in frontal plane was determined in male junior football players of different age levels between 7 and 18 years by measuring the intercondylar/intermalleolar distance (ICD/IMD) in an upright position. In addition to anthropometric data, multiple sport-specific data such as the start of their football career or training time per week were collected by means of questionnaires (clinical trial registration number: DRKS00020446). Results: 207 male junior football players were included in this survey. The mean age was 12.8 years. Within the group of 15 to 18 year olds, the prevalence of varus knee malalignment was highest at just under a third (32.1%). In the subpopulation that played actively for more than ten seasons, 28.9% showed varus leg axis. Regarding performance level, the highest IMD/ICD values and highest prevalence of varus alignment were found among football players who play on a semi-professional level (16.7%), compared to amateur (11.4%) and high-performance levels (2.8%). Conclusions: Further research is necessary to investigate if this high prevalence of varus knee in children of a higher age and higher playing levels is due to the selection bias of football players with varus knee or a real development of varus knee in individuals.
Dataset of Registered Hematoxylin–Eosin and Ki67 Histopathological Image Pairs Complemented by a Registration Algorithm
Authors: Petríková Dominika , Cimrák Ivan , Tobiášová Katarína , Plank Lukáš
Published: August 2024

In this work, we describe a dataset suitable for analyzing the extent to which hematoxylin–eosin (HE)-stained tissue contains information about the expression of Ki67 in immunohistochemistry staining. The dataset provides images of corresponding pairs of HE and Ki67 stainings and is complemented by algorithms for computing the Ki67 index. We introduce a dataset of high-resolution histological images of testicular seminoma tissue. The dataset comprises digitized histology slides from 77 conventional testicular seminoma patients, obtained via surgical resection. For each patient, two physically adjacent tissue sections are stained: one with hematoxylin and eosin, and one with Ki67 immunohistochemistry staining. This results in a total of 154 high-resolution images. The images are provided in PNG format, facilitating ease of use for image analysis compared to the original scanner output formats. Each image contains enough tissue to generate thousands of non-overlapping 224 × 224 pixel patches. This shows the potential to generate more than 50,000 pairs of patches, one with HE staining and a corresponding Ki67 patch that depicts a very similar part of the tissue. Finally, we present the results of applying a ResNet neural network for the classification of HE patches into categories according to their Ki67 label.
The Prescription and Safety of Oral Antidiabetic Drugs in Outpatients with Type 2 Diabetes Mellitus: An Observational, Retrospective, Multicenter Study on the Role of Adherence in a Real-Life Primary Care Setting
Authors: Marcianò Gianmarco , Vocca Cristina , Casarella Alessandro , Gallelli Luca , Rania Vincenzo , De Sarro Caterina , Citraro Rita , Palleria Caterina , Bianco Rosa Candida , Fera Iolanda , Greco Antonietta , Muraca Lucia , Nanci Giacinto , Rossi Carmelo Luciano , Ashour Michael , D’Agostino Bruno , De Sarro Giovambattista
Published: August 2024

Introduction: Type 2 diabetes mellitus (T2DM) is a common disease burdened with significant morbidity and mortality. Despite the substantial number of new available drug treatments, adherence to therapy and adverse drug reactions (ADRs) are the major constraint in the management of this disease. We evaluated the use, the adherence, and the safety of antidiabetic drugs in patients with T2DM. Methods: We performed an observational, retrospective, multicenter study on medical records of outpatients referred to general practitioners in Catanzaro (Calabria, Italy). Drug adherence was measured considering the packages of antidiabetic drugs prescribed at the time of admission, after three months, and 1 year later. ADRs were evaluated using the Naranjo probability scale. Collected data were analyzed using the Statistical Package for the Social Sciences. Results: During the study, we evaluated 12,170 medical records of seven general practitioners. The most prescribed drug was metformin alone (28.4%) or with other oral antidiabetics (19.6%) and then insulin (n: 354; men 190, women 164). Logistic regression showed an association between T2DM less than or equal to 5 years and low adherence (p = 0.023). During the study, we recorded 26 ADRs that were correlated with sex (women) and insulin treatment. Conclusions: this real-life study shows that patients with T2DM have a high adherence, probably related to their having a low number of ADRs.
A Heuristic Routing Algorithm for Heterogeneous UAVs in Time-Constrained MEC Systems
Authors: Chen Long , Liu Guangrui , Zhu Xia , Li Xin
Published: August 2024

The rapid proliferation of Internet of Things (IoT) ground devices (GDs) has created an unprecedented demand for computing resources and real-time data-processing capabilities. Integrating unmanned aerial vehicles (UAVs) into Mobile Edge Computing (MEC) emerges as a promising solution to bring computation and storage closer to the data sources. However, UAV heterogeneity and the time window constraints for task execution pose a significant challenge. This paper addresses the multiple heterogeneity UAV routing problem in MEC environments, modeling it as a multi-traveling salesman problem (MTSP) with soft time constraints. We propose a two-stage heuristic algorithm, heterogeneous multiple UAV routing (HMUR). The approach first identifies task areas (TAs) and optimal hovering positions for the UAVs and defines an effective fitness measurement to handle UAV heterogeneity. A novel scoring function further refines the path determination, prioritizing real-time task compliance to enhance Quality of Service (QoS). The simulation results demonstrate that our proposed HMUR method surpasses the existing baseline algorithms on multiple metrics, validating its effectiveness in optimizing resource scheduling in MEC environments.
Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
Authors: Gu Xinglong , Chen Guifen , Wu Guowei , Wen Chenghua
Published: August 2024

To address the issues of communication failure and inefficiency in clustered drone relay communication due to external malicious interference, this paper proposes a joint optimization method for relay communication rates under interference conditions for clustered drones. This method employs the following two-step processing framework: Firstly, the Discrete Soft Actor-Critic (DSAC) algorithm is used to train the relay drones for dynamic channel selection, effectively avoiding various types of interference. Simultaneously, the Bayesian optimization algorithm is applied to optimize the hyperparameters of the DSAC algorithm, further enhancing its performance. Subsequently, the modulation order, transmission power, trajectory of the relay drones, and power allocation factors of the clustered drones are jointly optimized. This complex problem is transformed into a convex subproblem for determining a solution, aiming to maximize the communication rate of the clustered drones. The simulation’s results demonstrate that the proposed algorithm exhibits excellent performances in terms of anti-interference capability, solution convergence, and stability. It effectively improves the mission efficiency of clustered drones under interference conditions and enhances their adaptability to dynamic environments.
The Impact of Tax Pressure on Long-Term Economic Growth in Morocco
Authors: Benkejjane Noureddine , Mhatchan Safaa , Oudgou Mohamed , Boudhar Abdeslam
Published: August 2024

Despite the tax reform in the 1980s, the Moroccan tax system still suffers from structural vulnerabilities that have led to inefficient tax policies and increasing sectoral disparities, which have led to an increase in the tax pressure rates between 1990 and 2020. To overcome these vulnerabilities, and according to the recommendations of the 2019 National Tax Conference, the Moroccan tax system has been recently restructured, with the aim of strengthening the principles of equity and tax efficiency. The main objective of this paper is to determine the impact of the tax pressure on long-term economic growth in Morocco from 1990 to 2020. To do so, the methodology adopted in this work consists of verifying the effect of the tax pressure on economic growth through a quantitative methodology based on two vector autoregression approaches: the vector autoregression model (VAR) and the vector error correction model (VECM). The results of this study confirm that the effect of fiscal pressure on economic growth in Morocco is not significant in the long run, and therefore, we can deduce that taxation is not yet a well-mastered instrument for the state to act on the economic sphere. The results show a complex relationship between tax pressures and economic growth in Morocco, underlining the importance of tax reform. However, the research is limited by the specific models and the need to explore other determinants. The contribution of this paper lies in its in-depth empirical analysis of fiscal pressure and its influence on long-term economic growth in Morocco, as based on VAR and VECM modeling. This econometric approach makes it possible to isolate the dynamic effects and responses of economic variables over time, offering an understanding of the interactions between tax policy and long-term economic growth. By focusing specifically on the Moroccan context, this study offers an original perspective and helps to fill the gap in the empirical research in this area while providing a valuable analytical framework for assessing fiscal policies and their impact on long-term economic growth.
ALKU-Net: Adaptive Large Kernel Attention Convolution Network for Lung Nodule Segmentation
Authors: Chen Juepu , Liu Shuxian , Liu Yulong
Published: August 2024

The accurate segmentation of lung nodules in computed tomography (CT) images is crucial for the early screening and diagnosis of lung cancer. However, the heterogeneity of lung nodules and their similarity to other lung tissue features make this task more challenging. By using large receptive fields from large convolutional kernels, convolutional neural networks (CNNs) can achieve higher segmentation accuracies with fewer parameters. However, due to the fixed size of the convolutional kernel, CNNs still struggle to extract multi-scale features for lung nodules of varying sizes. In this study, we propose a novel network to improve the segmentation accuracy of lung nodules. The network integrates adaptive large kernel attention (ALK) blocks, employing multiple convolutional layers with variously sized convolutional kernels and expansion rates to extract multi-scale features. A dynamic selection mechanism is also introduced to aggregate the multi-scale features obtained from variously sized convolutional kernels based on selection weights. Based on this, we propose a lightweight convolutional neural network with large convolutional kernels, called ALKU-Net, which integrates the ALKA module in a hierarchical encoder and adopts a U-shaped decoder to form a novel architecture. ALKU-Net efficiently utilizes the multi-scale large receptive field and enhances the model perception capability through spatial attention and channel attention. Extensive experiments demonstrate that our method outperforms other state-of-the-art models on the public dataset LUNA-16, exhibiting considerable accuracy in the lung nodule segmentation task.
Innovative Programming Approaches to Address Z-Interference in High-Density 3D NAND Flash Memory
Authors: Choi Yu Jin , Hong Seul Ki , Park Jong Kyung
Published: August 2024

Increasing the bit density in 3D NAND flash memory involves reducing the pitch of ON (Oxide-Nitride) molds in the Z-direction. However, this reduction drastically increases Z-interference, adversely affecting cell distribution and accelerating degradation of reliability limits. Previous studies have shown that programming from the top word line (WL) to the bottom WL, instead of the traditional bottom-to-top approach, alleviates Z-interference. Nevertheless, detailed analysis of how Z-interference varies at each WL depending on the programming sequence remains insufficient. This paper investigates the causes of Z-interference variations at Top, Middle, and Bottom WLs through TCAD analysis. It was found that as more electrons are programmed into WLs within the string, Z-interference variations increase due to increased resistance in the poly-Si channel. These variations are exacerbated by tapered vertical channel profiles resulting from high aspect ratio etching. To address these issues, a method is proposed to adjust bitline biases during verification operations of each WL. This method has been validated to enhance the performance and reliability of 3D NAND flash memory.
Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization
Authors: Gao Weijie , Xie Shijun , Wang Heng , Zhang Yufeng , Ling Yao
Published: August 2024

Recently, researchers have achieved the goal of using unified architecture to achieve multiple modulation modes under specific conditions. However, existing research still suffers from the problem of large resource and time overhead. This paper proposes an adaptive modem based on a vector quantization (VQ) long short-term memory autoencoder (LSTM-AE), designed to implement modulation and demodulation of signals from the second to thirty-second order in a lightweight way. By leveraging the memory capacity of the LSTM module and the compression capability of the autoencoder, the model is able to support multi-order modulation methods. This study used the Adam optimizer for training and testing on a simple dataset extended by adding AWGN noise only and made modifications based on the MSE loss function to balance the accuracy and training speed of each part of the model. Experiments demonstrate that the proposed method not only achieves comparable modem performance to existing frameworks for second to thirty-second order signals, but also significantly reduces the number of parameters and training time. Experimental results indicate that the proposed methodology not only matches the performance of existing frameworks for signals ranging from the second to the thirty-second order, but also employs merely 79.6% of the average parameter count and a mere 7.4% of the average training duration. This represents a substantial reduction in resource expenditure.
Enhancing Image Copy Detection through Dynamic Augmentation and Efficient Sampling with Minimal Data
Authors: Fawzy Mohamed , Tawfik Noha S. , Saleh Sherine Nagy
Published: August 2024

Social networks have become deeply integrated into our daily lives, leading to an increase in image sharing across different platforms. Simultaneously, the existence of robust and user-friendly media editors not only facilitates artistic innovation, but also raises concerns regarding the ease of creating misleading media. This highlights the need for developing new advanced techniques for the image copy detection task, which involves evaluating whether photos or videos originate from the same source. This research introduces a novel application of the Vision Transformer (ViT) model to the image copy detection task on the DISC21 dataset. Our approach involves innovative strategic sampling of the extensive DISC21 training set using K-means clustering to achieve a representative subset. Additionally, we employ complex augmentation pipelines applied while training with varying intensities. Our methodology follows the instance discrimination concept, where the Vision Transformer model is used as a classifier to map different augmentations of the same image to the same class. Next, the trained ViT model extracts descriptors of original and manipulated images that subsequently underwent post-processing to reduce dimensionality. Our best-achieving model, tested on a refined query set of 10K augmented images from the DISC21 dataset, attained a state-of-the-art micro-average precision of 0.79, demonstrating the effectiveness and innovation of our approach.
Environmental Impact Assessment of Volatile Organic Compound Emissions during Trenchless Cured-in-Place Pipe Installation
Authors: Bavilinezhad Salar , Najafi Mohammad , Kaushal Vinayak , Elledge William , Kaynak Burak
Published: August 2024

Cured-in-place pipe (CIPP) lining is a widely adopted method for pipeline renewal, offering advantages such as rapid installation and cost-effectiveness. However, concerns regarding the environmental impacts of volatile organic compound (VOC) emissions during the installation process have raised issues regarding the CIPP method. The literature review conducted in this paper indicated the need for stringent monitoring and management practices to mitigate adverse effects, emphasizing the importance of safe installation protocols. This paper presents the initial results of a case study focusing on VOC emissions, specifically investigating non-styrene vinyl ester resins and water curing. To capture emissions from CIPP activities, the methodology involved air sampling using various equipment, including photoionization detectors (PID), summa canisters, passive worker sampler sorbent tubes, and method 13 cartridges. The preliminary findings indicate that non-styrene vinyl ester resins resulted in VOC emissions well below the exposure limits set by OSHA and USEPA, with the highest measured concentration being 2.54 ppm. This demonstrates that non-styrene resins can significantly reduce environmental and health risks. The future phases of this project will explore different resins and curing methods to further validate these findings and develop comprehensive guidelines for safe CIPP installations.
Determination of Variable Humidity Profile for Lactic Acid Maximization in Fungal Solid-State Fermentation
Authors: Groff María Carla , Noriega Sandra Edith , Díaz Meglioli María Eugenia , Rodríguez Laura , Kuchen Benjamín , Scaglia Gustavo
Published: August 2024

Solid-state fermentation (SSF) is the bioprocess where microorganisms are cultivated in the absence of free water under controlled conditions. Lactic acid can be produced by Rhizopus oryzae SSF of grape stalks. During the microorganism’s growth, the temperature and water content of the solid bed fluctuate, leading to areas of either dry or excessive moisture in the solid substrate. Therefore, it is crucial to control the water supply to the matrix. In this work, we obtain lactic acid through SSF of grape stalks using Rhizopus oryzae NCIM 1299. The SSF was conducted at a fixed temperature of 35 °C, with five constant relative humidity (RH) levels: 50, 57, 65, 72, and 80%RH. Mathematical models, including the Logistic and First-Order Plus Dead-Time models for fungal biomass growth and the Luedeking and Piret with Delay Time model for lactic acid production, were adjusted to kinetic curves. Growth kinetic parameters (Xmax, μmax, Tp, T0, Yp/x, and td) were determined for all conditions. These kinetic parameters were then correlated with relative humidity using a second-degree polynomial relationship. We observed a decrease in Xmax with an increasing %RH, while the value of Yp/x increased at a higher %RH. Finally, the optimal variable relative humidity profile was obtained by applying the dynamic optimization technique, resulting in a 16.63% increase in lactic acid production.
Transferability of Empirical Models Derived from Satellite Imagery for Live Fuel Moisture Content Estimation and Fire Risk Prediction
Authors: Marino Eva , Yáñez Lucía , Guijarro Mercedes , Madrigal Javier , Senra Francisco , Rodríguez Sergio , Tomé José Luis
Published: August 2024

Estimating live fuel moisture content (LFMC) is critical for assessing vegetation flammability and predicting potential fire behaviour, thus providing relevant information for wildfire prevention and management. Previous research has demonstrated that empirical modelling based on spectral data derived from remote sensing is useful for retrieving LFMC. However, these types of models are often very site-specific and generally considered difficult to extrapolate. In the present study, we analysed the performance of empirical models based on Sentinel-2 spectral data for estimating LFMC in fire-prone shrubland dominated by Cistus ladanifer. We used LFMC data collected in the field between June 2021 and September 2022 in 27 plots in the region of Andalusia (southern Spain). The specific objectives of the study included (i) to test previous existing models fitted for the same shrubland species in a different study area in the region of Madrid (central Spain); (ii) to calibrate empirical models with the field data from the region of Andalusia, comparing the model performance with that of existing models; and (iii) to test the capacity of the best empirical models to predict decreases in LFMC to critical threshold values in historical wildfire events. The results showed that the empirical models derived from Sentinel-2 data provided accurate LFMC monitoring, with a mean absolute error (MAE) of 15% in the estimation of LFMC variability throughout the year and with the MAE decreasing to 10% for the critical lower LFMC values (<100%). They also showed that previous models could be easily recalibrated for extrapolation to different geographical areas, yielding similar errors to the specific empirical models fitted in the study area in an independent validation. Finally, the results showed that decreases in LFMC in historical wildfire events were accurately predicted by the empirical models, with LFMC <80% in this fire-prone shrubland species.