bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Non-Invasive Murine Model of Neonatal Hypoxic-Ischemic Encephalopathy Demonstrates Developmental Delay and Motor Deficits with Activation of Inflammatory Pathways in Monocytes
Authors: Lemanski Elise A. , Collins Bailey A. , Ebenezer Andrew T. , Anilkumar Sudha , Langdon Victoria A. , Zheng Qi , Ding Shanshan , Franke Karl Royden , Schwarz Jaclyn M. , Wright-Jin Elizabeth C.
Published: September 2024

Neonatal hypoxic-ischemic encephalopathy (HIE) occurs in 1.5 per 1000 live births, leaving affected children with long-term motor and cognitive deficits. Few animal models of HIE incorporate maternal immune activation (MIA) despite the significant risk MIA poses to HIE incidence and diagnosis. Our non-invasive model of HIE pairs late gestation MIA with postnatal hypoxia. HIE pups exhibited a trend toward smaller overall brain size and delays in the ontogeny of several developmental milestones. In adulthood, HIE animals had reduced strength and gait deficits, but no difference in speed. Surprisingly, HIE animals performed better on the rotarod, an assessment of motor coordination. There was significant upregulation of inflammatory genes in microglia 24 h after hypoxia. Single-cell RNA sequencing (scRNAseq) revealed two microglia subclusters of interest following HIE. Pseudobulk analysis revealed increased microglia motility gene expression and upregulation of epigenetic machinery and neurodevelopmental genes in macrophages following HIE. No sex differences were found in any measures. These results support a two-hit noninvasive model pairing MIA and hypoxia as a model for HIE in humans. This model results in a milder phenotype compared to established HIE models; however, HIE is a clinically heterogeneous injury resulting in a variety of outcomes in humans. The pathways identified in our model of HIE may reveal novel targets for therapy for neonates with HIE.
The Role of Mesenchymal Stem Cells in Modulating Adaptive Immune Responses in Multiple Sclerosis
Authors: Dadfar Sepehr , Yazdanpanah Esmaeil , Pazoki Alireza , Nemati Mohammad Hossein , Eslami Majid , Haghmorad Dariush , Oksenych Valentyn
Published: September 2024

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system, leading to significant disability through neurodegeneration. Despite advances in the understanding of MS pathophysiology, effective treatments remain limited. Mesenchymal stem cells (MSCs) have gained attention as a potential therapeutic option due to their immunomodulatory and regenerative properties. This review examines MS pathogenesis, emphasizing the role of immune cells, particularly T cells, in disease progression, and explores MSCs’ therapeutic potential. Although preclinical studies in animal models show MSC efficacy, challenges such as donor variability, culture conditions, migratory capacity, and immunological compatibility hinder widespread clinical adoption. Strategies like genetic modification, optimized delivery methods, and advanced manufacturing are critical to overcoming these obstacles. Further research is needed to validate MSCs’ clinical application in MS therapy.
Research Progress of Taste Biosensors in Simulating Taste Transduction Mechanism
Authors: Liu Jingjing , Kuang Jiale , Zhang Yan , Chen Yizhou , Liu Shikun , Li Yanfeng , Qiao Lixin , Wei Zhenbo , Jiang Shui , Meng Jie
Published: September 2024

The simulation of human sensory functions is a key trend in the field of sensor development. In taste sensing, taste biosensors emulate taste perception using biorecognition elements that participate in taste transduction, such as taste receptors, cells, tissues, etc. This approach obtains high selectivity and a wide detection range of human taste perception, making taste biosensors widely used in food analysis and taste perception studies. By combining biorecognition elements with suitable data processing and analysis techniques, the taste information generated during the process of taste transduction, obtained by the sensing elements of the sensor, can be accurately captured. In this paper, we explore current available solutions to stability and sensitivity, and other challenges in taste biosensors using taste receptors, cells, and tissues as sensing elements. We also outline the applied signal processing techniques based on the signal characteristics from different types of taste biosensors. Finally, it is proposed that the development of taste biosensing sensors will further promote the application of intelligent sensory evaluation and human perception analysis systems in food, medicine, and other fields.
Effect of Muscle Strength on Functionality after Shoulder Tendon Transfer in Brachial Plexus Birth Injury: Is There a Relationship between Them?
Authors: Hoşbay Zeynep , Utku Umut Gülsena , Tanrıverdi Müberra , Altaş Okyar , Aydın Atakan
Published: September 2024

Background/Objectives: Secondary problems in BPBI occur due to decreased muscle strength in the upper extremities. Comprehensive assessment methods are necessary to understand structural problems and to plan appropriate interventions in children with BPBI. We investigated the relationship between distal muscle strength, range of motion (ROM), and functionality by comparing distal muscle strength on the affected and unaffected sides in patients with BPBI who underwent shoulder tendon transfer. Methods: A total of 25 children with BPBI, 13 (52%) girls and 12 (48%) boys, aged 4–7 years (mean age: 5.98 ± 1.27 years), who had undergone shoulder tendon transfer surgery at least one year prior to the study were included. The muscle strength of the elbow, forearm, and wrist were assessed using the MicroFET®2 Digital Hand Dynamometer. The ROM of the elbow, forearm, and wrist were measured using the universal goniometer. The Pediatric Evaluation of Disability Inventory (PEDI) was used for functionality assessment. Results: The strength of the elbow flexor–extensor, forearm pronator–supinator, and wrist extensor muscles on the affected side was greater than on the unaffected side in all children (p < 0.001). No correlation was found between muscle strength, ROM, and functionality in the affected extremity (p > 0.005). Conclusions: Although children with BPBI have good shoulder function after shoulder tendon transfer, structural problems in the distal joints may affect their functionality during daily life. Distal joint strengthening and ROM exercises, as well as bimanual functional activities, should be included in the rehabilitation programs of children with BPBI after shoulder tendon transfer.
Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference
Authors: Zhen Hao , Shi Yucheng , Huang Yongcan , Yang Jidong J. , Liu Ninghao
Published: September 2024

Harnessing the power of Large Language Models (LLMs), this study explores the use of three state-of-the-art LLMs, specifically GPT-3.5-turbo, LLaMA3-8B, and LLaMA3-70B, for crash severity analysis and inference, framing it as a classification task. We generate textual narratives from original traffic crash tabular data using a pre-built template infused with domain knowledge. Additionally, we incorporated Chain-of-Thought (CoT) reasoning to guide the LLMs in analyzing the crash causes and then inferring the severity. This study also examine the impact of prompt engineering specifically designed for crash severity inference. The LLMs were tasked with crash severity inference to: (1) evaluate the models’ capabilities in crash severity analysis, (2) assess the effectiveness of CoT and domain-informed prompt engineering, and (3) examine the reasoning abilities with the CoT framework. Our results showed that LLaMA3-70B consistently outperformed the other models, particularly in zero-shot settings. The CoT and Prompt Engineering techniques significantly enhanced performance, improving logical reasoning and addressing alignment issues. Notably, the CoT offers valuable insights into LLMs’ reasoning process, unleashing their capacity to consider diverse factors such as environmental conditions, driver behavior, and vehicle characteristics in severity analysis and inference.
Why Permanent Makeup (PMU) Is Not a Lifetime Application
Authors: Andreou Eleni , Hatziantoniou Sophia , Rallis Efstathios , Kefala Vasiliki
Published: September 2024

Permanent makeup (PMU) is a popular form of tattooing applied to the eyebrows, eyelids, and lips for corrective and aesthetic purposes. It also has medical applications, such as reconstructing the breast areola, mimicking hair follicles on the head, and covering scars and vitiligo. Unlike body tattoos, PMU often requires reapplication to maintain color density and shape, as the pigments fade over time. This fading is likely due to the characteristics of PMU colorants rather than the application methods or apparatuses. The aim of our study was to assess the application depth of PMU colorants and tattoo ink after procedures. PMU colorants typically contain larger pigment particles in their composition compared to the nanoparticles found in traditional tattoo inks. We applied both tattoo ink and PMU colorants on SKH-1 mice using a PMU apparatus and a tattoo apparatus. To clarify the semi-permanent nature of PMU compared to the more permanent body tattoos, skin biopsies were performed at various intervals throughout this study. The results showed that PMU and tattoo ink were placed at approximately the same depth but exhibited key differences in behavior. PMU with larger inorganic pigments fades over time, while tattoos with smaller, stable organic pigments ensure permanence.
The Role of the Basophil Activation Test in the Diagnosis of Drug-Induced Anaphylaxis
Authors: Czarnobilska Maria , Bulanda Małgorzata , Czarnobilska Ewa , Dyga Wojciech , Mazur Marcel
Published: September 2024

The diagnosis of drug-induced anaphylaxis (DIA) is a serious health problem. The Basophil activation test (BAT) is considered a specific in vitro provocation, and compared to in vivo provocation, it is more convenient, cheaper, and safer for the patient. This study aimed to evaluate the usefulness of the BAT in the diagnosis of DIA. This study included 150 patients referred to a reference allergy clinic with suspected drug allergies. All patients underwent a detailed clinical evaluation supplemented with the BAT. Positive BAT results were obtained in two out of 21 patients who were to receive the COVID-19 vaccine. The sensitivity and specificity of the BAT were 40% and 75% for the COVID-19 vaccine, 67% and 58% for DMG PEG 2000, and 100% and 75% for PEG 4000, respectively. Nine out of 34 patients with suspected antibiotic allergies had positive BAT results with 14 different antibiotics. Positive BAT results were also obtained with NSAIDs in two patients and with local anesthetics in three patients. The confirmation of allergy by the BAT improves the safety profile of the diagnostic work-up as it may defer the need for drug provocation, preventing potential anaphylactic reactions.
A Metagenomics Pipeline to Characterize Self-Collected Vaginal Microbiome Samples
Authors: Thomas-White Krystal , Hilt Evann E. , Olmschenk Genevieve , Gong Maryann , Phillips Caleb D. , Jarvis Courtney , Sanford Nicholas , White Jennifer , Navarro Pita
Published: September 2024

Vaginitis is a widespread issue for women worldwide, yet current diagnostic tools are lacking. Bacterial vaginosis (BV) is the most prevalent type of vaginitis, found in 10–50% of reproductive-aged women. Current diagnostic methods for BV rely on clinical criteria, microscopy, or the detection of a few microbes by qPCR. However, many vaginal infections lack a single etiological agent and are characterized by changes in the vaginal microbiome community structure (e.g., BV is defined as a loss of protective lactobacilli resulting in an overgrowth of anaerobic bacteria). Shotgun metagenomic sequencing provides a comprehensive view of all the organisms present in the vaginal microbiome (VMB), allowing for a better understanding of all potential etiologies. Here, we describe a robust VMB metagenomics sequencing test with a sensitivity of 93.1%, a specificity of 90%, a negative predictive value of 93.4%, and a positive predictive value of 89.6% certified by Clinical Laboratory Improvement Amendments (CLIA), the College of American Pathologist (CAP), and the Clinical Laboratory Evaluation Program (CLEP). We sequenced over 7000 human vaginal samples with this pipeline and described general findings and comparisons to US census data.
A Mediterranean Diet-Oriented Intervention Rescues Impaired Blood Cell Bioenergetics in Patients with Metabolic Dysfunction-Associated Steatotic Liver Disease
Authors: Segala Agnese , Vezzoli Marika , Vetturi Alice , Garrafa Emirena , Zanini Barbara , Bottani Emanuela , Marullo Monica , Marconi Silvia , Ricci Chiara , Valerio Alessandra
Published: September 2024

Background: Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), a novel term for Non-Alcoholic Fatty Liver Disease (NAFLD), is associated with liver mitochondrial dysfunction. We previously demonstrated that mitochondrial respiratory capacity in peripheral blood mononuclear cells (PBMCs) was significantly reduced in patients with MASLD compared to non-MASLD controls. For MASLD treatment, guidelines recommend behavioral and dietary changes to reduce body weight. A recent 12-month clinical trial demonstrated that ameliorating patients’ lifestyles through improved adherence to the Mediterranean diet and encouraged physical activity results in MASLD remission or regression. Methods: As a sub-study of the 12-month clinical trial, we evaluated the effects of the Mediterranean diet-oriented intervention on PBMC mitochondrial DNA content and respiratory parameters and on various biomarkers associated with MASLD. Results: Contrary to what was found at the baseline, after twelve months of intervention, systemic inflammatory and bioenergetics parameters did not differ between MASLD patients (N = 15) and control subjects (N = 17). PBMCs from MASLD subjects showed rescued basal respiration, ATP-linked respiration, maximal respiration, and spare respiratory capacity. The observed recovery coincided with a significant increase in the patients’ adherence to the Mediterranean diet (Medscore). Conclusions: Our findings indicate that a Mediterranean diet-oriented intervention, without calorie reduction, preserves blood cell mitochondrial function in MASLD subjects. Thus, PBMC bioenergetics-based assays might be taken into account not only for diagnosing but also for monitoring therapeutic responses in MASLD.
Age-Related Variations in Clinical Profiles for Children with Sports- and Recreation-Related Concussions
Authors: Ren Sicong , Corwin Daniel J. , McDonald Catherine C. , Fedonni Daniele , Master Christina L. , Arbogast Kristy B.
Published: September 2024

Objective: The purpose was to examine clinical profiles in concussed children aged 5–9 and 10–12 years and compare them with those of adolescents >12 years. Methods: This study included patients aged 5–18 years presenting to a specialty care concussion program with a sports- and recreation-related (SRR) concussion ≤28 days postinjury. Demographics, injury mechanisms, symptoms, and clinical features were assessed. Chi-squared tests, one-way ANOVA, and Kruskal-Wallis were used for comparisons across age groups. Results: A total of 3280 patients with SRR concussion were included: 5.0% were 5–9 years, 18.4% were 10–12 years, and 76.6% were 13–18 years. Younger age groups had more males than females (5–9 years: 70.7% vs. 29.3%) and more commonly sustained their injury during limited- (28.7%), and non-contact (7.9%) activities compared to other age groups (p < 0.01). Younger children presented less symptoms frequently (p ≤ 0.042), but higher symptom severity in somatic and emotional domains (p ≤ 0.016). Fewer 5–9-year-olds reported changes in school (25.6%), sleep (46.3%), and daily habits (40.9%) than adolescents (p < 0.001). Conclusions: Among SRR-concussed children and adolescents, we found significant age-related variations in demographics, injury mechanism, symptoms, and clinical features. Recognizing these unique features in younger children may facilitate targeted management and treatment.
Intra and Inter-Rater Variability in the Interpretation of White Blood Cell Scintigraphy of Hip and Knee Prostheses
Authors: Campagna Giuseppe , Lauri Chiara , Manta Ringo , Ottaviani Roberta , Vella Walter Davide , Signore Alberto
Published: September 2024

Background: White blood cell (WBC) scintigraphy plays a major role in the diagnostic approach to periprosthetic infections. Although the procedure has been standardized by the publication of several guidelines, the interpretation of this technique may be susceptible to intra and inter-variability. We aimed to assess the reproducibility of interpretation between nuclear medicine physicians and by the same physician and to demonstrate that Cohen’s coefficient is more unstable than Gwet’s coefficient, as the latter is influenced by the prevalence rates. Methods: We enrolled 59 patients who performed a Technetium-99m WBC (99mTc-WBC) scintigraphy for suspected hip or knee prosthesis infection. Three physicians, blinded to all patient clinical data, performed two image readings. Each WBC study was assessed both visually and semi-quantitatively according to the guidelines of the European Association of Nuclear Medicine (EANM). For semi-quantitative analysis, readers drew an irregular Region of Interest (ROI) over the suspected infectious lesion and copied it to the normal contralateral bone. The mean counts per ROI were used to calculate lesion-to-reference tissue (LR) ratios for both late and delayed images. An increase in LR over time (LRlate> LRdelayed) of more than 20% was considered indicative of infection. Agreement between readers and between readings was assessed by the first-order agreement coefficient (Gwet’s AC1). Reading time for each scan was compared between the three readers in both the first and the second reading, using the Generalized Linear Mixed Model. Results: An excellent agreement was found among all three readers: 0.90 for the first reading and 0.94 for the second reading. Both inter- and intra-variability showed values ≥0.86. Gwet’s method demonstrated greater robustness than the Cohen coefficient when assessing the intra and inter-rater variability, since it is not influenced by the prevalence rate. Conclusions: These studies can contribute to improving the reliability of nuclear medicine imaging techniques and to evaluating the effectiveness of trainee preparation.
The Diagnostic Accuracy of Colon Capsule Endoscopy in Inflammatory Bowel Disease—A Systematic Review and Meta-Analysis
Authors: Lei Ian Io , Thorndal Camilla , Manzoor Muhammad Shoaib , Parsons Nicholas , Noble Charlie , Huhulea Cristiana , Koulaouzidis Anastasios , Arasaradnam Ramesh P.
Published: September 2024

Colon capsule endoscopy (CCE) has regained popularity for lower gastrointestinal investigations since the COVID-19 pandemic. While there have been systematic reviews and meta-analyses on colonic polyp detection using CCE, there is a lack of comprehensive evidence concerning colonic inflammation. Therefore, this systematic review and meta-analysis aimed to assess the diagnostic accuracy of CCE for colonic inflammation, predominantly ulcerative colitis (UC) and Crohn’s disease (CD). Methods: We systematically searched electronic databases (EMBASE, MEDLINE, PubMed Central, and Cochrane Library) for studies comparing the diagnostic accuracy between CCE and optical endoscopy as the standard reference. A bivariate random effect model was used for the meta-analysis. Results: From 3797 publications, 23 studies involving 1353 patients were included. Nine studies focused on UC, and ten focused on CD. For UC, CCE showed a pooled sensitivity of 92% (95% CI, 88–95%), a specificity of 71% (95% CI, 35–92%), and an AUC of 0.93 (95% CI, 0.89–0.97). For CD, the pooled sensitivity was 92% (95% CI, 89–95%), and the specificity was 88% (95% CI, 84–92%), with an AUC of 0.87 (95% CI, 0.76–0.98). Overall, for inflammatory bowel disease, the pooled sensitivity, specificity, and AUC were 90% (95% CI, 85–93%), 76% (95% CI, 56–90%), and 0.92 (95% CI, 0.94–0.97), respectively. Conclusions: Despite the challenges around standardised disease scoring and the lack of histological confirmation, CCE performs well in diagnosing inflammatory bowel disease. It demonstrates high sensitivity in both UC and Crohn’s terminal ileitis and colitis and high specificity in Crohn’s disease. Further studies are needed to evaluate the diagnostic accuracy of other colonic inflammatory conditions.
A Review of the Prognostic Significance of Neutrophil-to-Lymphocyte Ratio in Nonhematologic Malignancies
Authors: Koc Defne Cigdem , Mănescu Ion Bogdan , Mănescu Măriuca , Dobreanu Minodora
Published: September 2024

Biomarkers are crucial in cancer diagnostics, prognosis, and surveillance. Extensive research has been dedicated to identifying biomarkers that are broadly applicable across multiple cancer types and can be easily obtained from routine investigations such as blood cell counts. One such biomarker, the neutrophil-to-lymphocyte ratio (NLR), has been established as a prognostic marker in cancer. However, due to the dynamic nature of cancer diagnosis and treatment, periodic updates are necessary to keep abreast of the vast amount of published data. In this review, we searched the PubMed database and analyzed and synthesized recent literature (2018–February 2024) on the role of NLR in predicting clinical outcomes in nonhematologic malignancies. The search was conducted using the PubMed database. We included a total of 88 studies, encompassing 28,050 human subjects, and categorized the findings into four major groups: gastrointestinal cancer, cancers of the urinary tract and reproductive system, lung cancer, and breast cancer. Our analysis confirms that NLR is a reliable prognostic indicator in cancer, and we discuss the specific characteristics, limitations, and exceptions associated with its use. The review concludes with a concise Q&A section, presenting the most relevant take-home messages in response to five key practical questions on this topic.
Beyond Pharmacology: A Narrative Review of Alternative Therapies for Anxiety Disorders
Authors: Antos Zuzanna , Zackiewicz Klaudia , Tomaszek Natalia , Modzelewski Stefan , Waszkiewicz Napoleon
Published: September 2024

Background: Anxiety disorders significantly reduce patients’ quality of life. Current pharmacological treatments, primarily benzodiazepines and antidepressants, are associated with numerous side effects. Consequently, there is a continual search for alternative methods to traditional therapies that are less burdensome for patients and broaden their therapeutic options. Our objective was to determine the role of selected alternative methods in the treatment of anxiety disorders. Methods: In this review, we examined recent evidence on alternative treatments for anxiety disorders, including physical activity, mindfulness, virtual reality (VR) technology, biofeedback, herbal remedies, transcranial magnetic stimulation (TMS), cryotherapy, hyperbaric therapy, vagus nerve stimulation (VNS), 3,4-methylenedioxymethamphetamine (MDMA), electroconvulsive therapy (ECT), and eye movement desensitization and reprocessing (EMDR) therapy. For this purpose we reviewed PubMed and after initial search, we excluded works unrelated to our aim, non-orginal data and animal studies. We conducted second search to cover all minor methods. Results: We included 116 studies, which data is presented in Tables. We have investigated which methods can support treatment and which can be used as a stand-alone treatment. We assessed the risks to benefits of using alternative treatments. Conclusion: Alternative treatments significantly expand the options available to patients and clinicians, with many serving as adjuncts to traditional therapies. Among the methods presented, mindfulness has the most significant therapeutic potential.
Sampled-Data Exponential Synchronization of Complex Dynamical Networks with Saturating Actuators
Authors: Guo Runan , Lv Wenshun
Published: September 2024

This paper investigates the problem of exponential synchronization control for complex dynamical systems (CDNs) with input saturation. Considering the effects of transmission delay, a memory sampled-data controller is designed. A modified two-sided looped functional is constructed that takes into account the entire sampling period, which includes both current state information and delayed state information. This functional only needs to be positive definite at the sampling instants. Sufficient criteria and the controller design method are provided to ensure the exponential synchronization of CDNs with input saturation under the influence of transmission delay, as well as the estimation of the basin of attraction. Additionally, an optimization algorithm for enlarging the region of attraction is proposed. Finally, a numerical example is presented to verify the effectiveness of the conclusion.
Sustainable Development of Small Business in Kazakhstan
Authors: Zarubina Venera , Zarubin Mikhail , Yessenkulova Zhaukhar , Gumarova Tursyngul , Daulbayeva Almira , Meimankulova Zhuldyz , Kurmangalieva Aizhan
Published: September 2024

The small business sector in Kazakhstan demonstrates dynamic growth, but its sustainable development in modern conditions requires activation from government agencies, society and entrepreneurs themselves. The purpose of the article was to study the development of small entrepreneurship in Kazakhstan and develop a mechanism for sustainable development based on network interaction “business entities–society–state” with the identification of priority areas of development based on a rating approach. The methods of bibliographic analysis of scientific literature, statistical, correlation analysis, survey, comparative analysis and ranking based on data normalization were used. The results revealed the need to formalize indicators of sustainable development of the small business sector in strategic development programs; create conditions, support, explain the importance and prospects of investing in sustainable innovations by small businesses and form a balanced strategy for achieving carbon neutrality based on network interaction “business entities–society–state” with the identification of priority areas of development based on a rating approach to ensure transparency and publicity of decisions made. Conclusions were made about the need to popularize sustainable development among business entities, stimulate investment activity through government participation, enact legislative regulation of public presentation of non-financial reporting, and assist society in the formation of an ESG culture.
Assessment of Envelope- and Machine Learning-Based Electrical Fault Type Detection Algorithms for Electrical Distribution Grids
Authors: Alaca Ozgur , Piesciorovsky Emilio Carlos , Ekti Ali Riza , Stenvig Nils , Gui Yonghao , Olama Mohammed Mohsen , Bhusal Narayan , Yadav Ajay
Published: September 2024

This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions.
Multi-Feature Extraction and Selection Method to Diagnose Burn Depth from Burn Images
Authors: Zhang Xizhe , Zhang Qi , Li Peixian , You Jie , Sun Jingzhang , Zhou Jianhang
Published: September 2024

Burn wound depth is a significant determinant of patient treatment. Typically, the evaluation of burn depth relies heavily on the clinical experience of doctors. Even experienced surgeons may not achieve high accuracy and speed in diagnosing burn depth. Thus, intelligent burn depth classification is useful and valuable. Here, an intelligent classification method for burn depth based on machine learning techniques is proposed. In particular, this method involves extracting color, texture, and depth features from images, and sequentially cascading these features. Then, an iterative selection method based on random forest feature importance measure is applied. The selected features are input into the random forest classifier to evaluate this proposed method using the standard burn dataset. This method classifies burn images, achieving an accuracy of 91.76% when classified into two categories and 80.74% when classified into three categories. The comprehensive experimental results indicate that this proposed method is capable of learning effective features from limited data samples and identifying burn depth effectively.
Prediction of Environmental Parameters for Predatory Mite Cultivation Based on Temporal Feature Clustering
Authors: Ma Ying , Lin Hongjie , Chen Wei , Chen Weijie , Wang Qianting
Published: September 2024

With the significant annual increase in market demand for biopesticides, the industrial production demand for predatory mites, which hold the largest market share among biopesticides, has also been rising. To achieve efficient and low-energy consumption control of predatory mite breeding environmental parameters, accurate estimation of breeding environmental parameters is necessary. This paper collects and pre-processes hourly time series data on temperature and humidity from industrial breeding environments. Time series prediction models such as SVR, LSTM, GRU, and LSTNet are applied to model and predict the historical data of the breeding environment. Experiments validate that the LSTNet model is more suitable for such environmental modeling. To further improve prediction accuracy, the training data for the LSTNet model is enhanced using hierarchical clustering of time series features. After augmentation, the root mean square error (RMSE) of the temperature prediction decreased by 27.3%, and the RMSE of the humidity prediction decreased by 32.8%, significantly improving the accuracy of the multistep predictions and providing substantial industrial application value.
Comprehensive Data Augmentation Approach Using WGAN-GP and UMAP for Enhancing Alzheimer’s Disease Diagnosis
Authors: Yuda Emi , Ando Tomoki , Kaneko Itaru , Yoshida Yutaka , Hirahara Daisuke
Published: September 2024

In this study, the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was used to improve the diagnosis of Alzheimer’s disease using medical imaging and the Alzheimer’s disease image dataset across four diagnostic classes. The WGAN-GP was employed for data augmentation. The original dataset, the augmented dataset and the combined data were mapped using Uniform Manifold Approximation and Projection (UMAP) in both a 2D and 3D space. The same combined interaction network analysis was then performed on the test data. The results showed that, for the test accuracy, the score was 30.46% for the original dataset (unbalanced), whereas for the WGAN-GP augmented dataset (balanced), it improved to 56.84%, indicating that the WGAN-GP augmentation can effectively address the unbalanced problem.
Localization Method for Insulation Degradation Area of the Metro Rail-to-Ground Based on Monitor Information
Authors: Wang Aimin , Li Yu , Yang Wenxuan , Pan Guangxu
Published: September 2024

Since rail-to-ground insulation decreases, large-level direct currents (DCs) leak from railways and form metro stray currents, corroding the buried metal. To locate the rail-to-ground insulation deterioration area, a location method is proposed based on parameter identification methods and the monitored information including the station rail potentials, currents at the traction power substations (TPSs), and train traction currents and train positions. According to the monitoring information of two adjacent TPSs, the section location model of the metro line is proposed, in which the rail-to-ground conductances of the test section are equivalent to the lumped parameters. Using the rail resistivity and traction currents as the known information, the rail-to-ground conductances are calculated with the least square method (LSM). The rail-to-ground insulation deterioration sections are identified by comparing the calculated conductances with thresholds determined by the standard requirements and section lengths. Then, according to the section location results, a detailed location model of the degradation section is proposed, considering the location distance accuracy. Using the genetic algorithm (GA) to calculate the rail-to-ground conductances, degradation positions are located by comparing the threshold calculated with the standard requirements and location distance accuracy. The location method is verified by comparing the calculation results under different degradation conditions. Moreover, the applications of the proposed method to different degradation lengths and different numbers of degradation sections are analyzed. The results show that the proposed method can locate rail-to-ground insulation deterioration areas.
Guard Band Protection Scheme to Facilitate Coexistence of 5G Base Stations and Radar Altimeters
Authors: Li Jiaqi , Hwang Seung-Hoon
Published: September 2024

Reformation of the 3.7–4.0 GHz band to expand 5G communication deployment poses a risk of 5G signals disrupting radar altimeter operation, leading to data loss or inaccuracies. Thus, this paper proposes a guard band protection method to facilitate the coexistence of 5G base stations and radar altimeters operating in the 4.2–4.4 GHz band. To enhance the adjacent channel leakage ratio (ACLR), we implemented spectral regrowth on an oversampled waveform using a high-power amplifier model, filtering out-of-band spectral emissions. The results demonstrated that a 150 MHz guard band enables coexistence, except in the case of the 16-by-16 antenna array in rural environments. Notably, for the 4-by-4 antenna array in urban environments, coexistence can be achieved using a 50 MHz guard band. The proposed mitigation techniques may also be extended to promote coexistence between non-terrestrial networks and 5G communication systems, including satellites, unmanned aerial vehicles, and hot air balloons.
Ecosystem Services of the Baltic Sea—State and Changes during the Last 150 Years
Authors: Schernewski Gerald , Neumann Thomas , Bučas Martynas , von Thenen Miriam
Published: September 2024

We assess the ecosystem services across the entire Baltic Sea using ecosystem model simulations and historical socio-economic data. Our approach covers 150 years, aggregated for the years around 1880, 1960, and 2010. The ecosystem services assessed include commercially usable wild fish biomass and wild plant biomass, water quality regulation (nitrogen and phosphorus retention), carbon storage, biodiversity and habitats, as well as active recreation and landscape aesthetics. In 2010, the commercially usable fish biomass in the entire Baltic Sea was 9.24 million tons. The total retention of nitrogen in the Baltic Sea was 884,135 t/a, phosphorus retention was 32,058 t/a, and carbon storage was 3,668,100 t/a. Between 1880 and 2010, the Baltic Sea-wide average biodiversity index decreased from 73 to 60, the active recreational quality index decreased from 76 to 69, and the observational recreation index declined from 91 to 78. In 2010, the most monetarily significant single ecosystem service in the Baltic Sea was nitrogen retention with EUR 26,822 million/a, followed by cultural ecosystem services. Other relevant services were fish catches (EUR 277 million/a), phosphorus retention (EUR 3854 million/a), and carbon storage (202 million/a). The latter recently showed a steep increase due to rising prices for CO2 certificates.
Modeling the Impacts of Climate Change on Potential Distribution of Betula luminifera H. Winkler in China Using MaxEnt
Authors: Yang Qiong , Xiang Yangzhou , Li Suhang , Zhao Ling , Liu Ying , Luo Yang , Long Yongjun , Yang Shuang , Luo Xuqiang
Published: September 2024

Betula luminifera H. Winkler, a fast-growing broad-leaved tree species native to China’s subtropical regions, possesses significant ecological and economic value. The species’ adaptability and ornamental characteristics make it a crucial component of forest ecosystems. However, the impacts of global climate change on its geographical distribution are not well understood, necessitating research to predict its potential distribution shifts under future climate scenarios. Our aims were to forecast the impact of climate change on the potential suitable distribution of B. luminifera across China using the MaxEnt model, which is recognized for its high predictive accuracy and low sample data requirement. Geographical coordinate data of B. luminifera distribution points were collected from various databases and verified for redundancy. Nineteen bioclimatic variables were selected and screened for correlation to avoid overfitting in the model. The MaxEnt model was optimized using the ENMeval package, and the model accuracy was evaluated using the Akaike Information Criterion Correction (delta.AICc), Training Omission Rate (OR10), and Area Under the Curve (AUC). The potential distribution of B. luminifera was predicted under current and future climate scenarios based on the Shared Socio-economic Pathways (SSPs). The optimized MaxEnt model demonstrated high predictive accuracy with an AUC value of 0.9. The dominant environmental variables influencing the distribution of B. luminifera were annual precipitation, minimum temperature of the coldest month, and standard deviation of temperature seasonality. The potential suitable habitat area and its geographical location were predicted to change significantly under different future climate scenarios, with complex dynamics of habitat expansion and contraction. The distribution centroid of B. luminifera was also predicted to migrate, indicating a response to changing climatic conditions. Our findings underscore the importance of model optimization in enhancing predictive accuracy and provide valuable insights for the development of conservation strategies and forest management plans to address the challenges posed by climate change.
Implementation of White-Hat Worms Using Mirai Source Code and Its Optimization through Parameter Tuning
Authors: Yamamoto Yudai , Fukushima Aoi , Yamaguchi Shingo
Published: September 2024

Mirai, an IoT malware that emerged in 2016, has been used for large-scale DDoS attacks. The Mirai source code is publicly available and continues to be a threat with a variety of variants still in existence. In this paper, we propose an implementation system for malicious and white-hat worms created using the Mirai source code, as well as a general and detailed implementation method for white-hat worms that is not limited to the Mirai source code. The white-hat worms have the function of a secondary infection, in which the white-hat worm disinfects the malicious worm by infecting devices already infected by the malicious worm, and two parameters, the values of which can be changed to modify the rate at which the white-hat worms can spread their infection. The values of the parameters of the best white-hat worm for disinfection of the malicious botnet and the impact of the value of each parameter on the disinfection of the malicious botnet were analyzed in detail. The analysis revealed that for a white-hat worm to disinfect a malicious botnet, it must be able to infect at least 80% of all devices and maintain that situation for at least 300 s. Then, by tuning and optimizing the values of the white-hat worm’s parameters, we were able to successfully eliminate the malicious botnet, demonstrating the effectiveness of the white-hat botnet’s function of eliminating the malicious botnet.
Advanced Multi-Label Fire Scene Image Classification via BiFormer, Domain-Adversarial Network and GCN
Authors: Bai Yu , Wang Dan , Li Qingliang , Liu Taihui , Ji Yuheng
Published: September 2024

Detecting wildfires presents significant challenges due to the presence of various potential targets in fire imagery, such as smoke, vehicles, and people. To address these challenges, we propose a novel multi-label classification model based on BiFormer’s feature extraction method, which constructs sparse region-indexing relations and performs feature extraction only in key regions, thereby facilitating more effective capture of flame characteristics. Additionally, we introduce a feature screening method based on a domain-adversarial neural network (DANN) to minimize misclassification by accurately determining feature domains. Furthermore, a feature discrimination method utilizing a Graph Convolutional Network (GCN) is proposed, enabling the model to capture label correlations more effectively and improve performance by constructing a label correlation matrix. This model enhances cross-domain generalization capability and improves recognition performance in fire scenarios. In the experimental phase, we developed a comprehensive dataset by integrating multiple fire-related public datasets, and conducted detailed comparison and ablation experiments. Results from the tenfold cross-validation demonstrate that the proposed model significantly improves recognition of multi-labeled images in fire scenarios. Compared with the baseline model, the mAP increased by 4.426%, CP by 4.14% and CF1 by 7.04%.
Deep Reinforcement Learning for Fluid Mechanics: Control, Optimization, and Automation
Authors: Kim Innyoung , Jeon Youngmin , Chae Jonghyun , You Donghyun
Published: September 2024

A comprehensive review of recent advancements in applying deep reinforcement learning (DRL) to fluid dynamics problems is presented. Applications in flow control and shape optimization, the primary fields where DRL is currently utilized, are thoroughly examined. Moreover, the review introduces emerging research trends in automation within computational fluid dynamics, a promising field for enhancing the efficiency and reliability of numerical analysis. Emphasis is placed on strategies developed to overcome challenges in applying DRL to complex, real-world engineering problems, such as data efficiency, turbulence, and partial observability. Specifically, the implementations of transfer learning, multi-agent reinforcement learning, and the partially observable Markov decision process are discussed, illustrating how these techniques can provide solutions to such issues. Finally, future research directions that could further advance the integration of DRL in fluid dynamics research are highlighted.
Sustainability in Global Agri-Food Supply Chains: Insights from a Comprehensive Literature Review and the ABCDE Framework
Authors: Wang Gaofeng , Wang Yingying , Li Shuai , Yi Yang , Li Chenming , Shin Changhoon
Published: September 2024

The sustainability of global agricultural produce supply chains is crucial for ensuring global food security, fostering environmental protection, and advancing socio-economic development. This study integrates bibliometric analysis, knowledge mapping, and the ABCDE framework to conduct a comprehensive qualitative and quantitative analysis of 742 relevant articles from the Web of Science core database spanning January 2009 to July 2023. Initially, bibliometric analysis and knowledge mapping reveal the annual progression of research on the sustainability of global agricultural produce supply chains, the collaborative networks among research institutions and authors, and the geographic distribution of research activities worldwide, successfully pinpointing the current research focal points. Subsequently, the ABCDE framework, constructed from the quantitative findings, helps us identify and comprehend the antecedents, barriers and challenges, impacts, and driving forces affecting the sustainability of these supply chains. The study identifies globalization and technological advancement as the primary forces shaping the sustainability of agricultural produce supply chains, despite them also posing challenges such as resource constraints and environmental pressures. Moreover, the application of innovative technologies, the optimization of organizational models, and active stakeholder engagement are key to propelling supply chains toward more sustainable development, exerting a profound impact on society, the environment, and the economy. In conclusion, this study suggests future research directions. The integrated methodology presented offers new perspectives and deep insights into the complexities of sustainable global agricultural produce supply chains, demonstrating its potential to foster knowledge innovation and practical applications, providing valuable insights for academic research and policy formulation in this domain.
Practical 1-Methylcyclopropene Technology for Increasing Apple (Malus domestica Borkh) Storability in the Aksu Region
Authors: Zhang Shuang , Li Yuanqing , Du Meijun , Li Xihong , Wang Junbo , Ban Zhaojun , Jiang Yunhong
Published: September 2024

In recent years, Aksu apple has become popular with consumers because of its unique texture and taste. At present, maintaining quality during storage is the key problem with the apples in the Aksu region. 1-Methylcyclopropene (1-MCP) can delay fruit senescence, so is widely used in fruit preservation. However, many factors affect the preservation effect of 1-MCP. The effects of 1-MCP concentration (0 µL·L−1, 1 µL·L−1, 3 µL·L−1, 5 µL·L−1, and 8 µL·L−1) and postharvest application time (0, 1 and 2 d after harvest) on the quality of stored apple were studied. It was found that 1 µL·L−1 1-MCP was more beneficial in improving the quality of stored apples, reduced the respiration intensity and decay rate, increased the fruit firmness and total soluble solid content, and reduced the relative content of ester volatile aroma components. In addition, 1-MCP treatment applied at different postharvest times also affected the sensory quality and flavor of apples. The effect of 1-MCP treatment immediately after harvest was better.
Antibiotic-Loaded Dendrimer Hydrogels in Periodontal Bone Regeneration: An In Vitro Release Feasibility Study
Authors: Yesbeck Nicholas , Huang Da , Carrico Caroline , Madurantakam Parthasarathy , Yang Hu
Published: September 2024

The prescription of a course of oral antibiotics following bone grafting procedures is a common practice in clinical periodontics to reduce surgical site infections. The goal of this study is to characterize the release profile of antibiotics via local delivery using dendrimer hydrogels (DH) and to analyze the effect of two different particulate bone allografts on the release of the antibiotics in vitro. DH were synthesized from polyamidoamine (PAMAM) dendrimer G5 and polyethylene glycol diacrylate, and cefazolin was chosen as the antibiotic. The antibiotic-loaded samples were bathed in PBS and incubated at 37 °C; aliquots were taken (1 h, 2 h, 3 h, 4 h, 5 h, 6 h, 12 h, 24 h, 48 h, 72 h) and analyzed using HPLC to determine the amounts of released cefazolin. In samples with DH, the estimated maximum concentration of cefazolin was 36.97 ± 2.39 μg/mL (95% CI: 34.58–39.36) with 50% released in 4.17 h (95%: 3.26–5.07) and an estimated growth rate of 0.27 (95% CI: 0.17–0.37). For samples without DH, the estimated maximum concentration of cefazolin was 167.4 ± 7.0 μg/mL (95% CI: 160.4–174.4) with 50% released in 2.36 h (95% CI: 2.05–2.67) and an estimated growth rate of 0.70 (95% CI: 0.54–0.87). We conclude that DH are a promising platform for sustained antibiotic release and that the presence of bone grafts did not significantly affect their release.