bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Formation Mechanism of Serrated Chips in the Cutting Process of Powder Metallurgy Superalloys Based on a Modified J–C Constitutive Relation Simulation Model
Authors: Ren Xiaoping , Ling Chen , Wang Xuepeng , Li Yinghao , Liu Zhanqiang , Wang Bing
Published: July 2024

Powder metallurgy superalloys are attracting great attention due to their unique performance advantages, such as good oxidation resistance, corrosion resistance, excellent tensile behavior, durability, fatigue properties, and long-term tissue stability. Therefore, powder superalloys show strong vitality in the field of advanced aerospace engines. However, the cutting force is large, and the serrated chips lead to poor machinability in the cutting process. The influence of dynamic recrystallization softening on serrated chips in the cutting process cannot be ignored. In this paper, the formation mechanism of serrated chips in the FGH96 cutting process is studied considering the influence of dynamic recrystallization softening. Firstly, based on the J–C constitutive relation modified by the recrystallization stress softening established previously, a finite element simulation model of the right-angle cutting of FGH96 is established. According to the results of the simulation model, the variation law of the thermal mechanical loading field in the formation process of serrated chips is quantitatively characterized. The validity of the simulation model is verified by comparison with the cutting force, chip morphology, and strain rate obtained from the experiment. Simulation results show that, in the formation process of serrated chips, the temperature field, strain field, and strain rate field in the first deformation zone show similar distribution characteristics to the shear band distribution, and with the formation of serrated chips, their values gradually increase. On this basis, the formation mechanism of serrated chips is revealed, which is the stage of serrated chip initiation, the stage of generating 50% serrated chips, the stage of generating 75% serrated chips, and the stage of serrated chip formation.
6G-RUPA: A Flexible, Scalable, and Energy-Efficient User Plane Architecture for Next-Generation Mobile Networks
Authors: Giménez-Antón Sergio , Grasa Eduard , Perelló Jordi , Cárdenas Andrés
Published: July 2024

As the global deployment of Fifth Generation (5G) is being well consolidated, the exploration of Sixth Generation (6G) wireless networks has intensified, focusing on novel Key Performance Indicators (KPIs) and Key Value Indicators (KVIs) that extend beyond traditional metrics like throughput and latency. As 5G begins transitioning to vertical-oriented applications, 6G aims go beyond, providing a ubiquitous communication experience by integrating diverse Radio Access Networks (RANs) and fixed-access networks to form a hyper-converged edge. This unified platform will enable seamless network federation, thus realizing the so-called network of networks vision. Emphasizing energy efficiency, the present paper discusses the importance of reducing telecommunications’ environmental impact, aligning with global sustainability goals. Central to this vision is the proposal of a novel user plane network protocol architecture, called 6G Recursive User Plane Architecture (6G-RUPA), designed to be scalable, flexible, and energy-efficient. Briefly, 6G-RUPA offers superior flexibility in network adaptation, federation, scalability, and mobility management, aiming to enhance overall network performance and sustainability. This study provides a comprehensive analysis of 6G’s potential, from its conceptual framework to the high-level design of 6G-RUPA, addressing current challenges and proposing actionable solutions for next-generation mobile networks.
MRI-Targeted Prostate Fusion Biopsy: What Are We Missing outside the Target? Implications for Treatment Planning
Authors: Oderda Marco , Dematteis Alessandro , Calleris Giorgio , Diamand Romain , Gatti Marco , Marra Giancarlo , Adans-Dester Gilles , Al Salhi Yazan , Pastore Antonio , Faletti Riccardo , Gontero Paolo
Published: July 2024

Introduction: This study aimed to evaluate the added diagnostic value of systematic biopsies (SBx) after magnetic resonance imaging (MRI)-targeted biopsies (TBx) and the presence of prostate cancer (PCa) outside MRI targets, in a prospective, contemporary, multicentric series of fusion biopsy patients. Methods: We collected data on 962 consecutive patients who underwent fusion biopsy between 2022 and 2024. Prostate cancer was considered clinically significant (csPCa) in the case of grade ≥ 2. Median test and Fisher exact chi-square tests were used. To identify predictors of out-field positivity, univariate and multivariable logistic regression analyses were performed. Results: Prostate cancer and csPCa were detected by TBx only in 56% and 50%, respectively, and by SBx only in 55% and 45%, respectively (p < 0.001). Prostate cancer and csPCa were diagnosed by TBx in 100 (10%) and 82 (8%) SBx-negative cases and by SBx in 86 (9%) and 54 (6%) TBx-negative cases (p < 0.001). Tumors outside MRI targets were found in 213 (33%) cases in the same lobe and 208 (32%) in the contralateral lobe, most of them being csPCa. Predictors of out-field contralateral PCa were positive DRE (HR 1.50, p 0.03), PSA density ≥ 0.15 (HR 2.20, p < 0.001), and PI-RADS score 5 (HR 2.04, p 0.01). Conclusions: Both TBx and SBx identify a non-negligible proportion of csPCa when the other modality is negative. SBx after TBx should always be considered given the risk of missing other csPCa foci within the prostate, especially in patients with positive DRE, PSA density ≥ 0.15, and PIRADS 5 lesions.
Prostate-Specific Membrane Antigen Expression in Patients with Primary Prostate Cancer: Diagnostic and Prognostic Value in Positron Emission Tomography-Prostate-Specific Membrane Antigen
Authors: Tayara Omar , Poletajew Sławomir , Malewski Wojciech , Kunikowska Jolanta , Pełka Kacper , Kryst Piotr , Nyk Łukasz
Published: July 2024

Prostate cancer represents a significant public health challenge, with its management requiring precise diagnostic and prognostic tools. Prostate-specific membrane antigen (PSMA), a cell surface enzyme overexpressed in prostate cancer cells, has emerged as a pivotal biomarker. PSMA’s ability to increase the sensitivity of PET imaging has revolutionized its application in the clinical management of prostate cancer. The advancements in PET-PSMA imaging technologies and methodologies, including the development of PSMA-targeted radiotracers and optimized imaging protocols, led to diagnostic accuracy and clinical utility across different stages of prostate cancer. This highlights its superiority in staging and its comparative effectiveness against conventional imaging modalities. This paper analyzes the impact of PET-PSMA on prostate cancer management, discussing the existing challenges and suggesting future research directions. The integration of recent studies and reviews underscores the evolving understanding of PET-PSMA imaging, marking its significant but still expanding role in clinical practice. This comprehensive review serves as a crucial resource for clinicians and researchers involved in the multifaceted domains of prostate cancer diagnosis, treatment, and management.
Diagnostic Challenges in Difficult-to-Localize Insulinomas: A Case Report and Review of Literature
Authors: Grubor Nikica M. , Grubor Nikola N. , Micev Marjan
Published: July 2024

Non-somatostatin receptor expressing hypovascular insulinomas can be challenging to prove through imaging. This case highlights the utility of a structured approach to molecular imaging in patients with confirmed endogenous hyperinsulinemia. A 54-year-old woman was admitted because of a sudden loss of consciousness. Her relative reported that she complained of dizziness, intense sweating, blurry vision, and upper extremity tingling before becoming unresponsive for 20 min, after which the patient had little recollection of the event. She experienced similar episodes of shorter duration, trouble recalling everyday events, and unintentional weight gain of over 10 kg during the previous two years. Abdominal magnetic resonance imaging (MRI) and multidetector computerized tomography (MDCT) were unremarkable. Selective arterial calcium stimulation significantly increased hepatic venous insulin concentrations when the superior mesenteric and gastroduodenal arteries were stimulated. Technetium-99m (99mTc) octreotide single-photon emission computed tomography (SPECT) did not localize the lesion. Gallium-68 DOTA-Exendin-4 PET/CT acquisition was performed. A single intense 2 cm hyperperfused pancreatic lesion was located anteriorly in the head of the pancreas. Earlier targeted PET/CT imaging and recognition of significant neuropsychiatric symptoms attributable to the patient’s hypoglycemic state might have accelerated the resolution of her condition and obviated the need for unnecessary testing.
Artificial Intelligence Models for the Detection of Microsatellite Instability from Whole-Slide Imaging of Colorectal Cancer
Authors: Faa Gavino , Coghe Ferdinando , Pretta Andrea , Castagnola Massimo , Van Eyken Peter , Saba Luca , Scartozzi Mario , Fraschini Matteo
Published: July 2024

With the advent of whole-slide imaging (WSI), a technology that can digitally scan whole slides in high resolution, pathology is undergoing a digital revolution. Detecting microsatellite instability (MSI) in colorectal cancer is crucial for proper treatment, as it identifies patients responsible for immunotherapy. Even though universal testing for MSI is recommended, particularly in patients affected by colorectal cancer (CRC), many patients remain untested, and they reside mainly in low-income countries. A critical need exists for accessible, low-cost tools to perform MSI pre-screening. Here, the potential predictive role of the most relevant artificial intelligence-driven models in predicting microsatellite instability directly from histology alone is discussed, focusing on CRC. The role of deep learning (DL) models in identifying the MSI status is here analyzed in the most relevant studies reporting the development of algorithms trained to this end. The most important performance and the most relevant deficiencies are discussed for every AI method. The models proposed for algorithm sharing among multiple research and clinical centers, including federal learning (FL) and swarm learning (SL), are reported. According to all the studies reported here, AI models are valuable tools for predicting MSI status on WSI alone in CRC. The use of digitized H&E-stained sections and a trained algorithm allow the extraction of relevant molecular information, such as MSI status, in a short time and at a low cost. The possible advantages related to introducing DL methods in routine surgical pathology are underlined here, and the acceleration of the digital transformation of pathology departments and services is recommended.
Twitter and the Affordance: A Case Study of Participatory Roles in the #Marchforourlives Network
Authors: Chong Miyoung
Published: July 2024

The study empirically analyzed activism participants’ roles drawn from the lens of social media affordance and identified the activism opinion leaders based on the framework of network connectivity, message diffusion, and semantic relevancy through the case of the #Marchforourlives Twitter network, which has been rebranded as X. The study defines the #Marchforourlives Twitter network as a co-created activism network in collaboration with different degrees of contributors, such as the core advocates, the advocates, the supporters, and the amplifiers. The results showed that a very small number of tweets created by the core advocates played significant roles due to their extensive adoption by other participants, while many other original tweets were never mentioned or retweeted in the network. This study disclosed the extensive proportion of amplifiers as 95.13% among the examined participants. The study findings suggest that creating core agenda tweets with high amplifiability might be critical for successful hashtag activism to attract like-minded masses as networked protesters.
Sudden Cardiac Death-Etiology, Risk Factors and Demographic Characteristics: An Extensive Study of 1618 Forensic Autopsies
Authors: Radu Ioana , Farcas Anca Otilia , Nyulas Victoria , Radu Carmen Corina , Brinzaniuc Klara
Published: July 2024

Background: Sudden cardiac death (SCD) is a major public health concern worldwide, affecting all age and social groups. Methods: In this retrospective study, of the 8265 autopsies performed in the Institute of Legal Medicine, 1618 cases of SCD were included. The aim of this study is to identify demographic characteristics, etiological factors, epidemiological characteristics and risk factors that lead to SCD. Results: The highest incidence of SCD was in age group 40–69 years (65.0%), 71.6% of this age group being men. Of the total number, 32.1% (520) occurred in the emergency room. The most common cause of sudden death is represented by coronary atherosclerotic disease, reported in 89.8% (1453) of cases, tricoronary lesions being found in 60% (870) of cases. Etiological factors of SCD encountered during autopsies were acute myocardial infarction in 13.9% (225), dilated cardiomyopathy 43.9% (710), cardiac hypertrophy 579 (36.07%), pericarditis 1.9% (30), myocarditis 1.73% (28) and adipositas cordis 5% (81). Along with epicardial fat and BMI, alcohol consumption was recorded in 17.9% (290), this being a potential trigger. Conclusions: Based on forensic autopsy and histological findings, a wide variety of factors are involved in the etiopathogenesis of SCD, some of which can be eliminated through preventive measures implemented early.
Balance Impairment in the Burn Population: A Burn Model System National Database Study
Authors: Santos Edward , Chacon Kaitlyn L. , Shepler Lauren J. , McMullen Kara A. , Slavin Mary D. , van de Rijn Marc , Kowalske Karen J. , Ryan Colleen M. , Schneider Jeffrey C.
Published: July 2024

Balance is an important component of daily function and impairments can lead to injury and quality-of-life limitations. Balance is not well studied in the burn population. This study examines the frequency of long-term balance impairments and associated factors after a burn injury. The Burn Model System National Database was analyzed. Trouble with balance was self-reported at discharge, 6, 12, 24, and 60 months after injury. Regression analyses examined the associations between demographic and clinical characteristics and balance impairments at 12 months. Of 572 participants, balance impairments were most reported at discharge (40.3%), continuing over 60 months (26.8–36.0%). Those reporting balance impairments (n = 153) were more likely to be older, unemployed, have Medicaid or Medicare, receive inpatient rehabilitation, receive outpatient physical or occupational therapy, have vision problems, have leg or feet burns and swelling, and have foot numbness compared to those without (p ≤ 0.001). Regression analysis demonstrated a 4% increased odds of balance impairment for every increase in year of age (p < 0.001), 71% lower odds if employed at time of injury (p < 0.001), and 140% higher odds if receiving outpatient physical or occupational therapy at 12 months (p = 0.008). Common reports of balance impairments highlight the need for routine screenings to identify burn survivors that may benefit from targeted interventions.
ChatGPT Promises and Challenges in Education: Computational and Ethical Perspectives
Authors: Adel Amr , Ahsan Ali , Davison Claire
Published: July 2024

This paper investigates the integration of ChatGPT into educational environments, focusing on its potential to enhance personalized learning and the ethical concerns it raises. Through a systematic literature review, interest analysis, and case studies, the research scrutinizes the application of ChatGPT in diverse educational contexts, evaluating its impact on teaching and learning practices. The key findings reveal that ChatGPT can significantly enrich education by offering dynamic, personalized learning experiences and real-time feedback, thereby boosting teaching efficiency and learner engagement. However, the study also highlights significant challenges, such as biases in AI algorithms that may distort educational content and the inability of AI to replicate the emotional and interpersonal dynamics of traditional teacher–student interactions. The paper acknowledges the fast-paced evolution of AI technologies, which may render some findings obsolete, underscoring the need for ongoing research to adapt educational strategies accordingly. This study provides a balanced analysis of the opportunities and challenges of ChatGPT in education, emphasizing ethical considerations and offering strategic insights for the responsible integration of AI technologies. These insights are valuable for educators, policymakers, and researchers involved in the digital transformation of education.
An Improved Weighted Cross-Entropy-Based Convolutional Neural Network for Auxiliary Diagnosis of Pneumonia
Authors: Song Zhenyu , Shi Zhanling , Yan Xuemei , Zhang Bin , Song Shuangbao , Tang Cheng
Published: July 2024

Pneumonia has long been a significant concern in global public health. With the advancement of convolutional neural networks (CNNs), new technological methods have emerged to address this challenge. However, the application of CNNs to pneumonia diagnosis still faces several critical issues. First, the datasets used for training models often suffer from insufficient sample sizes and imbalanced class distributions, leading to reduced classification performance. Second, although CNNs can automatically extract features and make decisions from complex image data, their interpretability is relatively poor, limiting their widespread use in clinical diagnosis to some extent. To address these issues, a novel weighted cross-entropy loss function is proposed, which calculates weights via an inverse proportion exponential function to handle data imbalance more efficiently. Additionally, we employ a transfer learning approach that combines pretrained CNN model parameter fine-tuning to improve classification performance. Finally, we introduce the gradient-weighted class activation mapping method to enhance the interpretability of the model’s decisions by visualizing the image regions of focus. The experimental results indicate that our proposed approach significantly enhances CNN performance in pneumonia diagnosis tasks. Among the four selected models, the accuracy rates improved to over 90%, and visualized results were provided.
MTrans: M-Transformer and Knowledge Graph-Based Network for Predicting Drug–Drug Interactions
Authors: Wu Shiqi , Liu Baisong , Zhang Xueyuan , Shao Xiaowen , Lin Chennan
Published: July 2024

The combined use of multiple medications is common in treatment, which may lead to severe drug–drug interactions (DDIs). Deep learning methods have been widely used to predict DDIs in recent years. However, current models need help to fully understand the characteristics of drugs and the relationships between these characteristics, resulting in inaccurate and inefficient feature representations. Beyond that, existing studies predominantly focus on analyzing a single DDIs, failing to explore multiple similar DDIs simultaneously, thus limiting the discovery of common mechanisms underlying DDIs. To address these limitations, this research proposes a method based on M-Transformer and knowledge graph for predicting DDIs, comprising a dual-pathway approach and neural network. In the first pathway, we leverage the interpretability of the transformer to capture the intricate relationships between drug features using the multi-head attention mechanism, identifying and discarding redundant information to obtain a more refined and information-dense drug representation. However, due to the potential difficulty for a single transformer model to understand features from multiple semantic spaces, we adopted M-Transformer to understand the structural and pharmacological information of the drug as well as the connections between them. In the second pathway, we constructed a drug–drug interaction knowledge graph (DDIKG) using drug representation vectors obtained from M-Transformer as nodes and DDI types as edges. Subsequently, drug edges with similar interactions were aggregated using a graph neural network (GNN). This facilitates the exploration and extraction of shared mechanisms underlying drug–drug interactions. Extensive experiments demonstrate that our MTrans model accurately predicts DDIs and outperforms state-of-the-art models.
Improved Feature Point Extraction Method of VSLAM in Low-Light Dynamic Environment
Authors: Wang Yang , Zhang Yi , Hu Lihe , Ge Gengyu , Wang Wei , Tan Shuyi
Published: July 2024

Visual simultaneous localization and mapping (VSLAM) is pivotal for intelligent mobile robots. VSLAM systems can be used to identify scenes by obtaining massive amounts of redundant texture information from the environment. However, VSLAM faces a major challenge in dynamic low-light environments, in which the extraction of feature points is often difficult, leading to tracking failure with mobile robots. Therefore, we developed a method to improve the feature point extraction method used for VSLAM. We first used the contrast limited adaptive histogram equalization (CLAHE) method to increase the contrast in low-light images, allowing for the extraction of more feature points. Second, in order to increase the effectiveness of the extracted feature points, the redundant feature points were removed. We developed three conditions to filter the feature points. Finally, the proposed method was tested on popular datasets (e.g., TUM and OpenLORIS-Scene), and the results were compared with those of several traditional methods. The results of the experiments showed that the proposed method is feasible and highly robust in dynamic low-light environments.
Attention Mechanism and Neural Ordinary Differential Equations for the Incomplete Trajectory Information Prediction of Unmanned Aerial Vehicles Using Airborne Radar
Authors: Peng Haojie , Yang Wei , Wang Zheng , Chen Ruihai
Published: July 2024

Due to the potential for airborne radar to capture incomplete observational information regarding unmanned aerial vehicle (UAV) trajectories, this study introduces a novel approach called Node-former, which integrates neural ordinary differential equations (NODEs) and the Informer framework. The proposed method exhibits high accuracy in trajectory prediction, even in scenarios with prolonged data interruptions. Initially, data outside the acceptable error range are discarded to mitigate the impact of interruptions on prediction accuracy. Subsequently, to address the irregular sampling caused by data elimination, NODEs are utilized to transform computational interpolation into an initial value problem (IPV), thus preserving informative features. Furthermore, this study enhances the Informer’s encoder through the utilization of time-series prior knowledge and introduces an ODE solver as the decoder to mitigate fluctuations in the original decoder’s output. This approach not only accelerates feature extraction for long sequence data, but also ensures smooth and robust output values. Experimental results demonstrate the superior performance of Node-former in trajectory prediction with interrupted data compared to traditional algorithms.
Green Wave Arterial Cooperative Control Strategy Based on Through-Traffic Priority
Authors: Bao Riyong , Huang Wei , Lin Yi , Lian Peikun , Easa Said M. , Chen Ning
Published: July 2024

Mainline coordinated control is usually based on fixed speed and statistical traffic flow by period. However, in actual operation, the vehicles parked in front of the intersection and the arriving vehicles often fluctuate, and the through-traffic green time is wasted due to phase transition, which leads to mismatches between the signal plans and actual traffic flow requirements, affecting the traffic efficiency of the intersection. To address the above issues, using vehicle–road collaborative control (VRCC), by calculating the phase difference lead time and phase difference of adjacent intersections, the green extension time for the green wave through-traffic phase, and the guiding vehicle speed, the goal of reducing the detention volume of through traffic, reducing the waste of through-traffic green time caused by phase transitions and improving the throughput of through traffic can be achieved. The speed of the green wave traffic flow is increased by guiding vehicles to form saturated platoons during green periods. Finally, PTV VISSIM 4.3 was used for simulation verification, and the results showed that compared to not implementing the control strategy, the average delay on the arterial road was reduced by 85.1%, the average number of stops was reduced by 84.3%, the average travel time was reduced by 34%, and the average queue length was reduced by 62.6%. This significantly improved the efficiency of traffic on the arterial road and effectively reduced congestion.
LBCNIN: Local Binary Convolution Network with Intra-Class Normalization for Texture Recognition with Applications in Tactile Internet
Authors: Neshov Nikolay , Tonchev Krasimir , Manolova Agata
Published: July 2024

Texture recognition is a pivotal task in computer vision, crucial for applications in material sciences, medicine, and agriculture. Leveraging advancements in Deep Neural Networks (DNNs), researchers seek robust methods to discern intricate patterns in images. In the context of the burgeoning Tactile Internet (TI), efficient texture recognition algorithms are essential for real-time applications. This paper introduces a method named Local Binary Convolution Network with Intra-class Normalization (LBCNIN) for texture recognition. Incorporating features from the last layer of the backbone, LBCNIN employs a non-trainable Local Binary Convolution (LBC) layer, inspired by Local Binary Patterns (LBP), without fine-tuning the backbone. The encoded feature vector is fed into a linear Support Vector Machine (SVM) for classification, serving as the only trainable component. In the context of TI, the availability of images from multiple views, such as in 3D object semantic segmentation, allows for more data per object. Consequently, LBCNIN processes batches where each batch contains images from the same material class, with batch normalization employed as an intra-class normalization method, aiming to produce better results than single images. Comprehensive evaluations across texture benchmarks demonstrate LBCNIN’s ability to achieve very good results under different resource constraints, attributed to the variability in backbone architectures.
Energy Efficiency in Polish Farms
Authors: Szajner Piotr , Wieliczko Barbara
Published: July 2024

Agriculture in Poland plays an important social and environmental role. Accession to the EU resulted in structural and modernization changes, following adjustments to CAP obligations. In 2019, the European Green Deal and “From Farm to Fork” strategies called for circularity, zero emissions, and food and energy security. The purpose of this study was to assess the consumption and energy efficiency of Polish farms, identify challenges in energy management, and formulate recommendations. This study used data from Polish Statistics, FADN, and other public bodies collecting relevant data. The assessment of energy intensity was carried out based on the concept of technical efficiency by Farell and Debreu, defined as the ratio of effects to inputs. In addition, methods of comparative and descriptive statistics were used. The average annual dynamics of energy consumption and CO2 emissions were determined using the compound percentage formula. The results of this research indicate positive changes in the energy management in Polish agriculture, including a decrease in production energy intensity, CO2 emissions, and the amount of waste generated by the investments made. It is necessary to improve farm energy efficiency further and to increase the use of renewable energy to maintain cost competitiveness and meet environmental requirements.
Hybrid ANPC Grid-Tied Inverter Design with Passivity-Based Sliding Mode Control Strategy
Authors: Zhang Yifei , Li Kang , Zhang Li
Published: July 2024

Voltage source inverters are extensively used in the grid connection of renewable energy-sourced generators, and multilevel converters, in particular, have attracted a great deal of attention in recent years. This paper investigates the application of a novel passivity-based sliding mode (PSM) control scheme on three-level grid-tie active Neutral-Point-Clamped (ANPC) inverters that yield fast and stable responses to grid impedance variations. Simulation studies confirm that this control scheme can produce high tracking performance and is also robust against grid load variations. Furthermore, to enhance ANPC efficiency, the loss distribution of switching devices controlled by the proposed strategy is evaluated. An optimal scheme is finally proposed for allocating silicon and Wide-Band-Gap switching devices, resulting in a hybrid ANPC inverter capable of achieving a desirable trade-off between the power losses and the device cost.
Long Short-Term Memory Autoencoder and Extreme Gradient Boosting-Based Factory Energy Management Framework for Power Consumption Forecasting
Authors: Moon Yeeun , Lee Younjeong , Hwang Yejin , Jeong Jongpil
Published: July 2024

Electricity consumption prediction is crucial for the operation, strategic planning, and maintenance of power grid infrastructure. The effective management of power systems depends on accurately predicting electricity usage patterns and intensity. This study aims to enhance the operational efficiency of power systems and minimize environmental impact by predicting mid to long-term electricity consumption in industrial facilities, particularly in forging processes, and detecting anomalies in energy consumption. We propose an ensemble model combining Extreme Gradient Boosting (XGBoost) and a Long Short-Term Memory Autoencoder (LSTM-AE) to accurately forecast power consumption. This approach leverages the strengths of both models to improve prediction accuracy and responsiveness. The dataset includes power consumption data from forging processes in manufacturing plants, as well as system load and System Marginal Price data. During data preprocessing, Expectation Maximization Principal Component Analysis was applied to address missing values and select significant features, optimizing the model. The proposed method achieved a Mean Absolute Error of 0.020, a Mean Squared Error of 0.021, a Coefficient of Determination of 0.99, and a Symmetric Mean Absolute Percentage Error of 4.24, highlighting its superior predictive performance and low relative error. These findings underscore the model’s reliability and accuracy for integration into Energy Management Systems for real-time data processing and mid to long-term energy planning, facilitating sustainable energy use and informed decision making in industrial settings.
A Novel Sustainable Approach for Site Selection of Underground Hydrogen Storage in Poland Using Deep Learning
Authors: Derakhshani Reza , Lankof Leszek , GhasemiNejad Amin , Zarasvandi Alireza , Amani Zarin Mohammad Mahdi , Zaresefat Mojtaba
Published: July 2024

This research investigates the potential of using bedded salt formations for underground hydrogen storage. We present a novel artificial intelligence framework that employs spatial data analysis and multi-criteria decision-making to pinpoint the most appropriate sites for hydrogen storage in salt caverns. This methodology incorporates a comprehensive platform enhanced by a deep learning algorithm, specifically a convolutional neural network (CNN), to generate suitability maps for rock salt deposits for hydrogen storage. The efficacy of the CNN algorithm was assessed using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and the Correlation Coefficient (R2), with comparisons made to a real-world dataset. The CNN model showed outstanding performance, with an R2 of 0.96, MSE of 1.97, MAE of 1.003, and RMSE of 1.4. This novel approach leverages advanced deep learning techniques to offer a unique framework for assessing the viability of underground hydrogen storage. It presents a significant advancement in the field, offering valuable insights for a wide range of stakeholders and facilitating the identification of ideal sites for hydrogen storage facilities, thereby supporting informed decision-making and sustainable energy infrastructure development.
Bitter Phytochemicals Acutely Lower Blood Glucose Levels by Inhibition of Glucose Absorption in the Gut
Authors: Palatini Jackson Kimberly Marie , Mhawish Reham , Komarnytsky Slavko
Published: July 2024

For early hominids, frequent encounters with plant foods necessitated the ability to discern bitter poisons and adjust the activity of the gastrointestinal system in anticipation of carbohydrate-rich meals. Plants bitters were also used historically to manage a variety of metabolic and digestive disorders despite an immense structural diversity of bitter phytochemicals without a common molecular target. Our study confirms these observations in a standardized C57BL/6J prediabetic mouse model using 24 model compounds by demonstrating acute lower peak blood glucose values and improved glucose tolerance following intragastric, but not intraperitoneal, treatment. The administration of the synthetic bitter compound denatonium benzoate yielded similar results that were attenuated by co-application of the allosteric inhibitor of the bitter TAS2R receptors. We also show that these effects occur dose-dependently; associate with reduced glucose uptake, increased intracellular [Ca2+] fluxes, and enhanced GLP-1 expression; and are attenuated by the TAS2R inhibitor in the neuroendocrine STC-1 intestinal cells. These findings support the view that inhibition of glucose transport from the intestinal lumen to the blood by TAS2R bitter receptor signaling in the gut may represent a common mechanism in the acute response to oral ingestion of bitter phytochemicals.
Control of Floating Body Waves Due to an Airplane Takeoff from a Very Large Floating Airport
Authors: Kakinuma Taro , Fukuura Yusei
Published: July 2024

Numerical simulations were generated to investigate the response of a very large floating airport to an airplane takeoff, using the set of nonlinear shallow water equations of velocity potential for water waves interacting with a floating thin plate. We have proposed two methods to reduce persistent airport vibration: reflectance reduction by decreasing the flexural rigidity in airport edge parts and amplification reduction by decreasing the still water depth partially under airport runways. First, when the flexural rigidity is uniformly decreased in an airport edge part, the reflectance of the floating body waves due to a B737 was reduced because of the multiple reflections. However, the wave reflectance for a B747 increased, depending on the conditions. A too-long edge part was not effective in reducing the wave reflectance. Conversely, when the flexural rigidity is linearly decreased in an airport edge part, the wave reflectance was reduced for both airplanes. Second, when the still water depth under an airport runway is partially reduced at the location where floating body waves are amplified, the wave heights of floating body waves tended to decrease as the still water depth in the shallower area decreased.
Bone Drilling: Review with Lab Case Study of Bone Layer Classification Using Vibration Signal and Deep Learning Methods
Authors: Caesarendra Wahyu
Published: July 2024

In orthopedics, bone drilling is a crucial part of a surgical method commonly carried out for internal fixation in bone fracture treatment. The primary purpose of bone drilling is the creation of holes for screw insertion to immobilize fractured parts. The bone drilling task depends on the orthopedist and surgeon’s high level of skill and experience. This paper aimed to provide a summary of previously published review studies in the field of bone drilling. This review paper also presents a comprehensive review of the application of machine learning for bone drilling and as a future direction for automation systems. This review can also help medical surgeons and bone drillers understand the latest improvements through parameter selection and optimization strategies to reduce bone damage in bone drilling procedures. Apart from the review, bone drilling vibration data collected in a university laboratory experiment is also presented in this study. The vibration data consist of three different layers of femur cow bone, which are processed and classified using several deep learning (DL) methods such as long short-term memory (LSTM), convolutional neural network (CNN), and recurrent neural network (RNN). These DL methods are used in the bone drilling lab case study to prove that the layers of bone drilling are associated with the vibration signal and that they can be classified and predicted using DL methods. The result shows that LSTM is outperformed by CNN and RNN.
Development, Designing and Testing of a New Test Rig for Studying Innovative Polycrystalline Diamond Bearings
Authors: Cascino Alessio , Amedei Andrea , Meli Enrico , Rindi Andrea
Published: July 2024

This paper reports the preliminary experimental studies carried out on an innovative sliding bearing made of polycrystalline diamond, a material with excellent mechanical and chemical characteristics, used mainly in the drilling industry. Bearings crafted from this material do not necessitate lubrication due to their extremely low coefficient of friction and high resistance to wear. For this reason, they are prime candidates for replacing traditional oil bearings, eliminating the need for auxiliary systems and thereby reducing environmental risks. In this regard, an innovative test rig was designed, capable of reaching speeds up to 6000 rpm both in vertical and horizontal configurations thanks to a novel tilting frame. Moreover, with a high modularity it was possible to test three different kinds of radial PCD bearings. Dynamic data were acquired and elaborated to evaluate orbits, acceleration and absorbed torque, to finally compare these different configurations to better understand how dynamic behavior is influenced by bearings’ geometrical characteristics.
Numerical Simulation of Ablative Damage in Gas-Assisted Laser Processing of Wood
Authors: Liu Qingwei , Ning Lijia , Yang Chunmei , Wang Fucheng , Liu Tianxiang
Published: July 2024

To reduce defects in wood laser processing, this study establishes a multi-field coupling model that reflects the coupling relationship between laser energy, auxiliary gas, and slit quality. It reveals the temperature field distribution and heat transfer during this process at a macroscopic level. Using the COMSOL Multiphysics 5.6 software to simulate laser processing with or without helium gas assistance, we show that helium-assisted laser processing positively affects the quality of wood processing. We further researched the influence of different laser powers and spot radii on ablation damage volume and gasification volume, and the results indicate that the adopted model effectively simulates the influence of laser power and auxiliary gas on laser cutting ability, accurately reflecting the impact of different process parameters on cutting depth and kerf width. The accuracy and effectiveness of this model were validated through comparison with experimental data. This research enhances process reliability and economic benefits through numerical simulation and prediction, expands theoretical research and engineering applications in the laser processing field, and optimizes and innovates wood processing technology. It provides a promising method for enhancing the added value of wood products and efficiently using wood resources.
Genome-Wide Identification of WRKY in Suaeda australis against Salt Stress
Authors: Qu Yinquan , Wang Ji , Qu Caihui , Mo Xiaoyun , Zhang Xiumei
Published: July 2024

Suaeda australis is a typical halophyte due to its high salt tolerance. The WRKY gene family plays crucial roles in responding to salt stress, yet reports on WRKY genes in S. australis are scarce. In this study, we identified 47 WRKY genes in the S. australis genome. We then conducted comprehensive analyses, including investigations into their chromosomal locations, gene structures, phylogenetic relationships, promoter regions, conserved motifs, and expression profiles. The 47 WRKY genes were classified into three main groups (with six subfamilies). Among nine chromosomes, S. australis displayed an unequal pattern of distribution. The analysis of regulatory elements revealed that WRKY promoters were associated with light responsiveness, anaerobic induction, drought inducibility, meristem expression, and gibberellin responsiveness. Expression pattern analyses highlighted the role of several SaWRKYs, including Sau00527, Sau00681, Sau18413, Sau19293, Sau00810, Sau05901, Sau09209, Sau12457, and Sau14103. These genes exhibited higher expression levels under ST2 compared to ST1, indicating a significant response to salt stress. Higher SOD, POD, and CAT activity, as well as increased MDA and H2O2 content, were observed in ST2, in line with the expression patterns and our RTq-PCR results. Our study offers a profound understanding of the evolutionary development of S. australis WRKY members, clarifying their vital functions in responding to salt stress. Along with crucial genomic data, these findings establish a solid foundation for investigating the mechanisms of salt-stress regulation in S. australis. This research holds substantial scientific and ecological importance, offering potential contributions to the conservation of S. australis and the utilization of saline soil resources.
Impact of Different Land Use Types on Bacterial and Fungal Communities in a Typical Karst Depression in Southwestern China
Authors: Hu Cong , Zhang Zhonghua , Zhong Chaofang , Hu Gang , Xu Chaohao
Published: July 2024

Understanding the land use pattern relationships regarding the composition, diversity, and abundance of soil microbial communities in a typical karst depression in southwestern China is crucial for assessing the stability of local karst ecosystems. However, these aspects in typical karst depressions within northern tropical karst seasonal rainforests in China remain limited. Therefore, we examined the differences in soil microorganism abundance, diversity, community composition, and co-occurrence networks under five land use types in a tropical karst region in southwestern China: sugarcane fields, orchards, grasslands, plantation forests, and secondary forests. The soil microbial communities of samples from these areas were analyzed using 16S rRNA gene amplification. The abundances of Acidobacteria and Ascomycota were lowest (20.66% and 66.55%, respectively) in secondary forests and highest (35.59% and 89.35%, respectively) in sugarcane fields. Differences in microbial abundance across land use types were related to soil pH and total phosphorus. PCoA and ANOSIM demonstrated significant differences in soil bacterial and fungal community structures among the five land use types. Bacterial alpha-diversity showed no significant variation among the different land uses, whereas fungal alpha-diversity exhibited significant differences. Observed Chao1, ACE, and Shannon indices indicated that secondary forests had the highest fungal alpha-diversity. Land use changes also influenced bacterial and fungal co-occurrence networks, with the networks in secondary forests, plantation forests, and orchards being more complex and stable than those in grasslands and sugarcane fields. Key taxa such as Proteobacteria, Planctomycetes, Chloroflexi, Ascomycota, and Basidiomycota were predominantly connected within the co-occurrence networks, highlighting their high functional potential. This study provides insights that can inform more effective land use planning and management strategies in karst depressions, thereby enhancing ecological sustainability and balance.
Exploring the Efficient Irrigation Period for Larix kaempferi Seedlings in Nursery Pots in Greenhouse Conditions Using Optical Measurements
Authors: Jeong Ukhan , Han Seung Hyun , Kim Dohee , Kim Sohyun , Cheong Eun Ju
Published: July 2024

Larix kaempferi is in high demand in Korea due to its value in timber and afforestation. However, it faces challenges in terms of propagation and the collection of physiological information for seedling production. In particular, moisture supply is crucial in seedling production. Therefore, establishing efficient irrigation regimes based on optical measurements is essential. Optical measurement methods are expected to be non-destructive, rapid, and reduce labor consumption in nursery systems. This study applied optical measurements using vegetation indices (VIs), chlorophyll fluorescence (FL) imaging, and thermal (TH) imaging to explore the efficient irrigation period for one-year-old Larix kaempferi seedlings in greenhouse conditions under drought stress and perform rehydration experiments. It was observed that all the seedlings survived without irrigation until day 4 (D4) (soil moisture content: 5.3%). Upon rehydration on D6, 83.33% of the seedlings survived until D14. According to the optical measurement results, the TH parameters, PRI (photochemical reflectance index), and Fm (maximum fluorescence in a dark-adapted state) showed sensitive stress responses in all drought treatment pots on D6. Among them, thermal imaging was found to have the highest potential for addressing limitations and being utilized in the greenhouse. The results of this study are expected to provide foundational data for the development of smart nursery systems for efficient irrigation in the future.
Performance Evaluation of Lightweight Stream Ciphers for Real-Time Video Feed Encryption on ARM Processor
Authors: Khan Mohsin , Dagenborg Håvard , Johansen Dag
Published: July 2024

In resource-intensive Internet of Things applications, Lightweight Stream Ciphers (LWSCs) play a vital role in influencing both the security and performance of the system. Numerous LWSCs have been proposed, each offering certain properties and trade-offs that carefully balance security and performance requirements. This paper presents a comprehensive evaluation of prominent LWSCs, with a focus on their performance and resource consumption, providing insights into efficiency, efficacy, and suitability in the real-world application of resource-intensive live video feed encryption on an ARM processor. The study involves the development of a benchmarking tool designed to evaluate key metrics, including encryption frame rate, throughput, processing cycles, memory footprint, ROM utilization, and energy consumption. In addition, we apply the E−Rank metric, which combines key performance and resource metrics to derive a unified comparative measure for overall software performance.