bluuebunny/crossref_metadata_2025 huggingface dataset:
Architectural 3D-Printed Structures Created Using Artificial Intelligence: A Review of Techniques and Applications
Authors: Živković Milijana , Žujović Maša , Milošević Jelena
Published: September 2023

Artificial Intelligence (AI) and 3D printing (3DP) play considerable roles in what is known as the Fourth Industrial Revolution, by developing data- and machine-intelligence-based integrated production technologies. In architecture, this shift was induced by increasingly complex design requirements, posing important challenges for real-world design implementation, large-scale structure fabrication, and production quality standardization. The study systematically reviews the application of AI techniques in all stages of creating 3D-printed architectural structures and provides a comprehensive image of the development in the field. The research goals are to (1) offer a comprehensive critical analysis of the body of literature; (2) identify and categorize approaches to integrating AI in the production of 3D-printed structures; (3) identify and discuss challenges and opportunities of AI integration in architectural production of 3D-printed structures; and (4) identify research gaps and provide recommendations for future research. The findings indicate that AI is an emerging addition to the 3DP process, mainly transforming it through the real-time adjustment of the design or printing parameters, enhanced printing quality control, or prediction and optimization of key design features. However, the potential of the application of AI in large-scale architectural 3D printing still needs to be explored. Lastly, the study emphasizes the necessity of redefining traditional field boundaries, opening new opportunities for intelligent architectural production.
Integrated Hybrid Tweezer for Particle Trapping with Combined Optical and Acoustic Forces
Authors: Li Sen , Tong Xin , Cai Lutong , Zhang Lin
Published: September 2023

We propose an on-chip integrated hybrid tweezer that can simultaneously apply optical and acoustic forces on particles to control their motions. Multiple potential wells can be formed to trap particles, and the acoustic force generated by an interdigital transducer can balance the optical force induced by an optical waveguide. For example, by driving the waveguide with an optical power of 100 mW and the interdigital transducer with a voltage of 1.466 V, the particle with a refractive index of 1.4 and a diameter of 5 μm (similar to yeast cells) can be stably trapped on the waveguide surface, and its trapping position is controllable by changing the optical power or voltage.
Drying Behaviour of 50 mm Thick Turkey Oak Lumber
Authors: Deaconu Iulia-Teodora , Georgescu Sergiu-Valeriu , Campean Mihaela
Published: September 2023

Turkey oak lumber is difficult to dry, being prone to cracks even at low temperatures. The main objective of the research was to test different drying conditions in order to establish whether air-drying or kiln-drying from a green state was more suitable; if the lumber width had an influence on the drying quality; if prior steaming had a positive effect upon the kiln-drying time and quality; if end cracks could be avoided by protecting the board ends; and what the most appropriate kiln-drying conditions were, with conventional drying. The research results recommend kiln-drying as a better alternative than air-drying in terms of both time and quality, but very mild conditions must be applied. Wide lumber parts cracked both when air-dried and when kiln-dried. All defect-free lumber parts dried accurately, with no deterioration. Steaming had a positive effect upon the drying quality; no steamed part cracked. The end protection of the boards was efficient only in the case of kiln-drying.
Optimization of Median Modified Wiener Filter for Improving Lung Segmentation Performance in Low-Dose Computed Tomography Images
Authors: Lim Sewon , Park Minji , Kim Hajin , Kang Seong-Hyeon , Kim Kyuseok , Lee Youngjin
Published: September 2023

In low-dose computed tomography (LDCT), lung segmentation effectively improves the accuracy of lung cancer diagnosis. However, excessive noise is inevitable in LDCT, which can decrease lung segmentation accuracy. To address this problem, it is necessary to derive an optimized kernel size when using the median modified Wiener filter (MMWF) for noise reduction. Incorrect application of the kernel size can result in inadequate noise removal or blurring, degrading segmentation accuracy. Therefore, various kernel sizes of the MMWF were applied in this study, followed by region-growing-based segmentation and quantitative evaluation. In addition to evaluating the segmentation performance, we conducted a similarity assessment. Our results indicate that the greatest improvement in segmentation performance and similarity was at a kernel size 5 × 5. Compared with the noisy image, the accuracy, F1-score, intersection over union, root mean square error, and peak signal-to-noise ratio using the optimized MMWF were improved by factors of 1.38, 33.20, 64.86, 7.82, and 1.30 times, respectively. In conclusion, we have demonstrated that by applying the MMWF with an appropriate kernel size, the optimization of noise and blur reduction can enhance segmentation performance.
Optimal Insertion Torque for Orthodontic Anchoring Screw Placement: A Comprehensive Review
Authors: Uchida Yasuki , Namura Yasuhiro , Motoyoshi Mitsuru
Published: September 2023

The optimal insertion torque (IT) for orthodontic anchoring screws (OASs) was hypothesized to vary with OAS features and insertion methods. This review examines the indexed English literature, to determine the appropriate IT range for OAS success based on area of insertion and OAS features. Eleven original articles with OAS placement in humans including IT and success rate were selected and were used to evaluate the relationships among IT, success rates, screw design, and placement methods at different sites. The ITs and success rates ranged from 6.0 ± 3.2 to 15.7 ± 2.3 Ncm and from 62.5 to 100.0% in the upper and lower buccal alveolar areas, respectively. For the mid-palatal area, the range was 14.5 ± 1.6 to 25.6 ± 5.5 Ncm and 83.0 to 100.0%, respectively. ITs of 5–12 and 6–14 Ncm were found to be optimal for the commonly used φ1.5–1.7 mm OASs in the upper and lower interproximal areas, respectively. In the mid-palatal suture area, ITs of 11–16 and 20–25 Ncm were considered suitable for tapered φ1.5 mm and φ2.0 mm OASs, respectively. Although identified optimal IT ranges deserve the recommendations, care must be taken to monitor the IT during placement constantly.
Detailed Analysis of the Effects of Viscosity Measurement Errors Caused by Heat Transfer during Continuous Viscosity Measurements under Various Temperature Changes and the Proposed Solution of a Non-Dimensional Parameter Called the Akpek Number
Authors: Akpek Ali
Published: September 2023

It is extremely important to gather the viscosity behavior of fluids accurately for industries and academia. There is no better method than viscosity measurement to detect changes in the specific characteristics of the materials. However, viscosity measurement is indeed a very sensitive process. In nature, fluids are involved in widely various containers, and they are affected by serious temperature deviations. It is a necessity for viscometers to have the capability to obtain accurate data from all types of containers of fluids even in serious temperature variations in order to understand the natural phenomena inside the fluids. Conventional viscometers mainly neglect the effect of sudden temperature deviations inside the fluids, or they need to use very expensive water bath systems that stabilize the temperature around the test fluids, which is not feasible at all. In this research, the effects of non-uniform temperature fields are analysed detailly to confirm that even in extremely limited amounts, serious viscosity and temperature deviations may occur. Experiments were performed in two parts. The first part was conducted using several thermocouples with different test fluids to find out the effects of thermal conduction and convection. For the second part, particle image velocimetry (PIV) was utilized to comprehend the flow movement within the test fluids. It was shown that even for small volumes and even in very controlled environments, an almost 35% viscosity measurement error (VME) occurs. Finally, as a solution to this problem, a new non-dimensional parameter called the Akpek number was proposed. The Akpek number enables the estimation of VMEs in any possible case. VME is a very crucial obstacle that has urgency to be illuminated by researchers and scientists to improve fluid characteristics. The main goal of this research is to illuminate the importance of this problem and offer a potential solution. The final results are supported by experimental data and numerical simulations using OpenFOAM.
Machine Vision-Based Chinese Walnut Shell–Kernel Recognition and Separation
Authors: Zhang Yongcheng , Wang Xingyu , Liu Yang , Li Zhanbiao , Lan Haipeng , Zhang Zhaoguo , Ma Jiale
Published: September 2023

Walnut shell–kernel separation is an essential step in the deep processing of walnut. It is a crucial factor that prevents the increase in the added value and industrial development of walnuts. This study proposes a walnut shell–kernel detection method based on YOLOX deep learning using machine vision and deep-learning technology to address common issues, such as incomplete shell–kernel separation in the current airflow screening, high costs and the low efficiency of manually assisted screening. A dataset was produced using Labelme by acquiring walnut shell and kernel images following shellshock. This dataset was transformed into the COCO dataset format. Next, 110 epochs of training were performed on the network. When the intersection over the union threshold was 0.5, the average precision (AP), the average recall rate (AR), the model size, and floating point operations per second were 96.3%, 84.7%, 99 MB, and 351.9, respectively. Compared with YOLOv3, Faster Region-based Convolutional Neural Network (Faster R-CNN), and Single Shot MultiBox Detector algorithms (SSD), the AP value of the proposed algorithm was increased by 2.1%, 1.3%, and 3.4%, respectively. Similarly, the AR was increased by 10%, 2.3%, and 9%, respectively. Meanwhile, walnut shell–kernel detection was performed under different situations, such as distinct species, supplementary lighting, or shielding conditions. This model exhibits high recognition and positioning precision under different walnut species, supplementary lighting, and shielding conditions. It has high robustness. Moreover, the small size of this model is beneficial for migration applications. This study’s results can provide some technological references to develop faster walnut shell–kernel separation methods.
Location Optimization of Emergency Bell Based on Coverage Analysis for Crime Prevention
Authors: Lee Sun-Woo , Lim Hoi-Jeong , Choi Bo-Gyun
Published: September 2023

Typically, emergency bells are security facilities that, when activated, trigger an alarm and immediately dispatch a police car to prevent crime. However, there currently exists an ambiguity in the criteria for emergency bell installation. Consequently, this study aims to find an optimal location for emergency bells whilst considering several factors like cumulative crime incidents. In particular, we exploited emergency bell location data, data on five major crimes, and the geographic information of administrative dongs (primary division of districts) in this study. Specifically, we performed correlation analysis, principal component analysis, and K-means clustering for exploratory data analysis. To effectively cover all 17,437 crimes, which are not covered by the existing emergency bells in Gwangju metropolitan city from 2018 to 2021, the results from the implementation of the emergency bell location set-covering problem revealed the need for about 6228 emergency bells. More precisely, the emergency bell maximal covering location problem was employed to derive the coverage percentage for 250, 500, 800, 1000, and 1500 emergency bells. The results showed that 2850 emergency bells were required to cover over 80% of crime occurrence coordinates, saving over half of the budget compared with covering them all. Overall, this study is noteworthy in its potential role as a roadmap for the optimal placement of emergency bells for future crime prevention.
An Extended Special Issue on Advances in High-Performance Fiber-Reinforced Concrete
Authors: Tang Chao-Wei
Published: September 2023

Adding fiber to cement concrete can improve the shortcomings of concrete, such as low tensile properties and low ductility, and improve its resistance to permeability and impact [...]
Time-Sensitive and Resource-Aware Concurrent Workflow Scheduling for Edge Computing Platforms Based on Deep Reinforcement Learning
Authors: Zhang Jiaming , Wang Tao , Cheng Lianglun
Published: September 2023

The workflow scheduling on edge computing platforms in industrial scenarios aims to efficiently utilize the computing resources of edge platforms to meet user service requirements. Compared to ordinary task scheduling, tasks in workflow scheduling come with predecessor and successor constraints. The solutions to scheduling problems typically include traditional heuristic methods and modern deep reinforcement learning approaches. For heuristic methods, an increase in constraints complicates the design of scheduling rules, making it challenging to devise suitable algorithms. Additionally, whenever the environment undergoes updates, it necessitates the redesign of the scheduling algorithms. For existing deep reinforcement learning-based scheduling methods, there are often challenges related to training difficulty and computation time. The addition of constraints makes it challenging for neural networks to make decisions while satisfying those constraints. Furthermore, previous methods mainly relied on RNN and its variants to construct neural network models, lacking a computation time advantage. In response to these issues, this paper introduces a novel workflow scheduling method based on reinforcement learning, which utilizes neural networks for direct decision-making. On the one hand, this approach leverages deep reinforcement learning, eliminating the need for researchers to define complex scheduling rules. On the other hand, it separates the parsing of the workflow and constraint handling from the scheduling decisions, allowing the neural network model to focus on learning how to schedule without the necessity of learning how to handle workflow definitions and constraints among sub-tasks. The method optimizes resource utilization and response time, as its objectives and the network are trained using the PPO algorithm combined with Self-Critic, and the parameter transfer strategy is utilized to find the balance point for multi-objective optimization. Leveraging the advantages of reinforcement learning, the network can be trained and tested using randomly generated datasets. The experimental results indicate that the proposed method can generate different scheduling outcomes to meet various scenario requirements without modifying the neural network. Furthermore, when compared to other deep reinforcement learning methods, the proposed approach demonstrates certain advantages in scheduling performance and computation time.
A Diagonal Movement Pattern of Arm Elevation and Depression in Overhead Throwing Athletes: An Exploratory Kinematic Analysis for Clinical Application
Authors: Morais Nuno , Ferreira Joana , Gordo Jéssica , Vilas-Boas João Paulo , Pascoal Augusto G.
Published: September 2023

Assessing scapular position and motion during functional arm movement patterns may add relevant information to the evaluation of the clinical status and athletic performance of overhead sports athletes’ shoulders. This study aimed to examine the three-dimensional scapular kinematics of elite volleyball players with (n = 11) and without scapular dyskinesis (n = 11) in comparison to non-athletes (n = 27). Four distinct arm elevation/depression tasks were assessed: shoulder abduction/adduction, flexion/extension, scaption, and a diagonal movement pattern mimicking throwing (proprioceptive neuromuscular facilitation diagonal 2 for flexion/extension or PNF–D2–flx/ext). Kinematic data was recorded from the spiking/dominant shoulder using an electromagnetic system (FASTRAK, Polhemus Inc., Colchester, VT, USA); MotionMonitor v9 software, Innovative Sports Training, Inc., Chicago, IL, USA). The study compared scapular rotations at 15° intervals of humero-thoracic (HT) angles, ranging from minimum to 120°. Significantly different 3D scapular kinematics were observed between traditional arm motion tasks and PNF–D2 arm motion task (HT angle × task interaction effect, p < 0.001, 0.275 ≤ ηp2 ≤ 0.772). However, when considering the combined influence of phase, HT angle, task, and group factors, no differences were found between groups (phase × HT angle × task × group, p ≥ 0.161, 0.032 ≤ ηp2 ≤ 0.058). The inclusion of a functional arm movement pattern when evaluating scapular position and movement in overhead athletes does not appear to be mandatory. However, these findings are preliminary and highlight the need for more research in this area.
Localised Web Bearing Behaviour of Cold-Formed Austenitic Stainless-Steel Channels: Review of Design Rules and New Insight under Interior Loading
Authors: Yousefi Amir M. , Samali Bijan , Yu Yang
Published: September 2023

Stainless steels are modern high-performance construction materials exhibiting excellent corrosion resistance, recyclability, ductility, and durability which make them appealing to use in the construction industry. However, when used as structural sections, they are subjected to localised failure in the web. This study aims to examine the structural behaviour of cold-formed low-carbon content standard austenitic 304L and 316L stainless steel channels under localised interior bearing loads. The results of 21 tests on unlipped channels with different cross-section sizes and thicknesses are presented. A nonlinear quasi-static Finite Element (FE) model is then developed. The FE model is validated against experimental test results and demonstrated good agreement in terms of bearing strength and failure modes. In addition, the experimental and FE results are used to compare the results against the results predicted in accordance with the American specification SEI/ASCE 8:2002 and European Standard EN 1993-1-4:2006. It is found that the current design equations are unreliable and too unconservative to use for cold-formed austenitic stainless steel unlipped channels, especially when compared to SEI/ASCE 8:2002, as much as 41%.
Multi-Directional Displacement Threshold Energy and Crystal Irradiation Damage Model
Authors: Zhang Tingyu , Zeng Ying , Li Yan-Feng , Huang Hong-Zhong , Gul Niazi Sajawal
Published: September 2023

Subject to intense high-energy particle irradiation, various effects manifest within a material. Specifically, when high-energy particles collide with lattice atoms in a crystal material, a sequence of interactions is set in motion, initiating irradiation effects. Using GaAs solar cells as an example, this study investigates how potential barriers in different directions of atoms in sphalerite structures affect atomic displacement and establishes a probability model for lattice atomic displacement under proton irradiation. By combining Markov chains, changes in displacement threshold energy with different crystal orientations are described, and a damage model for cascading collision relationships that cause irradiation effects is established. Finally, the new model is compared to classical models, and differences in defects caused by proton impacts at several energies on GaAs crystals are simulated.
Complex Background Reconstruction for Novelty Detection
Authors: Zhao Kun , Su Man , An Ran , He Hui , Wang Zhi
Published: September 2023

Novelty detection aims to detect samples from classes different from the training samples (i.e., the normal class). Existing approaches predominantly make the target reconstruction better and choose the appropriate reconstruction error measurement method but ignore the influence of background information on this process. This paper proposes a novel reconstruction network and mutual information Siamese network. The reconstructed network aims to make the distribution of reconstructed samples consistent with that of original samples, intending to reduce background interference in the reconstruction process. After this, we measure the distance between the original and generated images based on a mutual information Siamese network, which extracts more discriminative features to calculate the similarity between the original images and their reconstructed ones. This part of the network uses global context information to improve the detection accuracy. We conduct extreme experiments to evaluate the proposed solution on two challenging public datasets. The experimental results show that the proposed method significantly outperforms the state-of-the-art methods.
Sonorous Touches: Listening to Jean-Luc Nancy’s Transimmanent Rhythms
Authors: Louria Hayon Adi
Published: September 2023

Luigi Russolo’s Intonarumori together with his manifesto L’arte dei rumori (1913) marked a break with the art of clear signification. From here on, noise and dispersed sounds replaced the concept of music reverberating the harmony of the spheres by propelling the quandaries of immanence contingent on palpable resonance performing the differential relational manner of heterogeneous existence. This somatic turn is central to Jean-Luc Nancy’s Listening, where he proposes listening as a tangible fundamental resonance rumbling the corpse sonore. This paper elaborates on the move from the art of music to the plurality of rhythmic worlds. Nancy’s proposition of sonorous existence demonstrates two movements, one that retreats from hearing the Pythagorean musical-arithmetical cosmos exhibited in Robert Fludd’s Monochord, the other plays the singular plural pulsations of dispersed creation performed by Michael Snow’s Tap.
Post-Merge Carbon Footprint Analysis and Sustainability in the NFT Art Market
Authors: Tian Zhongbo
Published: September 2023

The market for non-fungible token (NFT) art is expected to reach USD 44.2 billion in 2021 and increase by 67.57 percent in 2022, revolutionizing the relationship between artists, collectors, and investors. Despite this, concerns regarding the environmental impact of blockchain technology’s high energy consumption persist. NFT art transactions will continue to generate significant carbon emissions after Ethereum’s “Merge” to a Proof-of-Stake (PoS) system in September 2022, rendering many low-carbon solutions obsolete and necessitating further research into post-Merge alternatives. This study identifies solutions in the NFT art market, such as carbon neutrality, lazy minting, alternative consensus mechanisms, Layer 2 solutions and policy interventions. Carbon neutrality is achieved through investments in renewable energy or carbon credits to mitigate emissions generated by NFT art transactions. Lazy minting reduces energy consumption by postponing the creation of NFT art until a buyer is secured. In the NFT art ecosystem, alternative consensus mechanisms such as Proof of Authority (PoA) and Proof of Spacetime (PoST) reduce energy consumption. By offloading transactions from the primary blockchain, Layer 2 solutions enhance scalability and reduce energy consumption. Carbon taxes and energy consumption levies are examples of policy interventions that promote cleaner energy sources in the NFT art market. This study will explore the role of artists, collectors, galleries, and other significant players in encouraging environmentally sustainable practices in the NFT art market. In addition, it will investigate the effect of prominent NFT art sales on carbon emissions and the adoption of eco-friendly alternatives. By integrating and optimizing current carbon reduction strategies, the NFT art market can continue to flourish while reducing its environmental impact. The study emphasizes the significance of implementing a comprehensive strategy that incorporates multiple solutions that are tailored to the specific challenges of the NFT art market.
An Ensemble Model for PM2.5 Concentration Prediction Based on Feature Selection and Two-Layer Clustering Algorithm
Authors: Wu Xiaoxuan , Wen Qiang , Zhu Jun
Published: September 2023

Determining accurate PM2.5 pollution concentrations and understanding their dynamic patterns are crucial for scientifically informed air pollution control strategies. Traditional reliance on linear correlation coefficients for ascertaining PM2.5-related factors only uncovers superficial relationships. Moreover, the invariance of conventional prediction models restricts their accuracy. To enhance the precision of PM2.5 concentration prediction, this study introduces a novel integrated model that leverages feature selection and a clustering algorithm. Comprising three components—feature selection, clustering, and integrated prediction—the model first employs the non-dominated sorting genetic algorithm (NSGA-III) to identify the most impactful features affecting PM2.5 concentration within air pollutants and meteorological factors. This step offers more valuable feature data for subsequent modules. The model then adopts a two-layer clustering method (SOM+K-means) to analyze the multifaceted irregularity within the dataset. Finally, the model establishes the Extreme Learning Machine (ELM) weak learner for each classification, integrating multiple weak learners using the AdaBoost algorithm to obtain a comprehensive prediction model. Through feature correlation enhancement, data irregularity exploration, and model adaptability improvement, the proposed model significantly enhances the overall prediction performance. Data sourced from 12 Beijing-based monitoring sites in 2016 were utilized for an empirical study, and the model’s results were compared with five other predictive models. The outcomes demonstrate that the proposed model significantly heightens prediction accuracy, offering useful insights and potential for broadened application to multifactor correlation concentration prediction methodologies for other pollutants.
The Magnificent Realm of Affine Quantization: Valid Results for Particles, Fields, and Gravity
Authors: Klauder John R. , Fantoni Riccardo
Published: September 2023

Affine quantization is a relatively new procedure, and it can solve many new problems. This essay reviews this new, and novel, procedure for particle problems, as well as those of fields and gravity. New quantization tools, which are extremely close to, and even constructed from, the tools of canonical quantization, are able to fully solve selected problems that using the standard canonical quantization would fail. In particular, improvements can even be found with an affine quantization of fields, as well as gravity.
Innovative Interpolating Polynomial Approach to Fractional Integral Inequalities and Real-World Implementations
Authors: Samraiz Muhammad , Naheed Saima , Gul Ayesha , Rahman Gauhar , Vivas-Cortez Miguel
Published: September 2023

Our paper explores Hermite–Hadamard inequalities through the application of Abel–Gontscharoff Green’s function methodology, which involves interpolating polynomials and Riemann-type generalized fractional integrals. While establishing our main results, we explore new identities. These identities are used to estimate novel findings for functions, such that the second derivative of the functions is monotone, absolutely convex, and concave. A section relating the results of exploration to generalized means and trapezoid formulas is included in the applications. We anticipate that the method presented in this study will inspire further research in this field.
Polymer Electrolytes for Lithium-Sulfur Batteries: Progress and Challenges
Authors: Jia Mingxun , Li Tunan , Yang Daotong , Lu Luhua , Duan Limei , Liu Jinghai , Wu Tong
Published: September 2023

The lithium-sulfur battery has garnered significant attention from both researchers and industry due to its exceptional energy density and capacity. However, the conventional liquid electrolyte poses safety concerns due to its low boiling point, hence, research on liquid electrolytes has gradually shifted towards solid electrolytes. The polymer electrolyte exhibits significant potential for packaging flexible batteries with high energy density owing to its exceptional flexibility and processability, but it also has inherent disadvantages such as poor ionic conductivity, high crystallinity, and lack of active groups. This article critically examines recent literature to explore two types of polymer electrolytes, namely gel polymer electrolyte and solid polymer electrolyte. It analyzes the impact of polymers on the formation of lithium dendrites, addresses the challenges posed by multiple interfaces, and investigates the underlying causes of capacity decay in polymer solid-state batteries. Clarifying the current progress and summarizing the specific challenges encountered by polymer-based electrolytes will significantly contribute to the development of polymer-based lithium-sulfur battery. Finally, the challenges and prospects of certain polymer solid electrolytes in lithium-sulfur battery are examined, thereby facilitating the commercialization of solid polymer electrolytes.
Classification of Lithium-Ion Batteries Based on Impedance Spectrum Features and an Improved K-Means Algorithm
Authors: Zhang Qingping , Tian Jiaqiang , Yan Zhenhua , Li Xiuguang , Pan Tianhong
Published: September 2023

This article presents a classification method that utilizes impedance spectrum features and an enhanced K-means algorithm for Lithium-ion batteries. Additionally, a parameter identification method for the fractional order model is proposed, which is based on the flow direction algorithm (FDA). In order to reduce the dimensionality of battery features, the Pearson correlation coefficient is employed to analyze the correlation between impedance spectrum features. The battery classification is carried out using the improved K-means algorithm, which incorporates the optimization of the initial clustering center using the grey wolf optimization (GWO) algorithm. The experimental results demonstrate the effectiveness of this method in accurately classifying batteries and its high level of accuracy and robustness. Consequently, this method can be relied upon to provide robust support for battery performance evaluation and fault diagnosis.
An Efficient Binary Sand Cat Swarm Optimization for Feature Selection in High-Dimensional Biomedical Data
Authors: Pashaei Elnaz
Published: September 2023

Recent breakthroughs are making a significant contribution to big data in biomedicine which are anticipated to assist in disease diagnosis and patient care management. To obtain relevant information from this data, effective administration and analysis are required. One of the major challenges associated with biomedical data analysis is the so-called “curse of dimensionality”. For this issue, a new version of Binary Sand Cat Swarm Optimization (called PILC-BSCSO), incorporating a pinhole-imaging-based learning strategy and crossover operator, is presented for selecting the most informative features. First, the crossover operator is used to strengthen the search capability of BSCSO. Second, the pinhole-imaging learning strategy is utilized to effectively increase exploration capacity while avoiding premature convergence. The Support Vector Machine (SVM) classifier with a linear kernel is used to assess classification accuracy. The experimental results show that the PILC-BSCSO algorithm beats 11 cutting-edge techniques in terms of classification accuracy and the number of selected features using three public medical datasets. Moreover, PILC-BSCSO achieves a classification accuracy of 100% for colon cancer, which is difficult to classify accurately, based on just 10 genes. A real Liver Hepatocellular Carcinoma (TCGA-HCC) data set was also used to further evaluate the effectiveness of the PILC-BSCSO approach. PILC-BSCSO identifies a subset of five marker genes, including prognostic biomarkers HMMR, CHST4, and COL15A1, that have excellent predictive potential for liver cancer using TCGA data.
Personalized 3D Printed Eye Gear for Microscopic Surgeons Amidst and beyond COVID-19
Authors: Singh Ramandeep , Singh Rajdeep , Suri Ashish
Published: September 2023

The COVID-19 pandemic underscored the need for enhanced protective measures for healthcare workers, particularly surgeons, who face a heightened risk of exposure to infectious aerosols. However, conventional eye protection equipment such as face shields, goggles, or glasses often leads to ergonomic discomfort and a reduced field of view (FOV), impeding surgeons’ ability to perform microsurgical procedures with precision and ease. To address these limitations, this study aimed to develop personalized 3D-printed eye gear for microscopic surgeons based on facial anthropometry data. 3D scanning was employed to obtain facial data from ten neurosurgery residents. Utilizing computer-aided designing, eye gears tailored to the unique facial features of each participant were developed. Finite element analysis-based contact simulation was used to assess the pressure exerted by the eye gear. Multi-material 3D printing was employed to fabricate the personalized eye gear. Participants, while donning the eye gear, engaged in simulation-based micro suturing tasks at various magnifications of the operating microscope, and marked the FOV range. They provided feedback scores (1–10) on the effectiveness of the eye gear through a Likert scale questionnaire (Q1-Q8). Finite element analysis demonstrated uniform strain distribution on the face, indicating that the edges of the customized eye gear fit exactly to the user’s face. The average scores for the questionnaire Q1 to Q8 ranged from 6.8 to 8.5, with an overall mean score of 7.6. This indicates that the developed eye gear was simple to use and did not cause any discomfort. Additionally, the average reduction in the FOV was only 10.93% across the different operating microscope magnifications. These findings highlight eye gear’s potential to alleviate discomfort and enhance precision in microscopic surgeries. Consequently, personalized 3D-printed eye gear offers a promising solution for providing surgeons with a safe environment while preserving the benefits of the operating microscope.
Effects of Clipping an Invasive Plant Species on the Growth of Planted Plants of Two Co-Occurring Species in a Greenhouse Study
Authors: Ye Xiaoqi , Meng Jinliu , Ma Ruixiang , Wu Ming
Published: September 2023

The restoration of native plants in invaded habitats is constrained with the presence of highly competitive exotic species. Aboveground removal, such as clipping or mowing, of invasive plants is required for successful restoration. The effects of clipping an invasive plant species, Solidago canadensis, grown at five densities (1–5 plants per pot), and planting two co-occurring and competitive species, Sesbania cannabina and Imperata cylindrica, on the growth of both the invasive species and the co-occurring species were investigated in a greenhouse experiment. The established S. canadensis suppressed the growth of planted seedlings with 47.8–94.4% reduction in biomass, with stronger effects at higher densities; clipping significantly reduced 97.5–97.4% of biomass of S. canadensis and ameliorated the suppression effects (with only 8.7–52.7% reduction in biomass of the co-occurring plants), irrespective of density. Both the aboveground and belowground part of S. canadensis contributed to its suppression effects on planted co-occurring species. Seed sowing of co-occurring species reduced the belowground growth, but not the underground growth of S. canadensis. S. cannabina appeared to be more effective at reducing the growth of S. canadensis than I. cylindrica. Therefore, clipping together with planting competitive species that can overcome the belowground priority effects of S. canadensis could be a promising strategy for controlling S. canadensis invasion and restoring native plant communities.
LncRNA LNC-565686 Promotes Proliferation of Prostate Cancer by Inhibiting Apoptosis through Stabilizing SND1
Authors: Qin Xuke , Zhong Jiacheng , Wang Lei , Chen Zhiyuan , Liu Xiuheng
Published: September 2023

Long non-coding RNAs (lncRNAs), typically more than 200 nt long, cannot encode proteins, but can regulate gene expression. They play an indispensable role in the occurrence and progression of various cancers. The main purpose of this study is to discuss the role and mechanism of LNC-565686 in prostate cancer. First, we found an increased expression of LNC-565686 in prostate cancer cells using RNA sequencing, which was further verified using qRT-PCR. Then, catRAPID was used to find that LNC-565686 might regulate SND1. Furthermore, a protein half-life experiment was performed to verify that LNC-565686 could stabilize the expression of SND1. In order to further explore the effects of LNC-565686 and SND1 on prostate cancer cells, we knocked down LNC-565686 and SND1 in prostate cancer cells, and verified using CCK8 and flow cytometry and western blot for the detection of apoptosis-related indicators. Collectively, we have found that LNC-565686 can promote the proliferation of prostate cancer cells and inhibit apoptosis by stabilizing the expression of SND1. Therefore, targeting LNC-565686 might be a new treatment for prostate cancer.
Detection of Amyotrophic Lateral Sclerosis (ALS) Comorbidity Trajectories Based on Principal Tree Model Analytics
Authors: Wu Yang-Sheng , Taniar David , Adhinugraha Kiki , Tsai Li-Kai , Pai Tun-Wen
Published: September 2023

The multifaceted nature and swift progression of Amyotrophic Lateral Sclerosis (ALS) pose considerable challenges to our understanding of its evolution and interplay with comorbid conditions. This study seeks to elucidate the temporal dynamics of ALS progression and its interaction with associated diseases. We employed a principal tree-based model to decipher patterns within clinical data derived from a population-based database in Taiwan. The disease progression was portrayed as branched trajectories, each path representing a series of distinct stages. Each stage embodied the cumulative occurrence of co-existing diseases, depicted as nodes on the tree, with edges symbolizing potential transitions between these linked nodes. Our model identified eight distinct ALS patient trajectories, unveiling unique patterns of disease associations at various stages of progression. These patterns may suggest underlying disease mechanisms or risk factors. This research re-conceptualizes ALS progression as a migration through diverse stages, instead of the perspective of a sequence of isolated events. This new approach illuminates patterns of disease association across different progression phases. The insights obtained from this study hold the potential to inform doctors regarding the development of personalized treatment strategies, ultimately enhancing patient prognosis and quality of life.
Therapeutic Potential of Cannabis: A Comprehensive Review of Current and Future Applications
Authors: Leinen Zach J. , Mohan Rahul , Premadasa Lakmini S. , Acharya Arpan , Mohan Mahesh , Byrareddy Siddappa N.
Published: September 2023

Historically, cannabis has been valued for its pain-relieving, anti-inflammatory, and calming properties. Ancient civilizations like the Egyptians, Greeks, and Chinese medicines recognized their therapeutic potential. The discovery of the endocannabinoid system, which interacts with cannabis phytoconstituents, has scientifically explained how cannabis affects the human immune system, including the central nervous system (CNS). This review explores the evolving world of cannabis-based treatments, spotlighting its diverse applications. By researching current research and clinical studies, we probe into how cannabinoids like Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) help to manage conditions ranging from chronic pain, persistent inflammation, cancer, inflammatory bowel disease, and neurological disorders to even viral diseases such as Human Immunodeficiency virus (HIV), SARS-CoV-2. and the emerging monkeypox. The long-term recreational use of cannabis can develop into cannabis use disorder (CUD), and therefore, understanding the factors contributing to the development and maintenance of cannabis addiction, including genetic predisposition, neurobiological mechanisms, and environmental influences, will be timely. Shedding light on the adverse impacts of CUD underscores the importance of early intervention, effective treatment approaches, and public health initiatives to address this complex issue in an evolving landscape of cannabis policies and perceptions.
TAVI in a Heart Transplant Recipient—Rare Case Report and Review of the Literature
Authors: Preda Silvia , Câlmâc Lucian , Nica Claudia , Cacoveanu Mihai , Țigănașu Robert , Badea Aida , Zăman Alexandru , Ciomag (Ianula) Raluca , Nistor Claudiu , Gașpar Bogdan Severus , Iliuță Luminița , Dorobanțu Lucian , Iliescu Vlad Anton , Moldovan Horațiu
Published: September 2023

The global demand for cardiac transplants continues to rise, even with advancements in assistive devices. Currently, the estimated annual mortality rate stands at 3–5%, and patients often face a waiting time of approximately four years on transplant waiting lists. Consequently, many transplant centers have started to consider heart transplants from donors who may be deemed “less than ideal” or marginal. However, the decision to accept such donors must be highly individualized, taking into consideration the risks associated with remaining on the waiting list versus those posed by the transplantation procedure itself. A potential solution lies in the creation of two distinct recipient lists, matched with donor criteria, allowing marginal donors to provide the lifeline that selected patients require. This paper follows a two-step approach. Firstly, it offers an overview of the current state of affairs regarding the topic of transcatheter aortic valve implantation (TAVI) in orthotopic heart transplant (OHT) patients. Secondly, it presents firsthand experience from our clinical center with a comprehensive case presentation of a patient in this unique medical context. The clinical case refers to a 62-year-old male patient, a smoker with a history of hypertension, dyslipidemia, and a prior OHT a decade earlier, who presented with fatigue during minimal physical exertion. The Heart Team carefully reviewed the case, considering the patient’s immunosuppressed status and the heightened risk associated with a repeat intervention. In this instance, transcatheter aortic valve implantation (TAVI) was deemed the appropriate treatment. The TAVI procedure yielded successful results, leading to improved clinical status and enhanced cardiac function. The inclusion of marginal donors has introduced novel challenges related to the utilization of previously diseased marginal organs. TAVI has already demonstrated its efficacy and versatility in treating high-risk patients, including heart transplant recipients. Consequently, it emerges as a vital tool in addressing the unique challenges posed by the inclusion of marginal donors.
Long-Term Radiological Pulmonary Changes in Mechanically Ventilated Patients with Respiratory Failure due to SARS-CoV-2 Infection
Authors: Stoian Mircea , Roman Adina , Boeriu Alina , Onișor Danusia , Bandila Sergio Rareș , Babă Dragoș Florin , Cocuz Iuliu , Niculescu Raluca , Costan Anamaria , Laszlo Sergiu Ștefan , Corău Dragoș , Stoian Adina
Published: September 2023

From the first reports of SARS-CoV-2, at the end of 2019 to the present, the global mortality associated with COVID-19 has reached 6,952,522 deaths as reported by the World Health Organization (WHO). Early intubation and mechanical ventilation can increase the survival rate of critically ill patients. This prospective study was carried out on 885 patients in the ICU of Mureș County Clinical Hospital, Romania. After applying inclusion and exclusion criteria, a total of 54 patients were included. Patients were monitored during hospitalization and at 6-month follow-up. We analyzed the relationship between invasive mechanical ventilation (IMV) and non-invasive mechanical ventilation (NIMV) and radiological changes on thoracic CT scans performed at 6-month follow-up and found no significant association. Regarding paraclinical analysis, there was a statistically significant association between patients grouped by IMV and ferritin level on day 1 of admission (p = 0.034), and between patients grouped by PaO2/FiO2 ratio with metabolic syndrome (p = 0.03) and the level of procalcitonin (p = 0.01). A significant proportion of patients with COVID-19 admitted to the ICU developed pulmonary fibrosis as observed at a 6-month evaluation. Patients with oxygen supplementation or mechanical ventilation require dynamic monitoring and radiological investigations, as there is a possibility of long-term pulmonary fibrosis that requires pharmacological interventions and finding new therapeutic alternatives.
A Bio-Inspired Arched Foot with Individual Toe Joints and Plantar Fascia
Authors: Burgess Stuart , Beeston Alex , Carr Joshua , Siempou Kallia , Simmonds Maya , Zanker Yasmin
Published: September 2023

This paper presents the design and testing of an arched foot with several biomimetic features, including five individual MTP (toe) joints, four individual midfoot joints, and plantar fascia. The creation of a triple-arched foot represents a step further in bio-inspired design compared to other published designs. The arched structure creates flexibility that is similar to human feet with a vertical deflection of up to 12 mm. The individual toe joints enable abduction–adduction in the forefoot and therefore a natural pronation motion. Adult female bone data was obtained and converted into a CAD model to accurately identify the location of bones, joints, and arches. An analytical model is presented that gives the relationship between the vertical stiffness and horizontal stiffness of the longitudinal arches and therefore allows the optimization of stiffness elements. Experimental tests have demonstrated a vertical arch stiffness of 76 N/mm which is similar to adult human feet. The range of movement of the foot is similar to human feet with the following values: dorsi-plantarflexion (28°/37°), inversion-eversion (30°/15°), and abduction–adduction (30°/39°). Tests have also demonstrated a three-point contact with the ground that is similar to human feet.