bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling the Production Process of Lignin Nanoparticles Through Anti-Solvent Precipitation for Properties Prediction
Authors: Girard Victor , Marchal-Heussler Laurent , Chapuis Hubert , Brosse Nicolas , Canilho Nadia , Ziegler-Devin Isabelle
Published: November 2024

Global warming has recently intensified research interest in renewable polymer chemistry, with significant attention directed towards lignin nanoparticle (LNP) synthesis. Despite progress, LNP industrial application faces challenges: (1) reliance on kraft lignin from declining raw biomass processes, (2) sulfur-rich and condensed lignin use, (3) complex lignin macroparticles to LNP conversion, using harmful and toxic solvents, and, above all, (4) lack of control over the LNP production process (i.e., anti-solvent precipitation parameters), resulting in excessive variability in properties. In this work, eco-friendly LNPs with tailored properties were produced from a semi-industrial organosolv process by studying anti-solvent precipitation variables. Using first a parametric and then a Fractional Factorial Design, predictions of LNP sizes and size distribution, as well as zeta-potential, were derived from a model over beech by-products organosolv lignin, depending on initial lignin concentration (x1, g/L), solvent flow rate (x2, mL/min), antisolvent composition (x3, H2O/EtOH v/v), antisolvent ratio (x4, solvent/antisolvent v/v), and antisolvent stirring speed (x5, rpm). This novel chemical engineering approach holds promise for overcoming the challenges inherent in industrial lignin nanoparticle production, thereby accelerating the valorization of lignin biopolymers for high value-added applications such as cosmetics (sunscreen or emulsion) and medicine (encapsulation, nanocarriers), a process currently constrained by significant limitations.
Correction: Ma et al. Perioperative Enteral Immunonutrition Support for the Immune Function and Intestinal Mucosal Barrier in Gastric Cancer Patients Undergoing Gastrectomy: A Prospective Randomized Controlled Study. Nutrients 2023, 15, 4566
Authors: Ma Mingwei , Zheng Zicheng , Zeng Ziyang , Li Jie , Ye Xin , Kang Weiming
Published: November 2024

In the original publication [...]
Quantitative Analysis of Deer Bone Hydroethanolic Extract Using Label-Free Proteomics: Investigating Its Safety and Promoting Effect on Mouse Embryonic Osteoblastic Progenitor Cell Proliferation
Authors: Li Yanlu , Ma Junxia , Jiang Yingshan , Xing Yanchao , He Zhongmei , Chen Weijia , Zhao Yan , Geng Jianan , Zong Ying , Du Rui
Published: November 2024

Background: Deer bone is rich in proteins and free amino acids, offering high nutritional value and benefits such as strengthening bones and antioxidant properties. However, the development and utilization of deer bone resources are limited, and the safety evaluation of health foods is incomplete. Methods: We established a hydrogen ethanol extraction method for deer bone and analyzed the components of the deer bone hydroethanolic extract (DBHE) using liquid chromatography–tandem mass spectrometry (LC-MS/MS), gas chromatography–mass spectrometry (GC-MS), and inductively coupled plasma mass spectrometry (ICP-MS). Results: Using Label-free proteomics technology, we identified 69 proteins and 181 peptides. We also quantified 16 amino acids, 22 fatty acids, and 17 inorganic elements. Finally, we evaluated the safety of DBHE both in vitro and in vivo. The results indicated that DBHE did not exhibit any toxic effects at the doses we tested and can promote the proliferation of mouse embryonic osteoblastic progenitor cells (MC3T3-E1), demonstrating potential efficacy against osteoporosis and arthritis. Conclusions: This study provides a theoretical basis for the quality control, processing, and resource development of deer bone.
Plants with Potential Importance in Supporting the Treatment of Depression: Current Trends, and Research
Authors: Nurzyńska-Wierdak Renata
Published: November 2024

Depression is one of the most common diseases in the world, and it is also the most common mental disorder. Depressive disorders are a real threat not only to individuals, but also to the general population. This disease is a leading cause of disability and inability to work. Due to the numerous side effects of conventional drugs, attention is increasingly being paid to other solutions, including herbal medicines. Many plant species are known for their traditional uses in the treatment of anxiety, insomnia, and depression. The clinically proven effects of adaptogenic raw materials on depression symptoms are probably related to the positive impact of some secondary metabolites (terpenoids, alkaloids, glucosinolates, phenols). Currently, it is emphasized that in many cases the antioxidant and anti-inflammatory properties of plant substances play a protective role at the neurocellular level. Among the medicinal plants analyzed in clinical trials for the treatment of depression, the following seem to be particularly interesting: saffron (Crocus L.), turmeric (Curcuma L.), ginkgo (Ginkgo L.), St. John’s wort (Hypericum L.), and passionflower (Passiflora L.), which have broad and strong biological activity, well-documented history of action and use, and effectiveness in preventing and/or treating anxiety and depression. These plants are still in the sphere of biochemical and phytopharmaceutical research, the results of which are very promising.
Enhancing Cancer Treatment Through Combined Approaches: Photodynamic Therapy in Concert with Other Modalities
Authors: Hong Gyeong , Chang Ji-Eun
Published: November 2024

This review explores the role of photodynamic therapy (PDT) as an adjunctive treatment for cancers, with a focus on its potential to enhance the effects of established therapies like chemotherapy, surgery, and radiotherapy. Given the limitations of conventional cancer treatments, PDT’s ability to improve therapeutic outcomes through combination strategies is examined. In cancers such as lung, breast, cholangiocarcinoma, and cervical, PDT shows promise in enhancing response rates, reducing recurrence, and minimizing adverse effects when used alongside standard modalities. This study highlights current findings on PDT’s mechanisms in complementing chemotherapy, augmenting surgical precision, and enhancing radiotherapeutic effects, thus offering a multi-faceted approach to cancer treatment. Additionally, insights into the clinical application of PDT in these cancers emphasize its potential for reducing tumor resistance and supporting more effective, personalized care. By providing an overview of PDT’s synergistic applications across diverse cancer types, this review underscores its emerging significance in oncology as a tool to address traditional treatment limitations. Ultimately, this review aims to inform and inspire researchers and clinicians seeking to refine and innovate cancer therapy strategies through PDT integration, contributing to the advancement of more effective, synergistic cancer treatments.
Revisiting Inductive Confirmation in Science: A Puzzle and a Solution
Authors: Pelman Alik
Published: November 2024

When an empirical prediction E of hypothesis H is observed to be true, such observation is said to confirm, i.e., support (although not prove) the truth of the hypothesis. But why? What justifies the claim that such evidence supports the hypothesis? The widely accepted answer is that it is justified by induction. More specifically, it is commonly held that the following argument, (1) If H then E; (2) E; (3) Therefore, (probably) H (here referred to as ‘hypothetico-deductive confirmation argument’), is inductively strong. Yet this argument looks nothing like an inductive generalization, i.e., it does not seem inductive in the term’s traditional, enumerative sense. If anything, it has the form of the fallacy of affirming the consequent. This paper aims to solve this puzzle. True, in recent decades, ‘induction’ has been sometimes used more broadly to encompass any non-deductive, i.e., ampliative, argument. Applying Bayesian confirmation theory has famously demonstrated that hypothetico-deductive confirmation is indeed inductive in this broader, ampliative sense. Nonetheless, it will be argued here that, despite appearance, hypothetico-deductive confirmation can also be recast as enumerative induction. Hence, by being enumeratively inductive, the scientific method of hypothetico-deductive confirmation is justified through this traditional, more restrictive type of induction rather than merely by ampliative induction.
Correction: Mu et al. Preparation and Characterization of Corn Straw-Based Graphitized Carbon with Ferric Acetylacetonate as Catalyst. Processes 2023, 11, 2884
Authors: Mu Li , He Peng , Liu Pengfei
Published: November 2024

In the original publication [...]
Photocatalytic Degradation of Four Organic Dyes Present in Water Using ZnO Nanoparticles Synthesized with Green Synthesis Using Ambrosia ambrosioides Leaf and Root Extract
Authors: Medina-Acosta Martin , Chinchillas-Chinchillas Manuel J. , Garrafa-Gálvez Horacio E. , Garcia-Maro Caree A. , Rosas-Casarez Carlos A. , Lugo-Medina Eder , Luque-Morales Priscy A. , Soto-Robles Carlos A.
Published: November 2024

Currently, several organic dyes found in wastewater cause severe contamination problems for flora, fauna, and people in direct contact with them. This research proposes an alternative for the degradation of polluting dyes using ZnO nanoparticles (NPs) synthesized by an ecological route using leaf and root extracts of Ambrosia ambrosioides as a reducing agent (with a weight/volume ratio = 4%). Scanning Electron Microscopy (SEM) was used to determine the morphology, showing an agglomeration of cluster-shaped NPs. Using Transmission Electron Microscopy (TEM), different sizes of NPs ranging from 5 to 56 nm were observed for both synthesized NPs. The composition and structure of the nanomaterial were analyzed by infrared spectroscopy (FT-IR) and X-ray diffraction (XRD), showing as a result that the NPs have a wurtzite-like crystalline structure with crystallite sizes around 32–37 nm for both samples. Additionally, the bandgap of the NPs was calculated using Ultraviolet Visible Spectroscopy (UV–Vis), determining values of 2.82 and 2.70 eV for the NPs synthesized with leaf and root, respectively. Finally, thermogravimetric analysis demonstrated that the nanoparticles contained an organic part after the green synthesis process, with high thermal stability for both samples. Photocatalytic analysis showed that these nanomaterials can degrade four dyes under UV irradiation, reaching 90% degradation for methylene blue (MB), methyl orange (MO) and Congo red (CR) at 60, 100 and 60 min, respectively, while for methyl red (MR) almost 90% degradation was achieved at 140 min of UV irradiation. These results demonstrate that it is effective to use Ambrosia ambrosioides root and leaf extracts as a reducing agent for the formation of ZnO NPs, also evidencing their favorable application in the photocatalytic degradation of these four organic dyes.
Did Wu Zetian Name “卍” as “Wanzi”? A Historical Reassessment
Authors: Lu Lu
Published: November 2024

While scholarly works often attribute the pronunciation of “卍” as “wan” to Empress Wu Zetian in 693, associating it with the meaning “auspicious myriad virtues”, a closer examination of the history of “卍” in Chinese Buddhist translations suggests otherwise. The more accurate transliterations and translations of svastika emerged much later than the term “Wanzi” and had very limited influence. The connection between “卍” and “Wanzi” more likely appeared during the early transmission of Buddhism to China, when people used the accepted cursive form of “萬” to approximate the shape of the svastika symbol. However, as this rationale gradually became obsolete over time, the legend that “Empress Wu Zetian decreed that ‘卍’ be pronounced as ‘wan’” arose during the Song dynasty and has persisted to this day.
Polemic, Diatribe, and Farce: Jaina Postures vis-à-vis Sectarian Others in the Kannada Texts of Nayasēna, Brahmaśiva, and Vṛttavilāsa
Authors: Shanthamurthy Shubha
Published: November 2024

The Deccan in the first half of the second millennium is marked by political and religious ferment. The Cōḻas, Gaṅgas, Rāṣṭrakūṭas, and Cāḷukyas are contesting its mundane territory, while the Śaivas, Jainas, and Vaiṣṇavas are contesting its spiritual geography. Unlike the interactions of the earthly rulers which spill real blood, the bloodshed of the spiritual gurus is merely metaphorical. But, the animosity driving their interactions is no less intense, for survival is at stake for them just as it is for their secular counterparts. In this essay, I explore the Jaina point of view in sectarian contestations between the twelfth and the fourteenth centuries through the texts of three Kannada authors: Dharmāṁṛtam of Nayasēna (1112CE), Samayaparīkṣe of Brahmaśiva (c.1200CE), and Dharmaparīkṣe of Vṛttavilāsa (c.1360CE). My objective is to identify the sectarian ‘other’ that these authors address, dispute with and vilify, and to explore the changing nature of this sectarian ‘other’ and the shifting attitudes of these authors towards their opponents.
Estimating Rainfall Anomalies with IMERG Satellite Data: Access via the IPE Web Application
Authors: Ekpetere Kenneth Okechukwu , Mehta Amita V. , Coll James Matthew , Liang Chen , Onochie Sandra Ogugua , Ekpetere Michael Chinedu
Published: November 2024

This study assesses the possibilities of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) to estimate extreme rainfall anomalies. A web application, the IMERG Precipitation Extractor (IPE), was developed which allows for the querying, visualization, and downloading of time-series satellite precipitation data for points, watersheds, country extents, and digitized areas. The tool supports different temporal resolutions ranging from 30 min to 1 week and facilitates advanced analyses such as anomaly detection and storm tracking, an important component for climate change study. To validate the IMERG precipitation data for anomaly estimation over a 22-year period (2001 to 2022), the Rainfall Anomaly Index (RAI) was calculated and compared with RAI data from 2360 NOAA stations across the conterminous United States (CONUS), considering both dry and wet climate regions. In the dry region, the results showed an average correlation coefficient (CC) of 0.94, a percentage relative bias (PRB) of −22.32%, a root mean square error (RMSE) of 0.96, a mean bias ratio (MBR) of 0.74, a Nash–Sutcliffe Efficiency (NSE) of 0.80, and a Kling–Gupta Efficiency (KGE) of 0.52. In the wet region, the average CC of 0.93, PRB of 24.82%, RMSE of 0.96, MBR of 0.79, NSE of 0.80, and KGE of 0.18 were computed. Median RAI indices from both the IMERG and NOAA indicated an increase in rainfall intensity and frequency since 2010, highlighting growing concerns about climate change. The study suggests that IMERG data can serve as a valuable alternative for modeling extreme rainfall anomalies in data-scarce areas, noting its possibilities, limitations, and uncertainties. The IPE web application also offers a platform for extending research beyond CONUS and advocating for further global climate change studies.
Reconstruction of Fine-Spatial-Resolution FY-3D-Based Vegetation Indices to Achieve Farmland-Scale Winter Wheat Yield Estimation via Fusion with Sentinel-2 Data
Authors: Zhou Xijia , Wang Tao , Zheng Wei , Zhang Mingwei , Wang Yuanyuan
Published: November 2024

The spatial resolution (250–1000 m) of the FY-3D MERSI is too coarse for agricultural monitoring at the farmland scale (20–30 m). To achieve the winter wheat yield (WWY) at the farmland scale, based on FY-3D, a method framework is developed in this work. The enhanced deep convolutional spatiotemporal fusion network (EDCSTFN) was used to perform a spatiotemporal fusion on the 10 day interval FY-3D and Sentinel-2 vegetation indices (VIs), which were compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). In addition, a BP neural network was built to calculate the farmland-scale WWY based on the fused VIs, and the Aqua MODIS gross primary productivity product was used as ancillary data for WWY estimation. The results reveal that both the EDCSTFN and ESTARFM achieve satisfactory precision in the fusion of the Sentinel-2 and FY-3D VIs; however, when the period of spatiotemporal data fusion is relatively long, the EDCSTFN can achieve greater precision than ESTARFM. Finally, the WWY estimation results based on the fused VIs show remarkable correlations with the WWY data at the county scale and provide abundant spatial distribution details about the WWY, displaying great potential for accurate farmland-scale WWY estimations based on reconstructed fine-spatial-temporal-resolution FY-3D data.
Hyperbranched Thermosensitive Polymer-AuNP Composite Probe for Temperature Colorimetric Detection
Authors: Li Huidong , Zhou Yao , Gu Junqi , Zhong Wenjie , Li Xinlong , Liu Xunyong , Qiao Zhuhui , Liu Yi
Published: November 2024

Temperature detection is particularly important in the medical and scientific fields. Although there are various temperature detection methods, most of them focus on broad temperature detection, and basic research in specific fields, especially the detection of subtle temperature changes (32–34 °C) during wound infection, is still insufficient. For this purpose, a novel colorimetric temperature sensing probe is designed in this paper, which can quickly and intuitively respond to small temperature changes within a specific range through color changes. In this paper, hyperbranched polyethyleneimine (HPEI) was modified by isobutyrylation to prepare hyperbranched temperature-sensitive polymer (HPEI-IBAm). And it was combined with gold nanoparticles (AuNPs) prepared by a sodium citrate reduction method to construct an HPEI-IBAm-AuNP colorimetric probe. The probe exhibits excellent stability, even at salt concentrations of up to 12 g/L, thanks to the abundant amino functional groups and the large steric hindrance effect unique to HPEI-IBAm. In particular, the temperature detection range of the probe is precisely locked within 32–34 °C, enabling it to respond quickly and accurately to small temperature changes of only 2 °C. This feature is perfectly suited to the practical needs of temperature detection in infected wounds. The linear fitting coefficient of the temperature response is as high as 0.9929, ensuring the accuracy of the test results. The detection performance of the probe remained highly consistent over 10 cycles, fully proving its excellent reusability and durability. In addition, a flexible colorimetric sensor was prepared by combining the probe with polydimethylsiloxane (PDMS) film. This sensor is capable of rapidly detecting human skin temperature in real time, achieving an accuracy of 99.07% to 100.61%. It can provide a possible solution to the challenges of delayed and difficult temperature detection caused by different body parts and uneven surfaces, among others. This demonstrates its extensive practical value and potential, and it is expected to be further applied in the monitoring of wound infections.
Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions
Authors: Frosolini Andrea , Franz Leonardo , Caragli Valeria , Genovese Elisabetta , de Filippis Cosimo , Marioni Gino
Published: November 2024

The integration of artificial intelligence (AI) into medical disciplines is rapidly transforming healthcare delivery, with audiology being no exception. By synthesizing the existing literature, this review seeks to inform clinicians, researchers, and policymakers about the potential and challenges of integrating AI into audiological practice. The PubMed, Cochrane, and Google Scholar databases were searched for articles published in English from 1990 to 2024 with the following query: “(audiology) AND (“artificial intelligence” OR “machine learning” OR “deep learning”)”. The PRISMA extension for scoping reviews (PRISMA-ScR) was followed. The database research yielded 1359 results, and the selection process led to the inclusion of 104 manuscripts. The integration of AI in audiology has evolved significantly over the succeeding decades, with 87.5% of manuscripts published in the last 4 years. Most types of AI were consistently used for specific purposes, such as logistic regression and other statistical machine learning tools (e.g., support vector machine, multilayer perceptron, random forest, deep belief network, decision tree, k-nearest neighbor, or LASSO) for automated audiometry and clinical predictions; convolutional neural networks for radiological image analysis; and large language models for automatic generation of diagnostic reports. Despite the advances in AI technologies, different ethical and professional challenges are still present, underscoring the need for larger, more diverse data collection and bioethics studies in the field of audiology.
Fiber-Optic Sensor Spectrum Noise Reduction Based on a Generative Adversarial Network
Authors: Lu Yujie , Du Qingbin , Zhang Ruijia , Wang Bo , Liu Zigeng , Tang Qizhe , Dai Pan , Fan Xiangxiang , Huang Chun
Published: November 2024

In the field of fiber-optic sensing, effectively reducing the noise of sensing spectra and achieving a high signal-to-noise ratio (SNR) has consistently been a focal point of research. This study proposes a deep-learning-based denoising method for fiber-optic sensors, which involves pre-processing the sensor spectrum into a 2D image and training with a cycle-consistent generative adversarial network (Cycle-GAN) model. The pre-trained algorithm demonstrates the ability to effectively denoise various spectrum types and noise profiles. This study evaluates the denoising performance of simulated spectra obtained from four different types of fiber-optic sensors: fiber Fabry–Perot interferometer (FPI), regular fiber Bragg grating (FBG), chirped FBG, and FBG pair. Compared to traditional denoising algorithms such as wavelet transform (WT) and empirical mode decomposition (EMD), the proposed method achieves an SNR improvement of up to 13.71 dB, an RMSE that is up to three times smaller, and a minimum correlation coefficient (R2) of no less than 99.70% with the original high-SNR signals. Additionally, the proposed algorithm was tested for multimode noise reduction, demonstrating an excellent linearity in temperature response with a R2 of 99.95% for its linear fitting and 99.74% for the temperature response obtained from single-mode fiber sensors. The proposed denoising approach effectively reduces the impact of various noises from the sensing system, enhancing the practicality of fiber-optic sensing, especially for specialized fiber applications in research and industrial domains.
Gearbox Fault Diagnosis Based on Adaptive Variational Mode Decomposition–Stationary Wavelet Transform and Ensemble Refined Composite Multiscale Fluctuation Dispersion Entropy
Authors: Wang Xiang , Du Yang , Ji Xiaoting
Published: November 2024

Existing gearbox fault diagnosis methods are prone to noise interference and cannot extract comprehensive fault signals, leading to misdiagnosis or missed diagnosis. This paper proposes a method for gearbox fault diagnosis based on adaptive variational mode decomposition–stationary wavelet transform (AVMD-SWT) and ensemble refined composite multiscale fluctuation dispersion entropy (ERCMFDE). Initially, the kurtosis coefficient and autocorrelation coefficient are presented, and the Intrinsic Mode Functions are denoised through the application of AVMD-SWT. Secondly, the coarse-grained processing method of composite multiscale fluctuation dispersion entropy is extended to encompass three additional approaches: first-order central moment, second-order central moment, and third-order central moment. This enables the comprehensive extraction of feature information from the time series, thereby facilitating the formation of an initial hybrid feature set. Subsequently, recursive feature elimination (RFE) is employed for feature selection. Ultimately, the outcomes of the faults diagnoses are derived through the utilization of a Support Vector Machine with a Sparrow Search Algorithm (SSA-SVM), with the actual faults data collection and analysis conducted on an experimental platform for gearbox fault diagnosis. The experiments demonstrate that the method can accurately identify gearbox faults and achieve a high diagnostic accuracy of 98.78%.
Investigation of Device- and Circuit-Level Reliability of Inverse-Mode Silicon-Germanium Heterojunction Bipolar Transistors
Authors: Kim Taeyeong , Kim Garam , Cho Moon-Kyu , Cressler John D. , Han Jaeduk , Song Ickhyun
Published: November 2024

The reliability of inverse-mode silicon-germanium (SiGe) heterojunction bipolar transistors (HBTs) under dc stress and its potential impact on the performance of basic analog amplifiers are investigated. In order to properly reflect the stress effects in various circuit applications, the degradations under three different configurations (active bias, diode connection, and off state) were experimentally characterized with the stress voltages applied up to 3000 s for each case. Based on the changes in the Gummel response, the degradations in device parameters such as current gain (β), transconductance (gm), and base-to-emitter resistance (rπ) were extracted and compared with the forward-mode counterpart. In addition, with the use of a small-signal equivalent model of a SiGe HBT, simple single-stage analog amplifiers were simulated as representative examples and their circuit-level performance metrics including gain and bandwidth were studied to estimate degradation characteristics with accumulated stress. It was found that transimpedance gain decreases and operation bandwidth increases to different levels due to device degradation, whereas a voltage amplifier exhibited much less changes.
CrackNet: A Hybrid Model for Crack Segmentation with Dynamic Loss Function
Authors: Fan Yawen , Hu Zhengkai , Li Qinxin , Sun Yang , Chen Jianxin , Zhou Quan
Published: November 2024

Cracks are a common form of damage in infrastructure, posing significant risks to both personal safety and property. Along with the development of deep learning, visual-based crack automatic detection has been widely studied. However, this task is still challenging due to complex crack topology, noisy backgrounds, unbalanced categories, etc. To address these challenges, this research proposes a novel hybrid network, named CrackNet, which leverages the strengths of both CNN and transformer. On the encoder side, CNNs are employed to extract multi-level local features, while transformers are used to model global dependencies. Additionally, a strip pooling module is introduced to suppress irrelevant regions and enhance the network’s ability to segment narrow and elongated cracks. On the decoder side, an attention-based skip connection strategy and a mixed up-sampling module are implemented to restore detailed information. Furthermore, a joint learning loss combining Dice and cross-entropy with dynamic weighting is proposed to mitigate the effects of severe class imbalance. CrackNet is trained and evaluated on three public crack datasets, and experimental results show that the proposed model outperforms several well-known deep neural networks, with a particularly noticeable improvement in recall rate.
Lightweight Vehicle Detection Based on Mamba_ViT
Authors: Song Ze , Wang Yuhai , Xu Shuobo , Wang Peng , Liu Lele
Published: November 2024

Vehicle detection algorithms are essential for intelligent traffic management and autonomous driving systems. Current vehicle detection algorithms largely rely on deep learning techniques, enabling the automatic extraction of vehicle image features through convolutional neural networks (CNNs). However, in real traffic scenarios, relying only on a single feature extraction unit makes it difficult to fully understand the vehicle information in the traffic scenario, thus affecting the vehicle detection effect. To address this issue, we propose a lightweight vehicle detection algorithm based on Mamba_ViT. First, we introduce a new feature extraction architecture (Mamba_ViT) that separates shallow and deep features and processes them independently to obtain a more complete contextual representation, ensuring comprehensive and accurate feature extraction. Additionally, a multi-scale feature fusion mechanism is employed to enhance the integration of shallow and deep features, leading to the development of a vehicle detection algorithm named Mamba_ViT_YOLO. The experimental results on the UA-DETRAC dataset show that our proposed algorithm improves mAP@50 by 3.2% compared to the latest YOLOv8 algorithm, while using only 60% of the model parameters.
A Lightweight and Efficient Multimodal Feature Fusion Network for Bearing Fault Diagnosis in Industrial Applications
Authors: Mo Chaoquan , Huang Ke , Li Wenhan , Xu Kaibo
Published: November 2024

To address the issues of single-structured feature input channels, insufficient feature learning capabilities in noisy environments, and large model parameter sizes in intelligent diagnostic models for mechanical equipment, a lightweight and efficient multimodal feature fusion convolutional neural network (LEMFN) method is proposed. Compared with existing models, LEMFN captures rich fault features at multiple scales by combining time-domain and frequency-domain signals, thereby enhancing the model’s robustness to noise and improving data adaptability under varying operating conditions. Additionally, the convolutional block attention module (CBAM) and random overlapping sampling technology (ROST) are introduced, and through a feature fusion strategy, the accurate diagnosis of mechanical equipment faults is achieved. Experimental results demonstrate that the proposed method not only possesses high diagnostic accuracy and rapid convergence but also exhibits strong robustness in noisy environments. Finally, a graphical user interface (GUI)-based mechanical equipment fault detection system was developed to promote the practical application of intelligent fault diagnosis in mechanical equipment.
A Review on the Inertial Measurement Unit Array of Microelectromechanical Systems
Authors: Xuan Jiawei , Zhu Ting , Peng Gao , Sun Fayou , Dong Dawei
Published: November 2024

In recent years, microelectromechanical systems (MEMS) technology has developed rapidly, and low precision inertial devices have achieved small volume, light weight, and mass production. Under this background, array technology has emerged to achieve high precision inertial measurement under the premise of low cost. This paper reviews the development of MEMS inertial measurement unit (IMU) array technology. First, the different types of common inertial measurement unit arrays are introduced and the basic principles are explained. Secondly, IMU array’s development status is summarized by analyzing the research results over the years. Then, the key technologies and corresponding development status of IMU array are described, respectively, including error analysis modeling and calibration, data fusion technology, fault detection, and isolation technology. Finally, the characteristics and shortcomings of the past research results are summarized, the future research direction is discussed, and some thoughts are put forward to further improve the accuracy of the IMU array.
A Fabrication Method for Realizing Vertically Aligned Silicon Nanowires Featuring Precise Dimension Control
Authors: Mukherjee Sourav , Elsayed Mohannad Y. , Tawfik Hani H. , El-Gamal Mourad N.
Published: November 2024

Silicon nanowires (SiNWs) have garnered considerable attention in the last few decades owing to their versatile applications. One extremely desirable aspect of fabricating SiNWs is controlling their dimensions and alignment. In addition, strict control of surface roughness or diameter modulation is another key parameter for enhanced performance in applications such as photovoltaics, thermoelectric devices, etc. This study investigates a method of fabricating silicon nanowires using electron beam lithography (EBL) and the deep reactive ion etching (DRIE) Bosch process to achieve precisely controlled fabrication. The fabricated nanowires had a pitch error within 2% of the pitch of the direct writing mask. The maximum error in the average diameter was close to 25%. The simplified two-step method with tight control of the dimensions and surface tunability presents a reliable technique to fabricate vertically aligned SiNWs for some targeted applications.
The Role of Heater Size and Location in Modulating Natural Convection Behavior in Cu-Water Nanofluid-Loaded Square Enclosures
Authors: Alasiri Abdulaziz
Published: November 2024

Enhancing the energy efficiency of thermal systems reduces their consumption, lowers costs, and reduces undesired environmental impact, thus making these systems more sustainable. The current work introduces a passive method for heat transfer enhancement that is carried out using natural convection by nanofluid. This work introduces a computational study of the process of natural convection within a square cavity containing Cu/H2O nanofluid. The cavity wall on the left side undergoes partial isothermal heating, while the opposing side is fully cooled isothermally, with all other boundaries maintained adiabatic. A mathematical model formulated based on a 2-D model was used to provide the solution for the system of governing equations of mass, momentum, and energy conservation, employing the finite element technique. A commercial CFD package is utilized to perform the computational simulation. The present investigation delves into the impact of the Rayleigh number, nanoparticle concentration, heater length, and heater location on the flow field and heat transfer characteristics. The model outcomes were displayed for a wide range of the pertinent parameters as 103 ≤ Ra ≤ 106, 0.25 ≤ lh ≤ 1.0, 0.125 ≤ hc ≤ 0.875, and 0.02 ≤ ϕ ≤ 0.10. Also, correlation equations relating the average Nusselt number to these crucial parameters are derived. These equations are simple and can be applied in practice easily in many fields, such as electric and electronic equipment cooling and thermal management of heat sources. Also, these equations gather all the parameters that affect the heat transfer process. They are shedding light on the intricate interplay between these parameters in the natural convection heat transfer process.
The Mediating Effect of Innovative Performance on the Relationship Between the Use of Information Technology and Organizational Agility in SMEs
Authors: Homayoun Saeid , Salehi Mahdi , ArminKia AmirHossein , Novakovic Vesna
Published: November 2024

The current study has four main objectives. First, it aims to investigate the effect of the relationship between information technology (IT) dimensions (customer relationship management, knowledge management, and human resource management) and innovative practices on organizational agility in small and medium-size companies (SMEs). Second, it seeks to measure the relationship between IT components and innovative performance. Third, it examines the impact of innovative performance on organizational agility. Fourth it explores the mediating role of innovative performance in the relationship between IT and organizational agility. These objectives provide a clear roadmap for the research and guide the analysis and interpretation of the findings. This paper’s statistical population was composed of senior managers in SMEs in Khorsaran Razavi, Iran. The data were collected using standard questionnaires, 172 which were received in 2023 and analyzed using SPSS version 25 and SmartPLS version 4 software. The results demonstrate that using customer relationships, human resources, and knowledge management as three dimensions of IT and innovative performance can enhance organizational agility. Moreover, innovative performance plays a crucial role as a mediator, strengthening the impact of information IT dimensions on organizational agility. These findings underscore the practical relevance for companies operating in a dynamic economic environment. Special attention to organizational agility and practical factors will increase flexibility, speed of response, etc., and, ultimately, companies’ success in this tense economic environment. The innovation of this research is that the three dimensions of IT, including evaluating customer relationship management, human resource management, and knowledge management, is a growing research field in organizational agility. Therefore, this research is vital in empowering SMEs to increase agility. By evaluating the effect of the four variables of knowledge management, customer relationship management, human resource management, and innovative performance on organizational agility in SMEs, on the one hand, this research expands the theoretical literature and, on the other hand, helps such companies.
Sustainable Behavior of Generation Z Tourists’ Water Consumption
Authors: Lima-Vargas Alvaro Enrique , Martínez-González Oscar , Geronimo-Cruz Jessica , Lima-Vargas Suemi
Published: November 2024

Tourism is one of the sectors with the highest demand for fresh water. Indicators suggest that water consumption by tourists is more than double that of residents. This phenomenon can be explained by tourists’ interest in engaging in unique experiences, which frequently entails putting sustainable practices aside. To mitigate the environmental impact of tourism and promote sustainable practices, the state of Quintana Roo has implemented the Sustainable Tourism Master Plan 2030, linking the government and companies to reduce environmental impacts by reducing tourists’ water consumption. Despite these efforts, the use of fresh water by tourists continues to be more than double that of residents. Consequently, tourists’ sustainable behavior is not significantly influenced by external factors, such as awareness campaigns, but depends more on the personal aspects of each individual. Generation Z stands out for being more aware of environmental problems and showing a willingness to modify their behavior towards more sustainable practices. The objective of this study was to explain the factors that influence the sustainable behavior related to water consumption among Generation Z tourists. The methodology employed was cross-sectional, with a quantitative approach, focused on attitude variables, subjective norms, and perceived control as determinants of sustainable behavior. This research is also considered nonexperimental, as there was no direct intervention with the subjects of the study, A non-probabilistic convenience sampling method was used to collect data directly from Generation Z tourists in the state of Quintana Roo, specifically from the destinations of Tulum, Playa del Carmen, Bacalar, and Cancún. These locations were selected due to their prominence as major tourist attractions within the data collection areas, without further differentiation or classification. Data were collected through a face-to-face survey conducted over a period of two months, with a total of 408 respondents participating, of whom 57.8% were male. The results revealed that subjective norms and perceived control influence sustainable water consumption behavior, while attitudes do not have a direct impact. These findings will provide governments and businesses with a basis to design more effective strategies that encourage sustainable behavior among Gen Z tourists, thus offering a starting point for understanding the behavior of other generations.
Effectiveness of AR-Based Formative Peer Assessment on Chinese Writing Performance of Students with Different Cognitive Styles
Authors: Li Caixia , Chen Yuting , Zhou Siwei , Shi Jiandong , Yang Shasha , Li Ming
Published: November 2024

The ability to communicate effectively in writing and produce clear and cohesive text is a necessary skill in both educational settings and the workplace, yet many young students struggle to organize their thoughts and engage in deep thinking. To address these challenges, an augmented reality (AR) application titled “Explore Wild Animals” has been used to help students organize information; however, it may not accommodate different cognitive styles. Integrating formative peer assessment (FPA) strategy into AR-based instruction can enhance knowledge construction and address diverse cognitive needs. This study, conducted from May to June 2023, empirically investigates the effects of FPA in an AR environment on the writing performance of learners with field-independent (FI) and field-dependent (FD) cognitive styles. A total of 89 fifth-grade pupils from China were randomly assigned to two groups: one group adopting FPA in an AR environment (AR-FPA), and the other group adopting FPA in a conventional PowerPoint (PPT) version 2410 environment (FPA). The results of a two-way analysis of covariance (ANCOVA) indicate that the AR-FPA group outperformed the FPA group in writing performance. Specifically, FI learners benefitted more from the AR-FPA approach, while FD learners performed better with the FPA approach. However, multiple linear regression analysis reveals that the peer feedback quality and features showed little to no significant correlation with feedback providers’ writing performance, regardless of cognitive style. These results highlight the effectiveness of integrating AR and FPA in enhancing educational outcomes, providing practical insights for promoting the sustainability of technology-enhanced learning and teaching practices.
Research on Construction and Optimization Paths for Quality Concept Model of Green Supply Chain of Livestock Products
Authors: Zhang Jianjun , Ma Wenlong
Published: November 2024

The quality of the supply chain for livestock products is related to the sustainable development of the livestock industry and national food security. Based on supply chain theory, green supply chain theory, and quality management theory, combined with the characteristics of livestock products and field research in China and Mongolia, a conceptual model of green supply chain quality for livestock products was constructed around two dimensions: livestock product quality and green supply chain service quality. Among them, the factors affecting the quality of livestock products are composed of 10 factors pertaining to breeding, slaughtering and processing, circulation, and sales; the factors affecting the service quality of the green supply chain of livestock products are composed of 16 factors in five links of supply chain services, such as logistics, information flow, capital flow, personnel quality, and public services. The quality of livestock products and the quality of green supply chain services of livestock products are not independent from each other, but show a relationship of mutual influence and mutual restriction. Focusing on the two dimensions of livestock product quality and livestock product green supply chain service quality, an optimization path of livestock product green supply chain quality is proposed. The research findings have certain guiding significance for promoting the high-quality development of green supply chains for cross-border livestock products in China and Mongolia.
Are Climate Change Strategies Effective in Managing Urban Water Resources? The Case of Portugal
Authors: Santos Eleonora
Published: November 2024

This study examines the relationship between climate mitigation, adaptation strategies, and water management practices in Portugal from 2015 to 2021. Utilizing climate and water resource data from 2015 to 2021, including meteorological data (emperature, rainfall), wastewater treatment volumes, and energy efficiency metrics, the data are sourced from national agencies such as IPMA, ERSAR, APA, and Eurostat. The methodology employs correlation analysis to assess the relationships between climate variables (e.g., temperature, rainfall) and water resource indicators (e.g., reclaimed wastewater, energy efficiency). Despite notable reductions in greenhouse gas emissions and improvements in wastewater treatment efficiency, water resource stability remains a complex issue, particularly with regional disparities such as severe droughts in the Algarve. Additionally, the study evaluates the effectiveness of rainwater harvesting systems, reclaimed wastewater, and infiltration facilities, revealing a decline in reclaimed wastewater efficiency despite increased wastewater treatment. Rainwater harvesting systems (RWHSs) offer resilience, but their broader adoption is hindered by high costs and public perception challenges. Key recommendations include the development of resilient infrastructure, enhanced support for reclaimed water use, and increased investment in research to address water management challenges amid climate variability.
Impact of Personal Protective Equipment Use on Stress and Psychological Well-Being Among Firefighters: Systematic Review and Meta-Analysis
Authors: Darabont Doru Costin , Cioca Lucian-Ionel , Bejinariu Costică , Badea Daniel Onuț , Chivu Oana Roxana , Chiș Timur Vasile
Published: November 2024

This study evaluated the individual and psychosocial impacts of personal protective equipment on firefighters by conducting a systematic review and meta-analysis. While PPE is necessary for the protection of firefighters, it can also lead to a number of challenges, which may be both physical and psychological. This assessment incorporates insights from several studies that discuss the factors of stress related to the use of PPE by firefighters. The key aspects of the assessment include the adverse effects of overheating, the restriction of free movements, the inability to speak normally, and various other factors that increase stress. Studies have revealed that the use of PPE increases physical and mental stress levels by 10–25%, which increases overall anxiety by 35% and stress levels by 20–30%. The results suggest that the use of PPE is correlated with heightened levels of both physical and mental stress, leading to a 35% increase in anxiety and a 20–30% increase in stress. This study highlights the need to adopt a multidisciplinary approach to address and reduce the effects of PPE use on stress. Policy directions are aimed at improving PPE design, applying stress prevention programs, and controlling organizational strategies to protect firefighters’ safety and well-being.
(Re)Writing the City from Within: An Exploratory Approach to Sustainable Urban Morphologies from the Dialogue Between Public Space and Public Transport in Barcelona’s Trambesòs
Authors: Reyes-Schade Emilio , Grande-Ayala Carlos , Imam Ayman , M. Gbban Abdulrhman , Summan Maher , Karban Abdullah Saeed , Majrashi Abdulrahman Abdulaziz , Alamoudi Mohammed
Published: November 2024

Research on urban transport and mobility through everyday practices has generated new theoretical and methodological approaches to achieve more sustainable urban morphologies. This paper focuses on the characterization of the process of urban (re)qualification through the binomial of public space–public transport, based on the analysis of Trambesòs tramway in Barcelona, from 2004 to the present. This methodology consisted of an exploratory morphological analysis that identified patterns derived from punctual interventions, articulation axes, and saturation pieces in the urban fabric. The results revealed three main morphological patterns: the configuration of a particular character in each public space intervention associated with the tramway, the morphological improvement and dynamization of the urban fabric, and the transformation of the city’s image through the standardization of accessibility conditions, a key social sustainability indicator. In the discussion, the results are triangulated with the concepts of urban morphology and social processes and compared with quantitative studies, which can serve as a foundation for further in-depth research. In conclusion, the tramway has facilitated a transition from urban discontinuity and segregation to continuity and cohesion, highlighting the impact of the public space–public transport binomial on more sustainable urban morphologies. This contributes to how urban morphological analysis provides new approaches to understanding public transport as an extension of public space and support for urban habitability.