bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
Authors: Zhang Zilin , Deng Yaohua , Liu Xiali , Liao Jige
Published: November 2024

The rotating parts of large and complex equipment are key components that ensure the normal operation of the equipment. Accurate fault diagnosis is crucial for the safe operation of these systems. To simultaneously extract both local and global valuable fault feature information from key components of complex equipment, this study proposes a fault diagnosis network model, named MultiDilatedFormer, which is based on the fusion of transformer and multi-head dilated convolution. The newly designed multi-head dilated convolution module is sequentially integrated into the transformer-encoder architecture, constructing a feature extraction module where the complementary advantages of both components enhance overall performance. Firstly, the sample is expanded into a two-dimensional feature map and then input into the newly designed feature extraction module. Finally, the diagnostic output is performed by the designed patch feature fusion module and classifier module. Additionally, interpretability research is conducted on the proposed model, aiming to understand the decision-making mechanism of the model through visual analysis of the entire decision process. The experimental results on three different datasets indicate that the proposed model achieved high accuracy in fault diagnosis with relatively short data windows. The highest accuracy reached 97.95%, which was up to 10.97% higher than other models. Furthermore, the feasibility of the model is also verified in the actual dataset of the rotating parts of the injection molding machine. The excellent performance of the model on different datasets demonstrates its effectiveness in extracting comprehensive fault feature information and also proves its great potential in practical industrial applications.
Importance of Electrolytes in Exercise Performance and Assessment Methodology After Heat Training: A Narrative Review
Authors: Keefe Marcos S. , Benjamin Courteney L. , Casa Douglas J. , Sekiguchi Yasuki
Published: November 2024

Performing exercise in hot environmental conditions presents athletes with potential negative physiological and perceptual implications. Key constituents, such as fluid and electrolytes, are lost during sweating through the process of cooling the human body. The loss of electrolytes impairs exercise performance. Heat training is one strategy to combat sweat electrolyte loss, with decreased sweat electrolyte concentration being a main sudomotor adaptation. To measure sweat electrolyte concentration, two common assessment methods are typically utilized: whole-body washdown and regional sweat patch measurements. The effects of physiological adaptations and sweat electrolyte assessment methodology have been investigated; however, the importance of methodological differences between sweat electrolyte measurements following heat training has yet to be explored. This review explores the differences between sweat electrolyte measurement techniques following adaptations incurred with heat training. Future research directions are also provided.
YOLOv5DA: An Improved YOLOv5 Model for Posture Detection of Grouped Pigs
Authors: Shi Wenhui , Wang Xiaopin , Li Xuan , Fu Yuhua , Liu Xiaolei , Wang Haiyan
Published: November 2024

Accurate posture detection is the foundation for analyzing animal behavior, which can promote animal welfare. With the development of computer vision, such technology has been widely used in analyzing animal behavior without physical contact. However, computer vision technology for pig posture detection often suffers from problems of missed or false detection due to complex scenarios. To solve the problem, this study proposed a novel object detection model YOLOv5DA, which was based on YOLOv5s and designed for pig posture detection from 2D camera video. Firstly, we established the annotated dataset (7220 images) including the training set (5776 images), validation set (722 images), and test set (722 images). Secondly, an object detection model YOLOv5DA based on YOLOv5s was proposed to recognize pig postures (standing, prone lying, and side lying), which incorporated Mosaic9 data augmentation, deformable convolution, and adaptive spatial feature fusion. The comparative and ablation experiments were conducted to verify the model’s effectiveness and reliability. Finally, we used YOLOv5DA to detect the posture distribution of pigs. The results revealed that the standing posture was more frequent in the morning and afternoon and the side-lying posture was most common at noon. This observation demonstrated that the posture of pigs is influenced by temperature variations. The study demonstrated that YOLOv5DA could accurately identify three postures of standing, prone lying, and side lying with an average precision (AP) of 99.4%, 99.1%, and 99.1%, respectively. Compared with YOLOv5s, YOLOv5DA could effectively handle occlusion while increasing the mean precision (mAP) by 1.7%. Overall, our work provided a highly accurate, effective, low-cost, and non-contact strategy of posture detection in grouped pigs, which can be used to monitor pig behavior and assist in the early prevention of disease.
Changes in Upper Airway Space in a Patient with Pycnodysostosis Following Primary Surgery and Orthodontic Treatment: A 12-Year Follow-Up Case Report
Authors: Staderini Edoardo , Castellano Beatrice , Gioco Gioele , Schiavelli Anna , Gentile Davide
Published: November 2024

Pycnodysostosis (PDS) is an autosomal recessive form of osteosclerotic skeletal dysplasia characterized by craniofacial anomalies, a high-arched palate, and a narrowed pharyngeal space due to mandibular and maxillary deficiencies. These structural anomalies in the upper airway predispose PDS patients to obstructive sleep apnea (OSA). OSA can be managed in PDS patients using either a conservative (non-surgical and multidisciplinary) approach or surgical interventions. Given the craniofacial anomalies associated with PDS, orthodontic treatment aims to address the maxillary transverse deficit and retrognathia. Currently, there is no evidence regarding the impact of orthodontic therapy on OSA levels among PDS patients. This study reports the long-term morphological and functional changes in the upper airway of a PDS patient following adenotonsillectomy and uvulopalatoplasty at an early age (3.6 years) and orthodontic camouflage in late mixed dentition (10 years). Polysomnographies (PSGs), nuclear magnetic resonance (NMR), and lateral cephalograms taken before and after ENT surgery and orthodontic treatment were analyzed. Lateral X-rays were evaluated for changes in the posterior airway space (PAS), while polysomnographies provided the apnea–hypopnea index (AHI). The NMR scans were segmented to measure the perimeter and radius of the upper airway. The findings of this study indicate that improvements following uvulopalatoplasty (UPP) were partial and temporary. Nonetheless, orthodontic therapy and the use of elastics did not appear to influence the PAS or AHI. Future studies with larger cohorts are needed to evaluate the long-term effectiveness of the UPP procedure. This study was conducted in accordance with the CARE guidelines.
Effect of Processing Methods on Amino Acid and Fatty Acid Composition of Parkia biglobosa Seeds
Authors: Osunsanmi Foluso Oluwagbemiga , Ogunyinka Bolajoko Idiat , Oyinloye Babatunji Emmanuel , Opoku Andrew Rowland , Kappo Abidemi Paul
Published: November 2024

Parkia biglobosa (PB) is among the various underexploited nutritious plants. A comparative study was conducted on the effect processing PB has on the amino and fatty acid composition. Fermented (FPB), defatted (DPB), and protein isolates (PI) samples were prepared from Parkia biglobosa seeds using various processing methods, and evaluated using standard analytical protocols. The PI showed the highest significant total non-essential amino acids, essential amino acids, and conditionally essential amino acids when compared with FPB and DPB. In addition, the PI showed the highest significant (p < 0.05) total neutral amino acids, basic amino acids, total aromatic amino acids, and the lowest percentage cystine ratio of total sulphur amino acids and total amino acids in comparison with FPB and DPB. The PI also showed a better-predicted protein efficient ratio, essential amino acid index, biological value, protein content, and nutritional index than FPB and DPB. Furthermore, PI amino acid composition was compared favourably with the reference scores for a whole hen’s egg, preschool child, and provisional scoring pattern. Arginine and histidine values of PI were higher than the recommended values. The FPB (42.29%) showed the highest polyunsaturated fatty acids compared with the DPB (41.94%) and PI (26.7%). The dominant saturated, monounsaturated, and polyunsaturated fatty acids were stearic acid (15.1 DPB), linoleic acid (42.29 DPB), and oleic acid (14.05 PI). The DPB (1.84) showed a better polyunsaturated to saturated (P:S) ratio than FPB (1.74) and PI (0.91). The results revealed that the processing methods improved the relative amino acid composition, whereas the fatty acid composition improved in DPB. Therefore, PI could serve as an alternative in the formulation of complementary foods.
A Study of the Potential of Solidago virgaurea Extract as a Raw Material for Cosmetic Macroemulsions
Authors: Marzec Marta , Kaszczyk Grażyna , Sujka Witold , Nowak Izabela
Published: November 2024

The use of Solidago virgaurea extract as a raw material for cosmetics production fits the eco-tendency prevalent in the cosmetic industry for many years now perfectly. The study reported in this paper included an evaluation of the potential of the above-mentioned plant extract as a natural cosmetic ingredient applied in the form of cosmetic macroemulsions. The physicochemical parameters (pH, viscosity, particle size distribution) and physical stability (multiple light scattering) of the cosmetic formulations containing Solidago virgaurea (1.0 wt.%) were studied. An in vivo study was carried out on a group of 20 female volunteers. The results showed a high compatibility of the tested extract with the other components in the macroemulsion in the form of a serum for the body, which was the formulation containing Sorbitan Stearate as an emulsifier. Analysis of the results revealed a relation between the compatibility of the investigated herbal extract with the components of the cosmetic base and the effectiveness of this extract as an active substance. As a result of an improvement in the application parameters, an over 70% increase in the level of epidermis moisturization was observed along with other beneficial changes in the parameters of skin macrorelief and topography.
Advances in Graphene Field Effect Transistors (FETs) for Amine Neurotransmitter Sensing
Authors: Alimohammadzadeh Elmira , Hedley John
Published: November 2024

Amine neurotransmitters (NTs) are crucial in the central nervous system, and dysregulation in their levels is implicated in a spectrum of neurological disorders. Thus, a precise and timely assessment of their concentrations is critical for early diagnosis and treatment efficacy monitoring. Graphene-based field effect transistors (GFETs) have become a ground-breaking instrument in the detection of these NTs because of their exceptional electrical characteristics and adaptability. This paper summarises the significant advancements in GFET biosensors in amine NT detection and highlights developments in the selectivity, sensitivity, and limit of detection (LOD) attained by selecting various graphene materials and functionalisation approaches.
Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition
Authors: Chen Libang , Lin Jinyan , Bian Qihang , Liu Yikun , Zhou Jianying
Published: November 2024

Imaging through dense fog presents unique challenges, with essential visual information crucial for applications like object detection and recognition, thereby hindering conventional image processing methods. Despite improvements through neural network-based approaches, these techniques falter under extremely low visibility conditions exacerbated by inhomogeneous illumination, which degrades deep learning performance due to inconsistent signal intensities. We introduce in this paper a novel method that adaptively filters background illumination based on Structural Differential and Integral Filtering (SDIF) to enhance only the vital signal information. The grayscale banding is eliminated by incorporating a visual optimization strategy based on image gradients. Maximum Histogram Equalization (MHE) is used to achieve high contrast while maintaining fidelity to the original content. We evaluated our algorithm using data collected from both a fog chamber and outdoor environments and performed comparative analyses with existing methods. Our findings demonstrate that our proposed method significantly enhances signal clarity under extremely low visibility conditions and out-performs existing techniques, offering substantial improvements for deep fog imaging applications.
DanceCaps: Pseudo-Captioning for Dance Videos Using Large Language Models
Authors: Kim Seohyun , Lee Kyogu
Published: November 2024

In recent years, the dance field has been able to create diverse content by leveraging technical advancements such as deep learning models, generating content beyond the unique artistic creations that only humans can create. However, in terms of dance data, there are still a lack of video and label datasets or datasets that contain multiple tags for videos. To address this gap, this paper explores the feasibility of generating dance captions from tags using a pseudo-captioning approach, inspired by the significant improvements large language models (LLMs) have shown in other domains. Various tags are generated from features extracted from videos and audio, and LLMs are then instructed to produce dance captions based on these tags. Captions were generated using both the open dance dataset and Internet dance videos, followed by user evaluations of randomly sampled captions. Participants found the captions effective in describing dance movements, of expert quality, and consistent with video content. Additionally, positive feedback was received on the evaluation of the gap in image extraction and the inclusion of tag data. This paper introduces and validates a novel pseudo-captioning method for generating dance captions using predefined tags, contributing to the expansion of data available for dance research and offering a practical solution to the current lack of datasets in this field.
Exploring Dentists’ Preferences in Selecting Adhesive Systems: A Survey Analysis
Authors: Olariu Iustin , Marian Diana , Veja (Ilyes) Ioana , Flueras Razvan , Popovici Ramona Amina , Pitic (Cot) Dana Emanuela , Stana Horia Ademir , Vaida Luminita Ligia , Lile Ioana Elena
Published: November 2024

Dental adhesives play a crucial role in modern dentistry by enabling the bonding of diverse restorative materials to tooth surfaces. These systems have evolved through seven generations, each characterized by unique chemical compositions and application techniques. The advancements in dental adhesives have significantly impacted restorative dentistry by preserving tooth structure, enhancing aesthetics, and ultimately improving patient outcomes and treatment options. The choice of adhesive system depends on various factors, including the clinical scenario, material compatibility, and dentist preference. Ongoing advancements in adhesive technology continue to enhance treatment outcomes and streamline procedures for dental practitioners. The development of universal adhesives capable of functioning in both etch-and-rinse and self-etch modes has further simplified clinical protocols. This study is an observational cross-sectional study conducted among Romanian dentists. A questionnaire was distributed via email to Romanian dentists, encompassing seven questions that explored the predominant use of etch-and-rinse or self-etch adhesive systems, preferred adhesive types, and commonly encountered challenges in direct restorations. Statistical analyses were conducted using DATAtab version (2024). The study highlighted the varying preferences among Romanian dentists in selecting suitable adhesive agents. Etch-and-rinse (ER) techniques predominated in direct restorations, with universal adhesives being the most frequently used. Further investigation is warranted to delve into the preferences of Romanian dentists regarding the utilization of self-etch (SE), etch-and-rinse (ER), and universal adhesives (UAs).
Effect of Extracted Walnut Pomace Cake Edible Coating and Packaging Type on Quality Parameters of Walnut (Juglans regia L.) Kernels During Long Storage Periods
Authors: Kumar Pradeep , Szalóki-Dorkó Lilla , Szabó-Nótin Beatrix , Kereszturi Julianna , Tormási Judit , Abrankó László , Dalmadi István , Székely Dóra , Máté Mónika
Published: November 2024

Walnuts are highly valued for their rich nutritional content, but their quality degrades during storage due to oxidation and other quality reducing processes. This study investigated the use of bioactive compounds extracted from walnut pomace, a by-product of walnut oil production, to develop an edible coating that extends the shelf life of ‘Milotai 10’ walnut kernels. Walnut kernels were stored for eight months in four different packaging materials (PE, PP, MPP, PLA) and three treatments were used (air or without treatment, nitrogen gas, and walnut pomace extract coating). The effects of these combinations on walnut quality parameters, including polyphenol content, vitamin E, fatty acids, peroxide value, and color, were analyzed. The results show that the combination of metallized polypropylene packaging under nitrogen treatment or with atmospheric air and walnut pomace extract (MPP-A-E+) best preserves walnut quality, making it a promising option for extending shelf-life.
Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
Authors: Kong Xiangzeng , Wang Chuxin , Zhang Lintong , Zhang Wenqing , Chen Shimiao , Weng Haiyong , Hu Nana , Zhang Tingting , Qu Fangfang
Published: November 2024

The electrical penetration graph (EPG) technique is of great significance in elucidating the mechanisms of virus transmission by piercing-sucking insects and crop resistance to these insects. The traditional method of manually processing EPG signals encounters the drawbacks of inefficiency and subjectivity. This study investigated the data augmentation and automatic identification of various EPG signals, including A, B, C, PD, E1, E2, and G, which correspond to distinct behaviors exhibited by the Asian citrus psyllid. Specifically, a data augmentation method based on an improved deep convolutional generative adversarial network (DCGAN) was proposed to address the challenge of insufficient E1 waveforms. A multi-criteria evaluation framework was constructed, leveraging maximum mean discrepancy (MMD) to evaluate the similarity between the generated and real data, and singular value decomposition (SVD) was incorporated to optimize the training iterations of DCGAN and ensure data diversity. Four models, convolutional neural network (CNN), K-nearest neighbors (KNN), decision tree (DT), and support vector machine (SVM), were established based on DCGAN to classify the EPG waveforms. The results showed that the parameter-optimized DCGAN strategy significantly improved the model accuracies by 5.8%, 6.9%, 7.1%, and 7.9% for DT, SVM, KNN, and CNN, respectively. Notably, DCGAN-CNN effectively addressed the skewed distribution of EPG waveforms, achieving an optimal classification accuracy of 94.13%. The multi-criteria optimized DCGAN-CNN model proposed in this study enables reliable augmentation and precise automatic identification of EPG waveforms, holding significant practical implications for understanding psyllid behavior and controlling citrus huanglongbing.
Experimental and Numerical Study in Dynamic Compaction of Weakly-Cohesive Soils
Authors: Debeleac Carmen Nicoleta , Buraga Andrei , Nastac Silviu Marian
Published: November 2024

The rheological modeling of soil–drum interaction in the vibratory compaction process is a complex process. This paper aims to describe the behavior of soil–drum interaction through lumped parameter modeling. The amplitude of the vertical motion is evaluated for dynamic conditions using the rheological models (generalized and advanced Kelvin–Voigt-based models) and compared with the experimental results obtained from weakly cohesive soil compaction. Different modeling approaches are considered, and the results reveal that the properties of the soil as input play a vital role in the accuracy of the modeling.
Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
Authors: Younes Samar , Oloufa Amr
Published: November 2024

The growth of urbanization, population, and economic activity has led to a substantial increase in freight transportation demand, exceeding the capacity of existing infrastructure and creating new challenges across various regions. This has resulted in significant traffic congestion, increased travel times, and higher operational costs for commercial vehicle fleets. Leveraging topological data, such as road networks and traffic patterns, can enable more efficient routing strategies to navigate around congested areas. This study presents a comprehensive approach to truck rerouting strategy by integrating spatial analysis, truck characteristics, traffic conditions, road geometry, and cost–benefit analysis to select alternative routes suitable for commercial vehicle fleets. Incorporating real-time traffic information and predictive analytics, commercial vehicle operators can optimize their routes, reduce fuel consumption, and improve overall delivery efficiency. Three case studies were presented to demonstrate the proposed diversion decision framework. Two scenarios were designed for each case study: a base scenario with no diversion and an optimized scenario with a diversion strategy. The travel times, fuel consumption, and economic impacts between the two scenarios were compared and quantified as a total annual saving of USD 52 million. This approach goes beyond selecting alternative routes and provides decision makers with measurable benefits that justify diversion strategies.
An Optimization Framework for Waste Treatment Center Site Selection Considering Nighttime Light Remote Sensing Data and Waste Production Fluctuations
Authors: Xia Junbao , Liu Yanping , Yang Haozhong , Zhu Guodong
Published: November 2024

As urbanization accelerates, the management of urban solid waste poses increasingly intricate challenges. Traditional urban metrics, such as GDP and per capita consumption rates, have become inadequate for accurately reflecting the realities of waste generation; moreover, the linear correlation between these metrics and waste production is progressively diminishing. Consequently, this study introduces a novel methodology leveraging nighttime light remote sensing data to enhance the precision of urban solid waste production forecasts. By processing remote sensing data to mitigate noise and integrating it with conventional urban datasets, an innovative index system and predictive model were developed. Using Beijing as a case study, the gradient boosting regression algorithm yielded a prediction accuracy of 92%. Furthermore, in light of the substantial costs associated with waste recovery route planning and site selection for treatment facilities, this research further devised a location and distribution framework for waste treatment centers based on high-precision predictions of waste production while employing multi-objective evolutionary algorithms (MOEAs) alongside the non-dominated sorting genetic algorithm II (NSGA-II) for optimization. Distinct from prior studies, this study suggests that service point waste quantities are not fixed values but rather adhere to a normal distribution within specified ranges and thus provides a more realistic simulation of fluctuations in waste production while enhancing both the robustness and predictive accuracy of the model. In conclusion, by incorporating nighttime light remote sensing data along with advanced machine learning techniques, this study markedly improves forecasting accuracy for waste production while offering effective optimization strategies for site selection and recovery route planning—thereby establishing a robust data foundation aimed at refining urban solid waste management systems.
A New Encryption Algorithm Utilizing DNA Subsequence Operations for Color Images
Authors: Mirzajani Saeed , Moafimadani Seyed Shahabeddin , Roohi Majid
Published: November 2024

The computer network has fundamentally transformed modern interactions, enabling the effortless transmission of multimedia data. However, the openness of these networks necessitates heightened attention to the security and confidentiality of multimedia content. Digital images, being a crucial component of multimedia communications, require robust protection measures, as their security has become a global concern. Traditional color image encryption/decryption algorithms, such as DES, IDEA, and AES, are unsuitable for image encryption due to the diverse storage formats of images, highlighting the urgent need for innovative encryption techniques. Chaos-based cryptosystems have emerged as a prominent research focus due to their properties of randomness, high sensitivity to initial conditions, and unpredictability. These algorithms typically operate in two phases: shuffling and replacement. During the shuffling phase, the positions of the pixels are altered using chaotic sequences or matrix transformations, which are simple to implement and enhance encryption. However, since only the pixel positions are modified and not the pixel values, the encrypted image’s histogram remains identical to the original, making it vulnerable to statistical attacks. In the replacement phase, chaotic sequences alter the pixel values. This research introduces a novel encryption technique for color images (RGB type) based on DNA subsequence operations to secure these images, which often contain critical information, from potential cyber-attacks. The suggested method includes two main components: a high-speed permutation process and adaptive diffusion. When implemented in the MATLAB software environment, the approach yielded promising results, such as NPCR values exceeding 98.9% and UACI values at around 32.9%, demonstrating its effectiveness in key cryptographic parameters. Security analyses, including histograms and Chi-square tests, were initially conducted, with passing Chi-square test outcomes for all channels; the correlation coefficient between adjacent pixels was also calculated. Additionally, entropy values were computed, achieving a minimum entropy of 7.0, indicating a high level of randomness. The method was tested on specific images, such as all-black and all-white images, and evaluated for resistance to noise and occlusion attacks. Finally, a comparison of the proposed algorithm’s NPCR and UAC values with those of existing methods demonstrated its superior performance and suitability.
Reprogramming Heritage: An Approach for the Automatization in the Adaptative Reuse of Buildings
Authors: Domènech-Rodríguez Marta , López López David , Nadal Sergi , Queralt Anna , Cornadó Còssima
Published: November 2024

This article introduces a methodology for a novel data-driven computational model aimed at aiding public administrations in managing and evaluating the adaptative reuse of buildings while tackling ecological and digital challenges. Drawing from the 2030 Agenda for Sustainable Development, the study underscores the significance of innovative approaches in harnessing the economic potential of data. Focusing on Barcelona’s Ciutat Vella district, the research selects five historic public buildings for analysis, strategically positioned to spur local entrepreneurship and counteract tourism dominance. Through an extensive literature review, the article identifies a gap in computational models for building adaptative reuse and proposes a methodological framework that integrates data collection, processing, and computational modelling, underscored by GIS technology and open data sources. The proposed methodology for a computational algorithm aims to systematise spatial characteristics, assess programmatic needs, and optimise building usage, while addressing challenges such as data integration and quality assurance. Ultimately, the research presents a pioneering approach to building adaptative reuse, aimed at fostering sustainable urban development and offering replicable insights applicable to similar challenges in other cities.
Assessment of Soil Moisture in Vegetation Regions of Mu Us Sandy Land Using Several Aridity Indicators
Authors: Ren Jie , Zheng Hexiang , Wang Jun , Tong Changfu , Tian Delong , Lu Haiyuan , Liang Dong
Published: November 2024

Drought, a significant calamity in the natural domain, has extensive worldwide repercussions. Drought, primarily characterized by reduced soil moisture (SM), presents a significant risk to both the world environment and human existence. Various drought indicators have been suggested to accurately represent the changing pattern of SM. The study examines various indices related to the Drought Severity Index (DSI), Evaporation Stress Index(ESI), Vegetation Supply Water Index(VSWI), Temperature-Vegetation Dryness Index(TVDI), Temperature Vegetation Precipitation Dryness Index(TVPDI), Vegetation Health Index(VHI), and Temperature Condition Index (TCI). An evaluation was conducted to assess the effectiveness of seven drought indicators, such as DSI, ESI, TVPDI, VSWI, etc., in capturing the changes in SM in Mu Us Sandy Land. The research results indicated that DSI and ESI had the highest accuracy, while TVDI and VSWI showed relatively lower accuracy. However, their smaller fluctuations in the time series demonstrated stronger adaptability to different regions. Additionally, the delayed impact of aridity indices on soil moisture, variable attributes, temperature, and vegetation coverage in sandy land and grassland areas with low, medium, and high coverage all contributed to the effectiveness of the four aridity indices (DSI, ESI, VSWI, and TVPDI) in capturing the dynamics of soil moisture. The primary element that affects the effectiveness of TVDI is the divergence of the relationship curve between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which is a kind of deterioration. This paper presents a very efficient approach for monitoring soil moisture dynamics in dry and semi-arid regions. It also analyzes the patterns of soil moisture changes, offering valuable scientific insights for environmental monitoring and ecological enhancement.
A Study on the Spatiotemporal Dynamics of Land Cover Change and Carbon Storage in the Northern Gulf Economic Zone of Guangxi Based on the InVEST Model
Authors: Huang Shu-Qi , Wu Da-Fang , Pan Yue-Ling , Lin Jin-Yao , Zhou Ping
Published: November 2024

In recent years, the international community has increasingly focused on the “dual carbon” issue, as human-induced land use changes significantly impact ecosystem structure and carbon cycling. This study analyzes land use changes in the economic zone of the northern Gulf of Guangxi from 1980 to 2020, utilizing the InVEST model to simulate spatiotemporal changes in carbon storage and conducting zoning studies through spatial analysis. The findings reveal that ① forest land and arable land dominate the northern Gulf of Guangxi’s land use, with notable changes observed in forest land, unused land, and construction land areas. Forest land and construction land have increased by 1761.5 km2 and 1001.19 km2, respectively, while unused land has decreased by 1881.18 km2 from 2000 to 2020. ② The total carbon storage values in the northern Gulf of Guangxi in 1980, 2000, and 2020 were, respectively, 504.91 × 106/t, 487.29 × 106/t, and 500.31 × 106/t, with the expansion of construction land and conversion of forest land being the main reasons for the decrease in carbon storage. ③ In the northern Gulf of Guangxi, there is a slight upward trend in total carbon storage values over time. Spatially, higher carbon storage values are observed in mountainous and hilly areas at high altitudes, while the central and southern coastal areas exhibit lower carbon storage values. ④ The local spatial autocorrelation results reveal that Pu Bei County exhibits high–high clustering of carbon storage, while He Pu County undergoes a transition from high–low to low–low clustering, and several other administrative areas in Beihai demonstrates low–low clustering. Due to the imperative of economic development, the expansion of urban construction land encroaches upon ecological land, resulting in a decline in carbon storage. Therefore, in the Northern Gulf of Guangxi, it is essential to implement measures such as reforestation and establish ecological protection areas such as forests, grasslands, and wetlands to develop effective carbon sequestration methods and compensate for the carbon loss caused by the expansion of construction land.
Nanoparticle-Mediated Delivery of Deferasirox: A Promising Strategy Against Invasive Aspergillosis
Authors: Peppe Sydney , Farrokhi Moloud , Waite Evan A. , Muhi Mustafa , Matthaiou Efthymia Iliana
Published: November 2024

Background: Invasive aspergillosis (IA) is a deadly fungal lung infection. Antifungal resistance and treatment side effects are major concerns. Iron chelators are vital for IA management, but systemic use can cause side effects. We developed nanoparticles (NPs) to selectively deliver the iron chelator deferasirox (DFX) for IA treatment. Methods: DFX was encapsulated in poly(lactic-co-glycolic acid) (PLGA) NPs using a single emulsion solvent evaporation method. The NPs were characterized by light scattering and electron microscopy. DFX loading efficiency and release were assessed spectrophotometrically. Toxicity was evaluated using SRB, luciferase, and XTT assays. Therapeutic efficacy was tested in an IA mouse model, assessing fungal burden by qPCR and biodistribution via imaging. Results: DFX-NPs had a size of ~50 nm and a charge of ~−30 mV, with a loading efficiency of ~80%. Release kinetics showed DFX release via diffusion and bioerosion. The EC50 of DFX-NPs was significantly lower (p < 0.001) than the free drug, and they were significantly less toxic (p < 0.0001) in mammalian cell cultures. In vivo, NP treatment significantly reduced Af burden (p < 0.05). Conclusion: The designed DFX-NPs effectively target and kill Af with minimal toxicity to mammalian cells. The significant in vivo therapeutic efficacy suggests these NPs could be a safe and effective treatment for IA.
Navigating Alzheimer’s Disease Mouse Models: Age-Related Pathology and Cognitive Deficits
Authors: De Plano Laura Maria , Saitta Alessandra , Oddo Salvatore , Caccamo Antonella
Published: November 2024

Since the mid-1990s, scientists have been generating mouse models of Alzheimer’s disease to elucidate key mechanisms underlying the onset and progression of the disease and aid in developing potential therapeutic approaches. The first successful mouse model of Alzheimer’s disease was reported in 1995 with the generation of the PDAPP mice, which were obtained by the overexpression of gene coding for the amyloid precursor protein (APP). Since then, scientists have used different approaches to develop other APP overexpression mice, mice overexpressing tau, or a combination of them. More recently, Saito and colleagues generated a mouse model by knocking in mutations associated with familial Alzheimer’s disease into the APP gene. In this review, we will describe the most used animal models and provide a practical guide for the disease’s age of onset and progression. We believe that this guide will be valuable for the planning and experimental design of studies utilizing these mouse models.
Biophysical Analysis of EGCG’s Protective Effects on Camptothecin-Induced Oxidative Stress in Bone-like Cancer Cells Using Electric Cell-Substrate Impedance Sensing (ECIS)
Authors: Nguyen Ailinh , Joseph Eugene , Clement Peace , Alvarez Gisela , Crogman Horace T.
Published: November 2024

Various medical treatments aim to counteract the impact of oxidants on mammalian cells. One such antioxidant is Epigallocatechin-3-gallate (EGCG), an active ingredient in green tea, which has demonstrated protective effects against cellular oxidants like camptothecin (CAMPT). This study examines how EGCG mitigates CAMPT’s effects on UMR cells, focusing on cell proliferation and biophysical parameters. UMR cells were treated with different CAMPT concentrations and incubated for 72 h. Subsequently, cell proliferation and viability were assessed. In a separate experiment, UMR cells were co-treated with CAMPT and varying EGCG concentrations to evaluate EGCG’s ability to mitigate CAMPT’s oxidative effect. Electric Cell–Substrate Impedance Sensing (ECIS) technology was also used to assess the biophysical parameters of CAMPT-treated UMR cells, including cell monolayer resistance, cell spreading, and cell attachment. The results showed a concentration-dependent decrease in cell proliferation for CAMPT-treated UMR cells. However, co-treatment with EGCG reversed CAMPT’s oxidative effects in a concentration-dependent manner. ECIS technology revealed a decrease in biophysical parameters when UMR cells were treated with CAMPT alone. Statistical analysis indicated significant differences with p-values < 0.05. This study suggests that EGCG effectively protects UMR cells from oxidative stress and highlights its potential role in mitigating oxidative stress in mammalian cells. Additionally, the use of ECIS technology validates its application in corroborating the biological effects of CAMPT and EGCG on UMR cells.
Lateral Flow Assay for Preeclampsia Screening Using DNA Hairpins and Surface-Enhanced Raman-Active Nanoprobes Targeting hsa-miR-17-5p
Authors: Ng Ka Wai , Jaitpal Siddhant , Vu Ngoc Nhu , San Juan Angela Michelle T. , Tripathy Sayantan , Kodam Rohit Sai , Bastiray Abhishek , Cho Jae-Hyun , Choudhury Mahua , Coté Gerard L. , Mabbott Samuel
Published: November 2024

Preeclampsia (PE) is a serious complication that poses risks to both mothers and their children. This condition is typically asymptomatic until the second or even third trimester, which can lead to poor outcomes and can be costly. Detection is particularly challenging in low- and middle-income countries, where a lack of centralized testing facilities coincides with high rates of PE-related maternal mortality. Variations in the levels of hsa-miR-17-5p have been identified as constituting a potential early indicator for distinguishing between individuals with PE and those without PE during the first trimester. Thus, developing a screening test to measure hsa-miR-17-5p levels would not only facilitate rapid detection in the early stages of pregnancy but also help democratize testing globally. Here, we present a proof-of-principle lateral-flow assay (LFA) designed to measure hsa-miR-17-5p levels using DNA-hairpin recognition elements for enhanced specificity and nanoprobes for sensitive surface-enhanced resonance Raman scattering (SERS) signal transduction. The theoretical limit of detection for hsa-miR-17-5p was 3.84 × 10−4 pg/µL using SERS.
Development of a Recombinase Polymerase Amplification-Coupled CRISPR/Cas12a Platform for Rapid Detection of Antimicrobial-Resistant Genes in Carbapenem-Resistant Enterobacterales
Authors: Yang Ji Woo , Kim Heesu , Hyeon Lee-Sang , Yoo Jung Sik , Kang Sangrim
Published: November 2024

The worldwide spread of carbapenemase-producing Enterobacterales (CPE) represents a significant threat owing to the high mortality and morbidity rates. Traditional diagnostic methods are often too slow and complex for rapid point-of-care testing. Therefore, we developed a recombinase polymerase amplification (RPA)-coupled CRISPR/Cas12a system (RCCS), a rapid, accurate, and simple diagnostic platform for detecting antimicrobial-resistant genes. The RCCS detected carbapenemase genes (blaKPC and blaNDM) within 50 min, including 10 min for DNA extraction and 30–40 min for RCCS reaction (a 20 min RPA reaction with a 10–20-min CRISPR/Cas12a assay). Fluorescence signals obtained from the RCCS platform were visualized using lateral-flow test strips (LFSs) and real-time and endpoint fluorescence. The LFS clearly displayed test lines while detecting carbapenemase genes. Furthermore, the RCCS platform demonstrated high sensitivity by successfully detecting blaKPC and blaNDM at the attomolar and picomolar levels, respectively. The accuracy of the RCCS platform was validated with clinical isolates of Klebsiella pneumoniae and Escherichia coli; a 100% detection accuracy was achieved, which has not been reported when using conventional PCR. Overall, these findings indicate that the RCCS platform is a powerful tool for rapid and reliable detection of carbapenemase-encoding genes, with significant potential for implementation in point-of-care settings and resource-limited environments.
The Influence of Building Surroundings and Glass Cover in Bird Collisions
Authors: Lopes Amanda Cristina , Rocha Gustavo Oliveira Cotta , de Oliveira Passos Marcela Fortes , Barçante Luciana , de Azevedo Cristiano Schetini
Published: November 2024

The characteristics of building surroundings can influence the number of bird deaths caused by collisions with glass structures. Thus, this study investigated whether the number of trees, the distance to the nearest tree, the number of fruit trees, and the glass area influenced the number of bird collisions on a university campus in Brazil from March 2017 (breeding and non-breeding seasons) to January 2018 (breeding season). Twenty-four birds died due to collisions with the windows in the one-year sampling. Among the factors evaluated, the number of trees and the area of the glass predicted the number of deaths from collisions. The greater the number of trees and the glass area, the greater the number of bird collisions. This suggests that the more vegetation there is near windows, the more birds are attracted, and the less visible the glass barrier becomes, possibly due to the appearance of trees in reflections or scenes viewed through the glass, making it difficult for birds to distinguish the real landscape from the reflected environment. If large expanses of glass are placed on buildings near vegetation, including trees, more bird collisions will occur. Thus, to reduce bird collisions, building designs should reduce the amount of glass used on building exteriors near vegetation and ensure the glass is treated with visual markers.
The Impact of Relationships Within Combat Units on Post-Deployment Suicide Risk
Authors: Sher Leo
Published: November 2024

Multiple deployment factors may affect suicidality in combat veterans. The relationships between combat deployments and suicidality are complex and not completely understood. Studies of stress in the military and psychological effects of military actions are mostly focused on stressors relating to combat operations. However, many studies suggest that interactions within combat units affect post-deployment psychiatric conditions, suicidal ideation, and behavior. The goal of this article is to review and discuss how relationships within combat units may influence post-deployment suicide risk. Studies of the relationships within combat units are generally focused on two aspects: unit cohesion and harassment/abuse. Considerable evidence suggests that service members who report strong unit cohesion have a lower risk of post-deployment psychiatric disorders and suicidal behavior. Studies examining deployment sexual and non-sexual harassment and abuse have found that combat veterans who experience harassment and abuse during deployment are at heightened post-deployment suicide risk. Sound post-deployment social support and the efficient treatment of psychiatric disorders may mitigate the suicide risk associated with adverse relationships within combat units. Improvements in units’ cohesion and the prevention of harassment/abuse during a military deployment are necessary to reduce post-deployment psychiatric pathology, including suicidal behavior.
Chinese Doctoral Students Involved in Interdisciplinary Learning Score Higher on Scientific Creativity: The Roles of Teamwork Skills and Collaborative Behaviors
Authors: Chen Shuzhen , Ma Lichao , Ma Yinqi
Published: November 2024

Despite the growing recognition of the value of interdisciplinary learning in doctoral education, there is still a gap in the literature supporting the relationship between it and doctoral students’ scientific creativity in China. Based on a questionnaire survey of 457 doctoral students from the humanities and social sciences on the Chinese Mainland, this study adopted structural equation modeling to examine the relationships among interdisciplinary learning, teamwork skills, collaborative behaviors, and scientific creativity. The results indicated that there was a weak positive correlation between interdisciplinary learning and the scientific creativity of doctoral students. Teamwork skills mediated the relationship between interdisciplinary learning and creativity, while the mediating effect of collaborative behaviors did not hold. Moreover, the relationship between interdisciplinary learning and creativity can also be mediated by the sequential mediation of teamwork skills and collaborative behaviors.
Dynamic Testing and Finite Element Model Adjustment of the Ancient Wooden Structure Under Traffic Excitation
Authors: Wang Xin , Meng Zhaobo , Lv Xiangming , Wei Guoqiang
Published: November 2024

In situ dynamic testing is conducted to study the dynamic characteristics of the wooden structure of the North House main hall. The velocity response signals on the measurement points are obtained and analyzed using the self-interaction spectral method and stochastic subspace method, yielding natural frequencies, mode shapes, and damping ratios. This study reveals that the natural frequencies and damping ratios are highly consistent between the two methods. Therefore, to eliminate errors, the average of the results from both modal identification methods is taken as the final measured modal parameters of the structure. The natural frequencies of the first and second order in the X direction were 2.097 Hz and 3.845 Hz and in the Y direction were 3.955 Hz and 5.701 Hz. The modal frequency in the Y direction of the structure exceeds that in the X direction. Concurrently, a three-dimensional finite element model was established using ANSYS 2021R1, considering the semi-rigid properties of mortise–tenon connections, and validated based on in situ dynamic testing. The sensitivity analysis indicates adjustments to parameters such as beam–column elastic modulus, tenon–mortise joint stiffness, and roof mass for finite element model refinement. Modal parameter calculations from the corrected finite element model closely approximate the measured modal results, with maximum errors of 9.41% for the first two frequencies, both within 10% of the measured resonant frequencies. The adjusted finite element model closely matches the experimental results, serving as a benchmark model for the wooden structure of North House main hall. The validation confirms the rationality of the benchmark finite element model, providing valuable insights into ancient timber structures along transportation routes.