bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing Deep Learning Algorithms for Effective Chicken Tracking through Image Processing
Authors: Mehdizadeh Saman Abdanan , Siriani Allan Lincoln Rodrigues , Pereira Danilo Florentino
Published: August 2024

Identifying bird numbers in hostile environments, such as poultry facilities, presents significant challenges. The complexity of these environments demands robust and adaptive algorithmic approaches for the accurate detection and tracking of birds over time, ensuring reliable data analysis. This study aims to enhance methodologies for automated chicken identification in videos, addressing the dynamic and non-standardized nature of poultry farming environments. The YOLOv8n model was chosen for chicken detection due to its high portability. The developed algorithm promptly identifies and labels chickens as they appear in the image. The process is illustrated in two parallel flowcharts, emphasizing different aspects of image processing and behavioral analysis. False regions such as the chickens’ heads and tails are excluded to calculate the body area more accurately. The following three scenarios were tested with the newly modified deep-learning algorithm: (1) reappearing chicken with temporary invisibility; (2) multiple missing chickens with object occlusion; and (3) multiple missing chickens with coalescing chickens. This results in a precise measure of the chickens’ size and shape, with the YOLO model achieving an accuracy above 0.98 and a loss of less than 0.1. In all scenarios, the modified algorithm improved accuracy in maintaining chicken identification, enabling the simultaneous tracking of several chickens with respective error rates of 0, 0.007, and 0.017. Morphological identification, based on features extracted from each chicken, proved to be an effective strategy for enhancing tracking accuracy.
Nutrient Utilization and Gut Microbiota Composition in Giant Pandas of Different Age Groups
Authors: Wang Chengdong , Deng Wenwen , Huang Zhi , Li Caiwu , Wei Rongping , Zhu Yan , Wu Kai , Li Chengyao , Deng Linhua , Wei Ming , Chen Xuemei , Li Desheng
Published: August 2024

Proper feeding and nutrition are vital for maintaining the health of giant pandas (GPs), yet the impact of dietary changes and gut microbiota on their nutrient utilization remains unclear. To address these uncertainties, we investigated nutrient intake and apparent digestibility, as well as gut microbiota composition across different age groups of giant pandas: sub-adults (SGPs), adults (AGPs), and geriatrics (GGPs). Our findings revealed notable shifts in dietary patterns from SGPs to GGPs. As they aged, significantly more bamboo shoots and less bamboo were consumed. Consequently, GGPs showed significantly reduced crude fiber (CF) intake and digestibility, while crude protein (CP) did not alter significantly. In addition, 16S rRNA microbial sequencing results showed that unidentified_Enterobacteriaceae and Streptococcus were the dominant genera among all age groups. The relative abundance of the genus Enterococcus in GGPs was significantly higher than that in SGPs and AGPs (p < 0.05). Overall, our results indicated the importance of bamboo shoots as a major source of protein in GGPs’ diet, which can effectively compensate for the certain nutritional loss caused by the reduction in bamboo intake. Age-related changes in bacterial abundance have an effect on specific nutrient apparent digestibility in the gut of GPs. The data presented in this study serve as a useful reference for nutritional management in different ages of GPs under healthy conditions.
Optimal Investment Strategy for DC Pension Plan with Stochastic Salary and Value at Risk Constraint in Stochastic Volatility Model
Authors: Liu Zilan , Zhang Huanying , Wang Yijun , Huang Ya
Published: August 2024

This paper studies the optimal asset allocation problem of a defined contribution (DC) pension plan with a stochastic salary and value under a constraint within a stochastic volatility model. It is assumed that the financial market contains a risk-free asset and a risky asset whose price process satisfies the Stein–Stein stochastic volatility model. To comply with regulatory standards and offer a risk management tool, we integrate the dynamic versions of Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and worst-case CVaR (wcCVaR) constraints into the DC pension fund management model. The salary is assumed to be stochastic and characterized by geometric Brownian motion. In the dynamic setting, a CVaR/wcCVaR constraint is equivalent to a VaR constraint under a higher confidence level. By using the Lagrange multiplier method and the dynamic programming method to maximize the constant absolute risk aversion (CARA) utility of terminal wealth, we obtain closed-form expressions of optimal investment strategies with and without a VaR constraint. Several numerical examples are provided to illustrate the impact of a dynamic VaR/CVaR/wcCVaR constraint and other parameters on the optimal strategy.
Tuberculosis Infection and Comorbidities: A Public Health Issue in Baja California, Mexico
Authors: Hernández-Acevedo Gerson Ney , González-Vázquez Raquel , Reyes-Pavón Diana , Torres-Maravilla Edgar
Published: August 2024

According to the World Health Organization (WHO), tuberculosis (TB) remains a significant global health challenge, with approximately 10 million new cases and 1.4 million deaths reported in 2020. TB disproportionately affects low- and middle-income countries, where factors such as migrant population, malnutrition, type 2 diabetes, human immunodeficiency virus (HIV) co-infection, and COVID-19 exacerbate its impact. TB also leads to substantial economic losses due to decreased productivity and high healthcare costs. Despite advances in treatments, TB remains a major public health issue, particularly in poorer regions. In Mexico, TB is considered a moderate-incidence disease, with higher prevalence in border states, mainly due to population displacements. Effective TB control requires collaboration between Mexico and the United States of America given the high cross-border human movement, like in the Baja California State that reported predominantly pulmonary TB cases. Effective management of TB involves rapid diagnosis and identification of antibiotic resistance. Techniques such as PCR, high-resolution computed tomography (HRCT), and/or Xpert MTB/RIF have enhanced diagnostic accuracy. Future perspectives about TB management focus on developing new drugs and vaccines to combat drug-resistant strains, and the comorbidities associated, which must be addressed to reinforce of health public programs.
Enhancing Dermatological Diagnostics with EfficientNet: A Deep Learning Approach
Authors: Manole Ionela , Butacu Alexandra-Irina , Bejan Raluca Nicoleta , Tiplica George-Sorin
Published: August 2024

Background: Despite recent advancements, medical technology has not yet reached its peak. Precision medicine is growing rapidly, thanks to machine learning breakthroughs powered by increased computational capabilities. This article explores a deep learning application for computer-aided diagnosis in dermatology. Methods: Using a custom model based on EfficientNetB3 and deep learning, we propose an approach for skin lesion classification that offers superior results with smaller, cheaper, and faster inference times compared to other models. The skin images dataset used for this research includes 8222 files selected from the authors’ collection and the ISIC 2019 archive, covering six dermatological conditions. Results: The model achieved 95.4% validation accuracy in four categories—melanoma, basal cell carcinoma, benign keratosis-like lesions, and melanocytic nevi—using an average of 1600 images per category. Adding two categories with fewer images (about 700 each)—squamous cell carcinoma and actinic keratoses—reduced the validation accuracy to 88.8%. The model maintained accuracy on new clinical test images taken under the same conditions as the training dataset. Conclusions: The custom model demonstrated excellent performance on the diverse skin lesions dataset, with significant potential for further enhancements.
Effects of Exercise on the Inter-Session Accuracy of sEMG-Based Hand Gesture Recognition
Authors: Liu Xiangyu , Dai Chenyun , Liu Jionghui , Yuan Yangyang
Published: August 2024

Surface electromyography (sEMG) is commonly used as an interface in human–machine interaction systems due to their high signal-to-noise ratio and easy acquisition. It can intuitively reflect motion intentions of users, thus is widely applied in gesture recognition systems. However, wearable sEMG-based gesture recognition systems are susceptible to changes in environmental noise, electrode placement, and physiological characteristics. This could result in significant performance degradation of the model in inter-session scenarios, bringing a poor experience to users. Currently, for noise from environmental changes and electrode shifting from wearing variety, numerous studies have proposed various data-augmentation methods and highly generalized networks to improve inter-session gesture recognition accuracy. However, few studies have considered the impact of individual physiological states. In this study, we assumed that user exercise could cause changes in muscle conditions, leading to variations in sEMG features and subsequently affecting the recognition accuracy of model. To verify our hypothesis, we collected sEMG data from 12 participants performing the same gesture tasks before and after exercise, and then used Linear Discriminant Analysis (LDA) for gesture classification. For the non-exercise group, the inter-session accuracy declined only by 2.86%, whereas that of the exercise group decreased by 13.53%. This finding proves that exercise is indeed a critical factor contributing to the decline in inter-session model performance.
Parametric Modeling and Column Grid Analysis of the Sakyamuni Pagoda at Fogong Temple: Insights into the Yingxian Wooden Pagoda
Authors: Wang Daoru , Li Stephanie Yanqiu , Liu Ruguan , Hu Jianxin , Wang Ang
Published: August 2024

The Sakyamuni Pagoda of Fogong Temple, also known as the Yingxian Wooden Pagoda or the Wooden Pagoda of Ying County, is China’s oldest and tallest wooden pagoda. This pagoda was constructed in 1056 and has faced many challenges, including earthquakes, wars, and mistreatment. However, it is currently in danger of potential collapse due to severe structural challenges. Preserving this historic monument requires interdisciplinary collaboration across architectural history, materials science, and engineering. This paper proposes the development of a parametric model to build the pagoda that can be used for future preservation efforts. While not precisely reflecting the pagoda’s current condition, with the changes in inputs, the geometries of the structural members can be updated in real time, which offers adaptability crucial for preservation efforts. With the understanding of the original construction techniques, including its leaning column systems, the model not only helps preservation but also reveals the creativity of the architects or the craftsmen at the time. The 3D model, which includes more than 32,000 pieces along with the parametric files that can generate the column grid and dougongs, has been published along with this paper, and those files are available in the Data Availability Statement. In summary, the full digital model presented alongside this paper, unavailable elsewhere to the general public, serves as a practical preservation tool that can also be used for raising awareness for this cultural heritage site.
Social License to Operate for NIMBY Infrastructures: The Mechanism of the Four Components of Procedural Justice
Authors: Feng Diyang , Liu Yong , Ge Yujia
Published: August 2024

The key to obtaining the Social License to Operate (SLO) for businesses in the not-in-my-backyard (NIMBY) infrastructure projects is the justice of the process. The study constructs a model to analyze the effect of the four components of procedural justice (procedural regulations and the behavior and attitudes of the government and construction enterprises) on the SLO. A large-scale questionnaire survey is conducted to assess the role and the extent of the influence of these four components in obtaining the SLO for NIMBY infrastructures. The results of Structural Equation Modeling (SEM) and mediation effects tests show that procedural justice significantly influences the SLO for NIMBY infrastructure projects. All four components significantly improve community trust and social acceptance of the project. The construction enterprise’s informal treatment of the public’s behavior and attitudes has the largest influence, whereas the government’s formal treatment of the public’s behavior and attitudes has the least influence. The findings provide theoretical and practical guidance for improving the SLO for NIMBY infrastructure projects and promoting the sustainable development of these projects.
Data-Driven Predictive Modeling of Steel Slag Concrete Strength for Sustainable Construction
Authors: Albostami Asad S. , Al-Hamd Rwayda Kh. S. , Al-Matwari Ali Ammar
Published: August 2024

Conventional concrete causes significant environmental problems, including resource depletion, high CO2 emissions, and high energy consumption. Steel slag aggregate (SSA), a by-product of the steelmaking industry, offers a sustainable alternative due to its environmental benefits and improved mechanical properties. This study examined the predictive power of four modeling techniques—Gene Expression Programming (GEP), an Artificial Neural Network (ANN), Random Forest Regression (RFR), and Gradient Boosting (GB)—to predict the compressive strength (CS) of SSA concrete. Using 367 datasets from the literature, six input variables (cement, water, granulated furnace slag, superplasticizer, coarse aggregate, fine aggregate, and age) were utilized to predict compressive strength. The models’ performance was evaluated using statistical measures such as the mean absolute error (MAE), root mean squared error (RMSE), mean values, and coefficient of determination (R2). Results indicated that the GB model consistently outperformed RFR, GEP, and the ANN, achieving the highest R2 values of 0.99 and 0.96 for the training and testing dataset, respectively, followed by RFR with R2 values of 0.97 (training) and 0.93 (testing), GEP with R2 values of 0.85 (training) and 0.87 (testing), and ANN with R2 values of 0.61 (training) and 0.82 (testing). Additionally, the GB model had the lowest MAE values of 0.79 MPa (training) and 2.61 MPa (testing) and RMSE values of 1.90 MPa (training) and 3.95 MPa (testing). This research aims to advance predictive modeling in sustainable construction through analysis and well-defined conclusions.
Development of an Optimization-Based Budget Allocation Model for Seismic Strengthening Based on Seismic Risk Assessment
Authors: Kim Seokjung , Kim Jongkwan , Song Homin , Yoo Mintaek
Published: August 2024

This study presents a technology used for the prediction of economic losses to facilities in a given area during an earthquake, thereby enabling the efficient application of performance-based maintenance and seismic strengthening. We also propose an algorithm for the establishment of a reinforcement plan that minimizes earthquake-induced economic losses within a constrained budget. The algorithm incorporates fragility functions from prior research and utilizes an optimization technique for budget allocation, leveraging the target damage ratio concept and constrained optimization. Based on the fragility curve, the probability of occurrence for each damage state for a specific PGA value and the damage rate for each damage state are calculated. From these values, the expected damage ratio (EDR) is estimated. An optimization-based budget allocation algorithm is developed to find the elements that would result in the lowest damage rate for a limited cost. To validate the applicability of the model, we created a hypothetical city with a 30 km × 30 km area containing bridges, embankments, and buildings. The estimated pre- and post-reinforcement damage was assessed in two earthquake scenarios, allowing us to test the effectiveness of the optimization-based budget allocation model in reducing damage. These results suggest that the proposed model offers a viable strategy for efficient seismic strengthening within budgetary constraints.
Health-Oriented Evaluation and Optimization of Urban Square Space Elderly Suitability: A Case Study of Yiyang City Center
Authors: Li Yuhua , Tang Hui
Published: August 2024

Urban squares are the main place for older adults’ daily activities. The spatial layout of the urban square is not only related to the construction of a healthy city and the equitable distribution of public resources, but also of great significance in enhancing the sense of gain, security, and happiness of older adults. This essay takes “health needs of older adults” as the perspective, and constructs a multi-dimensional evaluation index system of urban square space for elderly suitability in terms of physiological, psychological, and social health. It also takes six urban squares in Yiyang city center as an example and analyzes and evaluates them using GIS network analysis and the entropy weight method. The results of the study show that (1) Yiyang city center squares show the distribution characteristics of “dense in the middle and scattered around”, and the spatial distribution is not balanced, which leads to a small service area. (2) The overall intervention value of urban square space is mainly distributed between 0.291 and 0.710, with an average value of 0.481, and the overall intervention effect of the square is good. The reasons for differences in health interventions for older adults in urban square spaces centered on six aspects: the quality of the square space, the variety and convenience of travel lines, the tidiness of the environment, physical activities, safety, and social interaction. Based on this, this essay proposes three optimization strategies for the renewal of urban squares, the construction of characteristic squares, and the reconstruction of the urban square space system. The aim is to provide a reference for the future design and renewal of the elderly suitability of urban squares, so as to promote the construction of a healthy city.
Frost Resistance Differences of Concrete in Frequent Natural Freeze–Thaw versus Standard Rapid Method
Authors: Deng Changzhong , Yu Lei , Wang Haoyu , Liu Zhaolei , Fan Dongmei
Published: August 2024

In order to find the anti-freezing durability differences between concrete in the frequent natural freeze–thaw conditions in the northwest of Sichuan Province, China, and concrete in the rapid freeze–thaw conditions of the standard rapid method, the typical temperature and humidity of the northwest of Sichuan Province were simulated. The results showed that the average number of freeze–thaw cycles in the northwest of this province can reach up to 150 per year. The relative dynamic modulus of C30 ordinary concrete, which is 100% pre-saturated, still remained above 90% after 450 cycles in simulated environments. However, during the rapid freeze–thaw test, even the C30 air-entrained concrete failed after 425 cycles. Compared to the saturation degree of concrete itself, the continuous replenishment of external moisture during freeze–thaw cycles is a key factor affecting the frost resistance of concrete. Rapid freeze–thaw reduces the number of the most probable pore sizes in ordinary concrete, and the pore size distribution curve tends to flatten. The reduction rate of the surface porosity of air-entrained concrete before and after rapid freeze–thaw is only about one third of that of ordinary concrete.
Tensegrity FlaxSeat: Exploring the Application of Unidirectional Natural Fiber Biocomposite Profiles in a Tensegrity Configuration as a Concept for Architectural Applications
Authors: Renner Markus , Spyridonos Evgenia , Dahy Hanaa
Published: August 2024

Material selection is crucial for advancing sustainability in the building sector. While composites have become popular, biocomposites play a pivotal role in raising awareness of materials deriving from biomass resources. This study presents a new linear biocomposite profile, fabricated using pultrusion technology, a continuous process for producing endless fiber-reinforced composites with consistent cross-sections. The developed profiles are made from flax fibers and a plant-based resin. This paper focuses on the application of these profiles in tensegrity systems, which combine compression and tension elements to achieve equilibrium. In this study, the biocomposite profiles were used as compression elements, leveraging their properties. The methods include geometrical development using physical and digital models to optimize the geometry based on material properties and dimensions. A parametric algorithm including physics simulations was developed for this purpose. Further investigations explore material options for tension members and connections, as well as assembly processes. The results include several prototypes on different scales. Initially, the basic tensegrity principle was built and explored. The lessons learned were applied in a final prototype of 1.5 m on a furniture scale, specifically a chair, integrating a hanging membrane serving as a seat. This structure validates the developed system, proving the feasibility of employing biocomposite profiles in tensegrity configurations. Furthermore, considerations for scaling up the systems to an architectural level are discussed, highlighting the potential to enhance sustainability through the use of renewable and eco-friendly building materials, while promoting tensegrity design applications.
Durability Requirements for Reinforced Concrete Structures Placed in a Hostile Tropical Coastal Environment
Authors: Castañeda Valdés Abel , Corvo Pérez Francisco , Pech Pech Ildefonso , Marrero Águila Rigoberto , Bastidas-Arteaga Emilio
Published: August 2024

In this work, a series of durability requirements are proposed for the construction of long-service-life reinforced concrete (RC) structures in a coastal environment with extreme atmospheric corrosivity. RC specimens were exposed in a coastal outdoor site in Cuba for three years. Carbon steel corrosion evaluation revealed an annual average atmospheric corrosion rate over the maximum limit established (ISO 9223:2012) for extreme (CX) atmospheric corrosivity. The service life of the RC structures, considered as the sum of the time-to-corrosion-initiation and time-to-corrosion-induced concrete cracking, was determined as a function of durability requirements. The most important durability requirements to achieve a long service life (>70 years) in RC structures subjected to a CX corrosivity category were defined as follows: water/cement ratio, compressive strength, percentage of effective capillary porosity, and concrete cover thickness. Under these hostile environments, the expansion of the corrosion products formed on the reinforcement steel and the induced cracking of the concrete could be attributed partially to the formation of the akaganeite phase in reinforcement steel, which revealed a different morphology compared to the akaganeite typically formed in bare carbon steels.
Seeding as a Decisive Tool for Increasing Space-Time-Yields in the Preparation of High-Quality Cu/ZnO/ZrO2 Catalysts
Authors: Guse David , Warmuth Lucas , Herfet Moritz , Adolf Katharina , Zevaco Thomas A. , Pitter Stephan , Kind Matthias
Published: August 2024

Aging is one of the key steps in the preparation of highly active Cu/ZnO-based catalysts for use in the production of methanol. If certain pH and temperature specifications are met, an initially amorphous precipitate transforms into the crystalline precursor phase of zincian malachite, which is characterized by a periodic arrangement of Cu and Zn atoms and has proven advantageous for the quality of the final catalyst. However, aging generally takes between 30 min and multiple hours until the desired phase transformation is completed. With our study, we show that aging can be significantly accelerated by seeding the freshly precipitated suspension with already aged zincian malachite crystals: the necessary aging time was reduced by 41% for seeding mass fractions as low as 3 wt.% and from 83 min to less than 2 min for 30 wt.% seeds. No negative influence of seeding on the phase composition, specific surface area, molar metal ratios, or the morphology of the aged precursor could be identified. Consequently, the catalyst performance in the synthesis of methanol from CO2, as well as from a CO/CO2 mixture, was identical to a catalyst from an unseeded preparation and showed small advantages compared to a commercial sample. Thus, we conclude that seeding is a vital tool to accelerate the preparation of all Cu/Zn-based catalysts while maintaining product quality, presumably also on an industrial scale.
Moiré Superlattices of Two-Dimensional Materials toward Catalysis
Authors: Wang Longlu , Wang Kun , Zheng Weihao
Published: August 2024

In recent years, there has been a surge in twistronics research, uncovering diverse emergent properties in twisted two-dimensional (2D) layered materials. Vertically stacking these materials with slight azimuthal deviation or lattice mismatch creates moiré superlattices, optimizing the structure and energy band and leading to numerous quantum phenomena with applications in electronics, optoelectronics, photonics, and twistronics. Recently, the superior (opto)electronic properties of these moiré superlattices have shown potential in catalysis, providing a platform to manipulate catalytic activity by adjusting twist angles. Despite their potential to revolutionize 2D catalysts, their application in catalysis is limited to simple reactions, and the mechanisms behind their catalytic performance remain unclear. Therefore, a comprehensive perspective on recent studies is needed to understand their catalytic effects for future research.
The Proangiogenic Effects of Melanoma-Derived Ectosomes Are Mediated by αvβ5 Integrin Rather than αvβ3 Integrin
Authors: Surman Magdalena , Wilczak Magdalena , Bzowska Małgorzata , Tylko Grzegorz , Przybyło Małgorzata
Published: August 2024

Ectosomes are carriers of proangiogenic factors during cancer progression. This study investigated whether the proangiogenic effect exerted by melanoma-derived ectosomes on recipient endothelial cells is mediated by ectosomal αvβ3 and αvβ5 integrins. Ectosomes were isolated from the conditioned culture media of four melanoma cell lines and melanocytes. Changes in gene and protein expression of αvβ3 and αvβ5 integrins, as well as VEGF and TNF-α were assessed in ectosome-treated endothelial cells. To confirm the functional involvement of ectosomal integrins in functional tests (Alamar Blue, wound healing and tube formation assays), ectosomes were also pretreated with anti-integrin antibodies and integrin-blocking peptides echistatin and cilengitide. Melanoma-derived ectosomes induced changes in the expression of αvβ3 and αvβ5 integrins in recipient endothelial cells, leading to increased viability, migratory properties, and tube formation potential. The extent of proangiogenic stimulation varied depending on the types of cells releasing ectosomes and the recipient cells. The use of anti-integrin antibodies and integrin-blocking peptides revealed a more significant role for the αvβ5 integrin/VEGF than the αvβ3 integrin/TNF-α pathway in the interactions between ectosomes and endothelial cells. The study demonstrated the functional role of ectosomal αvβ3 and αvβ5 integrins. It also provided a baseline understanding of ectosome-mediated αvβ3 integrin/TNF-α and αvβ5 integrin/VEGF signaling in angiogenesis.
Grid Forming Inverter as an Advanced Smart Inverter for Augmented Ancillary Services in a Low Inertia and a Weak Grid System Towards Grid Modernization
Authors: Rangarajan Shriram S. , Collins E. Randolph , Senjyu Tomonobu
Published: August 2024

Grid dynamics and control mechanisms have improved as smart grids have used more inverter-based renewable energy resources (IBRs). Modern converter technologies try to improve converters’ capacities to compensate for grid assistance, but their inertia still makes them heavily dependent on synchronous generators (SGs). Grid-following (GFL) converters ensure grid reliability. As RES penetration increases, the GFL converter efficiency falls, limiting integration and causing stability difficulties in low-inertia systems. A full review of grid converter technologies, grid codes, and controller mechanisms is needed to determine the current and future needs. A more advanced converter is needed for integration with more renewable energy sources (RESs) and to support weak grids without SGs and with low inertia. Grid-forming (GFM) inverters could change the electrical business by addressing these difficulties. GFM technology is used in hybrid, solar photovoltaic (PV), battery energy storage systems (BESSs), and wind energy systems to improve these energy systems and grid stability. GFM inverters based on BESSs are becoming important internationally. Research on GFM controllers is new, but the early results suggest they could boost the power grid’s efficiency. GFM inverters, sophisticated smart inverters, help maintain a reliable grid, energy storage, and renewable power generation. Although papers in the literature have compared GFM and GFL, none of them have examined them in terms of their performance in a low-SCR system. This paper shows how GFM outperforms GFL in low-inertia and weak grid systems in the form of a review. In addition, a suitable comparison of the results considering the performance of GFM and GFL in a system with varying SCRs has been depicted in the form of simulation using PSCAD/EMTDC for the first time.
Classification of High-Resolution Chest CT Scan Images Using Adaptive Fourier Neural Operators for COVID-19 Diagnosis
Authors: Gurrala Anusha , Arora Krishan , Sharma Himanshu , Qamar Shamimul , Roy Ajay , Chakraborty Somenath
Published: August 2024

In the pursuit of advancing COVID-19 diagnosis through imaging, this paper introduces a novel approach utilizing adaptive Fourier neural operators (AFNO) for the analysis of high-resolution computed tomography (HRCT) chest images. The study population comprised 395 patients with 181,106 labeled high-resolution COVID-19 CT images from the HRCTCov19 dataset, categorized into four classes: ground glass opacity (GGO), crazy paving, air space consolidation, and negative for COVID-19. The methods included image preprocessing, involving resizing and normalization, followed by the application of the AFNO model, which enables efficient token mixing in the Fourier domain independent of input resolution. The model was trained using the Adam optimizer with a learning rate of 1 × 10−⁴ and evaluated using metrics such as accuracy, precision, recall, and F1 score. The results demonstrate AFNO’s superior performance in few-shot segmentation tasks over traditional self-attention mechanisms, achieving an overall accuracy of 94%. Specifically, the model showed high precision and recall for the GGO and negative classes, indicating its robustness and effectiveness. This research has significant implications for the development of AI-powered diagnostic tools, particularly in environments with limited access to high-quality imaging data and those where computational efficiency is critical. Our findings suggest that AFNO could serve as a powerful model for analyzing HRCT images, potentially leading to improved diagnosis and understanding of COVID-19, representing a critical step in combating the pandemic.
A Randomized Trial Comparing Concurrent versus Sequential Radiation and Endocrine Therapy in Early-Stage, Hormone-Responsive Breast Cancer
Authors: McGee Sharon F. , Clemons Mark , Pond Gregory , Caudrelier Jean-Michel , Liu Michelle , Alzahrani Mashari Jemaan , Ng Terry L. , Awan Arif A. , Sehdev Sandeep , Hilton John , Savard Marie-France , Fallowfield Lesley , Kumar Vikaash , Freedman Orit , Vandermeer Lisa , Hutton Brian , Bourque Jean-Marc
Published: August 2024

Concerns exist regarding increased toxicities, including endocrine therapy toxicity, with concurrent radiation and endocrine therapy in early breast cancer (EBC). We present a pragmatic, randomized trial comparing concurrent versus sequential endocrine and radiotherapy in hormone-responsive EBC. In this multicenter trial, patients were randomized to receive adjuvant endocrine therapy concurrent with, or sequential to, radiotherapy. The primary outcome was change in endocrine therapy toxicity from baseline to 3 months post radiotherapy using the Functional Assessment of Cancer Therapy–Endocrine Symptom (FACT-ES) score. From September 2019 to January 2021, 133 patients were randomized to concurrent endocrine and radiotherapy, and 127 to sequential treatment. Most patients were post-menopausal (72.7%, 189/260) with stage 1 disease (65.8%, 171/260). Tamoxifen was the endocrine therapy of choice for 69.6% (181/260) of patients, and an aromatase inhibitor for the remainder. The median total radiation dose and fractions were 40.1 Gray (range 26–50) and 15 fractions (range 5–25), respectively. For the primary outcome of change in endocrine therapy toxicity per FACT-ES scores from baseline to 3 months post radiotherapy, no significant difference was found between the groups (median [range] = −4.9 (−82, 38.8) for concurrent and −5.1 (−42, 40) for sequential, p = 0.87). This is the first trial to investigate the impact of concurrent versus sequential adjuvant endocrine and radiotherapy on endocrine therapy-related toxicities. The findings provide further support to allow the optimal timing of radiation and endocrine therapy to be tailored for the individual patient.
The Recent Environmental History, Attempted Restoration and Future Prospects of a Challenged Lobelia Pond in Northeastern Belgium
Authors: Denys Luc , Packet Jo , Leyssen An , Vanderhaeghe Floris
Published: August 2024

Softwater ponds with Lobelia dortmanna (EU habitat type 3110) represent the rarest aquatic habitat in Belgium. As in many other European countries, its unfavourable conservation status necessitates restoration according to the EU Habitats Directive, which is compromised by a range of pressures and faces increasing social–economic opposition. To explore appropriate goals and remaining obstacles for its ecological rehabilitation, we investigated the environmental history of a pond, formerly renowned for the occurrence of this habitat. We complemented monitoring data with information inferred from diatoms analysed from old samples, herbarium specimens and surface sediments, vegetation records, physical–chemical analyses and additional observations. This indicated almost circumneutral, slightly buffered and nutrient-poor conditions for the first decades of the 20th century. Deposition of atmospheric pollutants caused gradual acidification from the early 1940s, intensifying into mineral-acidic conditions by the 1970s. More recently, a period of alkalinisation and eutrophication followed despite some restoration efforts. We discuss these changes in the contexts of general setting, external pressures and internal processes. Reflecting upon the prospects for restoring the pond’s emblematic biodiversity, management implications for this and other softwater sites dealing with similar problems are discussed. A new combination in the diatom genus Iconella is proposed.
Origin of Type II tRNA Variable Loops, Aminoacyl-tRNA Synthetase Allostery from Distal Determinants, and Diversification of Life
Authors: Lei Lei , Burton Zachary
Published: August 2024

The three 31 nucleotide minihelix tRNA evolution theorem describes the evolution of type I and type II tRNAs to the last nucleotide. In databases, type I and type II tRNA V loops (V for variable) were improperly aligned, but alignment based on the theorem is accurate. Type II tRNA V arms were a 3′-acceptor stem (initially CCGCCGC) ligated to a 5′-acceptor stem (initially GCGGCGG). The type II V arm evolved to form a stem–loop–stem. In Archaea, tRNALeu and tRNASer are type II. In Bacteria, tRNALeu, tRNASer, and tRNATyr are type II. The trajectory of the type II V arm is determined by the number of unpaired bases just 5′ of the Levitt base (Vmax). For Archaea, tRNALeu has two unpaired bases, and tRNASer has one unpaired base. For Bacteria, tRNATyr has two unpaired bases, tRNALeu has one unpaired base, and tRNASer has zero unpaired bases. Thus, the number of synonymous type II tRNA sets is limited by the possible trajectory set points of the arm. From the analysis of aminoacyl-tRNA synthetase structures, contacts to type II V arms appear to adjust allosteric tension communicated primarily via tRNA to aminoacylating and editing active sites. To enhance allostery, it appears that type II V arm end loop contacts may tend to evolve to V arm stem contacts.
Improved Nonlinear Model Predictive Control Based Fast Trajectory Tracking for a Quadrotor Unmanned Aerial Vehicle
Authors: Ma Hongyue , Gao Yufeng , Yang Yongsheng , Xu Shoulin
Published: August 2024

This article studies a nonlinear model predictive control (NMPC) scheme for the trajectory tracking efficiency of a quadcopter UAV. A cost function is first proposed that incorporates weighted increments of control forces in each direction, followed by a weighted summation. Furthermore, a contraction constraint for the cost function is introduced based on the numerical convergence of the system for the sampling period of the UAV control force. Then, an NMPC scheme based on improved continuous/generalized minimum residuals (C/GMRES) is proposed to obtain acceptable control performance and reduce computational complexity. The proposed control scheme achieves efficient and smooth tracking control of the UAV while guaranteeing the closed-loop stability of the system. Finally, simulation results are presented to illustrate the effectiveness and superior performance of the proposed NMPC control scheme.
A Multi-Dimensional Reverse Auction Mechanism for Volatile Federated Learning in the Mobile Edge Computing Systems
Authors: Hong Yiming , Zheng Zhaohua , Wang Zizheng
Published: August 2024

Federated learning (FL) can break the problem of data silos and allow multiple data owners to collaboratively train shared machine learning models without disclosing local data in mobile edge computing. However, how to incentivize these clients to actively participate in training and ensure efficient convergence and high test accuracy of the model has become an important issue. Traditional methods often use a reverse auction framework but ignore the consideration of client volatility. This paper proposes a multi-dimensional reverse auction mechanism (MRATR) that considers the uncertainty of client training time and reputation. First, we introduce reputation to objectively reflect the data quality and training stability of the client. Then, we transform the goal of maximizing social welfare into an optimization problem, which is proven to be NP-hard. Then, we propose a multi-dimensional auction mechanism MRATR that can find the optimal client selection and task allocation strategy considering clients’ volatility and data quality differences. The computational complexity of this mechanism is polynomial, which can promote the rapid convergence of FL task models while ensuring near-optimal social welfare maximization and achieving high test accuracy. Finally, the effectiveness of this mechanism is verified through simulation experiments. Compared with a series of other mechanisms, the MRATR mechanism has faster convergence speed and higher testing accuracy on both the CIFAR-10 and IMAGE-100 datasets.
A Systematic Literature Review on the Use of Federated Learning and Bioinspired Computing
Authors: Marin Machado de Souza Rafael , Holm Andrew , Biczyk Márcio , de Castro Leandro Nunes
Published: August 2024

Federated learning (FL) and bioinspired computing (BIC), two distinct, yet complementary fields, have gained significant attention in the machine learning community due to their unique characteristics. FL enables decentralized machine learning by allowing models to be trained on data residing across multiple devices or servers without exchanging raw data, thus enhancing privacy and reducing communication overhead. Conversely, BIC draws inspiration from nature to develop robust and adaptive computational solutions for complex problems. This paper explores the state of the art in the integration of FL and BIC, introducing BIC techniques and discussing the motivations for their integration with FL. The convergence of these fields can lead to improved model accuracy, enhanced privacy, energy efficiency, and reduced communication overhead. This synergy addresses inherent challenges in FL, such as data heterogeneity and limited computational resources, and opens up new avenues for developing more efficient and autonomous learning systems. The integration of FL and BIC holds promise for various application domains, including healthcare, finance, and smart cities, where privacy-preserving and efficient computation is paramount. This survey provides a systematic review of the current research landscape, identifies key challenges and opportunities, and suggests future directions for the successful integration of FL and BIC.
A Lightweight Wildfire Detection Method for Transmission Line Perimeters
Authors: Huang Xiaolong , Xie Weicheng , Zhang Qiwen , Lan Yeshen , Heng Huiling , Xiong Jiawei
Published: August 2024

Due to extreme weather conditions and complex geographical features, the environments around power lines in forest areas have a high risk of wildfires. Once a wildfire occurs, it causes severe damage to the forest ecosystem. Monitoring wildfires around power lines in forested regions through deep learning can reduce the harm of wildfires to natural environments. To address the challenges of wildfire detection around power lines in forested areas, such as interference from complex environments, difficulty detecting small target objects, and high model complexity, a lightweight wildfire detection model based on the improved YOLOv8 is proposed. Firstly, we enhanced the image-feature-extraction capability using a novel feature-extraction network, GS-HGNetV2, and replaced the conventional convolutions with a Ghost Convolution (GhostConv) to reduce the model parameters. Secondly, the use of the RepViTBlock to replace the original Bottleneck in C2f enhanced the model’s feature-fusion capability, thereby improving the recognition accuracy for small target objects. Lastly, we designed a Resource-friendly Convolutional Detection Head (RCD), which reduces the model complexity while maintaining accuracy by sharing the parameters. The model’s performance was validated using a dataset of 11,280 images created by merging a custom dataset with the D-Fire data for monitoring wildfires near power lines. In comparison to YOLOv8, our model saw an improvement of 3.1% in the recall rate and 1.1% in the average precision. Simultaneously, the number of parameters and computational complexity decreased by 54.86% and 39.16%, respectively. The model is more appropriate for deployment on edge devices with limited computational power.
An Efficient Multi-Branch Attention Network for Person Re-Identification
Authors: Han Ke , Zhu Mingming , Li Pengzhen , Dong Jie , Xie Haoyang , Zhang Xiyan
Published: August 2024

Due to the absence of tailored designs that address challenges such as variations in scale, disparities in illumination, and instances of occlusion, the implementation of current person re-identification techniques remains challenging in practical applications. An Efficient Multi-Branch Attention Network over OSNet (EMANet) is proposed. The structure is composed of three parts, the global branch, relational branch, and global contrastive pooling branch, and corresponding features are obtained from different branches. With the attention mechanism, which focuses on important features, DAS attention evaluates the significance of learned features, awarding higher ratings to those that are deemed crucial and lower ratings to those that are considered distracting. This approach leads to an enhancement in identification accuracy by emphasizing important features while discounting the influence of distracting ones. Identity loss and adaptive sparse pairwise loss are used to efficiently facilitate the information interaction. In experiments on the Market-1501 mainstream dataset, EMANet exhibited high identification accuracies of 96.1% and 89.8% for Rank-1 and mAP, respectively. The results indicate the superiority and effectiveness of the proposed model.
The Role of FPGAs in Modern Option Pricing Techniques: A Survey
Authors: O Mahony Aidan , Hanzon Bernard , Popovici Emanuel
Published: August 2024

In financial computation, Field Programmable Gate Arrays (FPGAs) have emerged as a transformative technology, particularly in the domain of option pricing. This study presents the impact of Field Programmable Gate Arrays (FPGAs) on computational methods in finance, with an emphasis on option pricing. Our review examined 99 selected studies from an initial pool of 131, revealing how FPGAs substantially enhance both the speed and energy efficiency of various financial models, particularly Black–Scholes and Monte Carlo simulations. Notably, the performance gains—ranging from 270- to 5400-times faster than conventional CPU implementations—are highly dependent on the specific option pricing model employed. These findings illustrate FPGAs’ capability to efficiently process complex financial computations while consuming less energy. Despite these benefits, this paper highlights persistent challenges in FPGA design optimization and programming complexity. This study not only emphasises the potential of FPGAs to further innovate financial computing but also outlines the critical areas for future research to overcome existing barriers and fully leverage FPGA technology in future financial applications.
High-Precision Calibration Method and Error Analysis of Infrared Binocular Target Ranging Systems
Authors: Zeng Changwen , Wei Rongke , Gu Mingjian , Zhang Nejie , Dai Zuoxiao
Published: August 2024

Infrared binocular cameras, leveraging their distinct thermal imaging capabilities, are well-suited for visual measurement and 3D reconstruction in challenging environments. The precision of camera calibration is essential for leveraging the full potential of these infrared cameras. To overcome the limitations of traditional calibration techniques, a novel method for calibrating infrared binocular cameras is introduced. By creating a virtual target plane that closely mimics the geometry of the real target plane, the method refines the feature point coordinates, leading to enhanced precision in infrared camera calibration. The virtual target plane is obtained by inverse projecting the centers of the imaging ellipses, which are estimated at sub-pixel edge, into three-dimensional space, and then optimized using the RANSAC least squares method. Subsequently, the imaging ellipses are inversely projected onto the virtual target plane, where its centers are identified. The corresponding world coordinates of the feature points are then refined through a linear optimization process. These coordinates are reprojected onto the imaging plane, yielding optimized pixel feature points. The calibration procedure is iteratively performed to determine the ultimate set of calibration parameters. The method has been validated through experiments, demonstrating an average reprojection error of less than 0.02 pixels and a significant 24.5% improvement in calibration accuracy over traditional methods. Furthermore, a comprehensive analysis has been conducted to identify the primary sources of calibration error. Ultimately, this achieves an error rate of less than 5% in infrared stereo ranging within a 55-m range.