bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Crop Pest Detection Model Based on YOLOv5
Authors: Yang Wenji , Qiu Xiaoying
Published: February 2024

The damage caused by pests to crops results in reduced crop yield and compromised quality. Accurate and timely pest detection plays a crucial role in helping farmers to defend against and control pests. In this paper, a novel crop pest detection model named YOLOv5s-pest is proposed. Firstly, we design a hybrid spatial pyramid pooling fast (HSPPF) module, which enhances the model’s capability to capture multi-scale receptive field information. Secondly, we design a new convolutional block attention module (NCBAM) that highlights key features, suppresses redundant features, and improves detection precision. Thirdly, the recursive gated convolution (g3Conv) is introduced into the neck, which extends the potential of self-attention mechanism to explore feature representation to arbitrary-order space, enhances model capacity and detection capability. Finally, we replace the non-maximum suppression (NMS) in the post-processing part with Soft-NMS, which improves the missed problem of detection in crowded and dense scenes. The experimental results show that the mAP@0.5 (mean average precision at intersection over union (IoU) threshold of 0.5) of YOLOv5s-pest achieves 92.5% and the mAP@0.5:0.95 (mean average precision from IoU 0.5 to 0.95) achieves 72.6% on the IP16. Furthermore, we also validate our proposed method on other datasets, and the outcomes indicate that YOLOv5s-pest is also effective in other detection tasks.
Automated Model Selection Using Bayesian Optimization and the Asynchronous Successive Halving Algorithm for Predicting Daily Minimum and Maximum Temperatures
Authors: Roy Dilip Kumar , Hossain Mohamed Anower , Haque Mohamed Panjarul , Alataway Abed , Dewidar Ahmed Z. , Mattar Mohamed A.
Published: February 2024

This study addresses the crucial role of temperature forecasting, particularly in agricultural contexts, where daily maximum (Tmax) and minimum (Tmin) temperatures significantly impact crop growth and irrigation planning. While machine learning (ML) models offer a promising avenue for temperature forecasts, the challenge lies in efficiently training multiple models and optimizing their parameters. This research addresses a research gap by proposing advanced ML algorithms for multi-step-ahead Tmax and Tmin forecasting across various weather stations in Bangladesh. The study employs Bayesian optimization and the asynchronous successive halving algorithm (ASHA) to automatically select top-performing ML models by tuning hyperparameters. While both the Bayesian and ASHA optimizations yield satisfactory results, ASHA requires less computational time for convergence. Notably, different top-performing models emerge for Tmax and Tmin across various forecast horizons. The evaluation metrics on the test dataset confirm higher accuracy, efficiency coefficients, and agreement indices, along with lower error values for both Tmax and Tmin forecasts at different weather stations. Notably, the forecasting accuracy decreases with longer horizons, emphasizing the superiority of one-step-ahead predictions. The automated model selection approach using Bayesian and ASHA optimization algorithms proves promising for enhancing the precision of multi-step-ahead temperature forecasting, with potential applications in diverse geographical locations.
Intercropped Soybean Plant Population in a Coffee Plantation and Its Effects on Agronomic Parameters and Geospatial Information
Authors: de Carvalho Eberton , de Assis Gleice Aparecida , Martins George Deroco , Marques Douglas José , dos Santos Edson Aparecido , Xavier Laura Cristina Moura , Malta Lorrayne Maria Rodrigues , Zampiroli Renan
Published: February 2024

Coffee farming has high land use value, which can result in economic losses without proper land use planning. Intercropping has improved coffee production by providing an alternative income source to producers, especially in the crop formation phase. The objective of this study was to evaluate productivity, growth, and geospatial data in different soybean plant populations intercropped with coffee. The experiment was conducted at the Federal University of Uberlândia, in Monte Carmelo, Minas Gerais, Brazil. It had an experimental randomized block design with five treatments: the control (no soybeans); 80 thousand plants ha−1; 160 thousand plants ha−1; 240 thousand plants ha−1; and 320 thousand plants ha−1. Productive and vegetative characteristics of coffee and soybeans were evaluated, as well as the NDVI and GNDI vegetation indices. The mass of 1000 grains of soybean reached its peak (178.96 g) with a population of 222 thousand plants of soybean ha−1. The maximum soybean productivity of 102.78 bags ha−1 was obtained in the population of 185 thousand plants soybean ha−1. An increasing population of 240–320 thousand plants ha−1 soybean between coffee rows reduced soybean yield due to reduced light, causing plant lodging. The biometric parameters of the coffee trees did not change, which was evidenced by high normalized and green normalized difference vegetation indices (NDVI and GNDVI, respectively). Therefore, it is concluded that the use of soybeans between the coffee trees does not affect the vegetative and productive parameters of the coffee tree, making the use of the intercrop viable.
Refined Evaluation of Climate Suitability of Maize at Various Growth Stages in Major Maize-Producing Areas in the North of China
Authors: Wang Xiaowei , Li Xiaoyu , Lou Yunsheng , You Songcai , Zhao Haigen
Published: February 2024

The Northeast region of China and Huang Huai Hai (3H) region are vital maize production bases in northern China that are crucial for national food security. The absence of phenological data hinders a detailed assessment of the alignment between maize development stages and climatic resources. This study combines the authors’ maize phenology data with climate suitability modeling to evaluate maize’s climate suitability at different developmental stages in both regions. This study shows that during the maize growth cycle, the average temperature, precipitation, sunshine, and comprehensive climate suitability were 0.77, 0.49, 0.87, and 0.65, respectively, in the Northeast. In contrast, the average temperature, precipitation, sunshine, and comprehensive climate suitability in the 3H region were 0.98, 0.53, 0.73, and 0.70, respectively. Precipitation is a major factor influencing maize growth, with temperature and sunshine impacting growth differently across regions. Temperature significantly affects maize in the Northeast, while sunshine plays a greater role in the 3H region. The Northeast is suitable for drought-resistant maize varieties, and implementing a late harvest policy in Liaoning could enhance maize yield. The 3H region generally has favorable climatic conditions. Apart from certain parts of Henan needing drought-resistant varieties, areas with ample growing seasons can adopt long-duration varieties to maximize thermal resource utilization. Our results have important implications for optimizing maize planting strategies and enhancing regional resilience, aiming to assess meteorological factors’ impact on maize growth in key production areas.
Spatial Distribution Characteristics of Soil C:N:P:K Eco-Stoichiometry of Farmland and Grassland in the Agro-Pastoral Ecotone in Inner Mongolia, China
Authors: Zhang Yanli , Liu Miao , Han Li , Yang Jinhu , Zhao Xinyao , Qu Jiahui , Li Lijun , Bai Yunlong , Yan Dong , Hou Guannan
Published: February 2024

Ecological stoichiometry (ES) is an important index that reflects the balance of various elements in ecological processes. Therefore, it is of great significance to understand the soil nutrient cycle to clarify the environmental control of soil carbon (C), nitrogen (N), phosphorus (P), and potassium (K). In this study, we analyzed the spatial distribution of soil C, N, P, and K contents and the C:N:P:K stoichiometric characteristics of 0–20 cm and 20–40 cm of farmland and grassland in four agro-pastoral areas in Inner Mongolia. Spearman correlation was used to analyze the effects of environmental factors on the soil C:N:P:K stoichiometric relationship. The results showed that there was no fixed Redfield ratio for the soil stoichiometric relationship of farmland and grassland in Inner Mongolia, and the values were 15:2:1:9 to 145:10:1:26 and 25:1:1:29 to 228:15:1:65, respectively. The stoichiometric relationships between farmland and grassland were consistent with the law of geographical and spatial heterogeneity. The ratios of C:N, C:P, C:K, N:P, and N:K showed an N distribution from west to east, while the ratio of P:K showed a V distribution. The stoichiometric relationships in grassland soil were mainly affected by soil organic carbon and total nitrogen content, while those in farmland were mainly affected by total nitrogen and total phosphorus content. The annual mean precipitation has a significant effect on stoichiometric relationships in farmland, while the annual mean temperature has a more significant effect on grassland. In conclusion, the spatial distribution difference in the soil stoichiometric relationship in the agro-pastoral ecotone of Inner Mongolia was more significant than the difference in the land use pattern. The influences of annual mean temperature and annual mean precipitation on soil ecological stoichiometry were in accordance with the geographical spatial similarity law. Compared with grassland, the stoichiometric relationship of farmland soil was greatly affected by fertilization, and farmland in this region was mainly limited by carbon and nitrogen. Thus, field management should be carried out according to local conditions. This study is of great significance as it promotes the rational utilization of land resources and the sustainable development of agriculture.
Deep Learning Model Effectiveness in Forecasting Limited-Size Aboveground Vegetation Biomass Time Series: Kenyan Grasslands Case Study
Authors: Noa-Yarasca Efrain , Osorio Leyton Javier M. , Angerer Jay P.
Published: February 2024

Timely forecasting of aboveground vegetation biomass is crucial for effective management and ensuring food security. However, research on predicting aboveground biomass remains scarce. Artificial intelligence (AI) methods could bridge this research gap and provide early warning to planners and stakeholders. This study evaluates the effectiveness of deep learning (DL) algorithms in predicting aboveground vegetation biomass with limited-size data. It employs an iterative forecasting procedure for four target horizons, comparing the performance of DL models—multi-layer perceptron (MLP), long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), and CNN-LSTM—against the traditional seasonal autoregressive integrated moving average (SARIMA) model, serving as a benchmark. Five limited-size vegetation biomass time series from Kenyan grasslands with values at 15-day intervals over a 20-year period were chosen for this purpose. Comparing the outcomes of these models revealed significant differences (p < 0.05); however, none of the models proved superior among the five time series and the four horizons evaluated. The SARIMA, CNN, and CNN-LSTM models performed best, with the statistical model slightly outperforming the other two. Additionally, the accuracy of all five models varied significantly according to the prediction horizon (p < 0.05). As expected, the accuracy of the models decreased as the prediction horizon increased, although this relationship was not strictly monotonic. Finally, this study indicated that, in limited-size aboveground vegetation biomass time series, there is no guarantee that deep learning methods will outperform traditional statistical methods.
First Report of Caseous Lymphadenitis by Corynebacterium pseudotubercolosis and Pulmonary Verminosis in a Roe Deer (Capreolus capreolus Linnaeus, 1758) in Italy
Authors: Di Donato Alessandra , Gambi Lorenzo , Ravaioli Valentina , Perulli Simona , Cirasella Letizia , Rossini Rachele , Luppi Andrea , Tosi Giovanni , Fiorentini Laura
Published: February 2024

Caseous lymphadenitis is a chronic debilitating disease typical of small ruminants, but it is also noted in several other domestic and wild species. In this report, we present the first documented case in Italy of pseudotuberculosis in a roe deer (Capreolus capreolus, Linnaeus 1758) found dead in the mountains of Forlì-Cesena province, Emilia Romagna region. The carcass underwent necropsy according to standard protocols, revealing generalized lymphadenopathy and severe apostematous pneumonia with multifocal and encapsulated abscesses. Corynebacterium pseudotuberculosis was isolated from the lung parenchyma, lymph nodes and abscesses. Additionally, severe parasitic bronchopneumonia of the caudal lobes and gastrointestinal strongyle infestation were detected. To our knowledge, this is the first documented case of CLA referable to C. pseudotubercolosis in a roe deer in Italy.
Effect of Lighter and Heavier Initial Weight on Growth Performance and Carcass Traits of Single-Source Beef Steers
Authors: Norman Thomas C. , DeHaan Erin R. , Francis Forest L. , Rusche Warren C. , Smith Zachary K.
Published: February 2024

The objective of the study was to determine the influence that initial BW has on growth performance responses, efficiency of dietary net energy (NE) utilization, and carcass traits in feedlot steers. Charolais×Red Angus steers (n = 70) selected from a larger single-source group were used in a 209-d growing-finishing feedlot experiment. Steers were assigned to two groups based on initial BW (light initial weight, LIW = 273 ± 16.0 kg; heavy initial weight, HIW = 356 ± 14.2 kg) and allotted into 10 pens (n = 7 steers per pen; 5 pens per experimental group) the within pen standard deviation for LIW was from 14.1 kg to 20.9 kg and for HIW was from 13.7 kg to 16.0 kg. Steers were fed a common diet once daily. Experimental data were analyzed as a randomized complete block design with pen as the experimental unit. LIW steers had a greater cumulative HH change (p = 0.04). A treatment × day interaction (p = 0.05) was observed for HH with HIW steers having a greater HH at all time points. Final BW and carcass-adjusted (HCW/0.625) BW were greater for HIW steers by 13.1% and 13.4% respectively (p ≤ 0.01). HIW steers had a greater DMI (p = 0.01) compared to LIW. Cumulative ADG was greater for HIW by 3% (p = 0.04). LIW steers had better feed conversion (p = 0.01). HIW steers had greater (p ≤ 0.05) HCW, marbling scores, and yield grade (YG), with decreased REA/HCW (p = 0.01) compared to LIW. The distribution of USDA Yield Grade was altered by initial BW (p = 0.04). No differences were detected (p ≥ 0.22) for the distribution of Quality grade nor liver abscess prevalence and severity. Regression coefficients did not differ between LIW and HIW for urea space calculations of empty body water, fat, or protein (p ≥ 0.70). A quadratic response was noted for empty body fat (EBF), empty body water (EBH20), and carcass protein (CP). In conclusion, HIW steers had greater growth, but poorer feed efficiency compared to LIW steers. Steers with a HIW produced fatter carcasses with a greater degree of marbling.
Automatic Recognition and Quantification Feeding Behaviors of Nursery Pigs Using Improved YOLOV5 and Feeding Functional Area Proposals
Authors: Luo Yizhi , Xia Jinjin , Lu Huazhong , Luo Haowen , Lv Enli , Zeng Zhixiong , Li Bin , Meng Fanming , Yang Aqing
Published: February 2024

A novel method is proposed based on the improved YOLOV5 and feeding functional area proposals to identify the feeding behaviors of nursery piglets in a complex light and different posture environment. The method consists of three steps: first, the corner coordinates of the feeding functional area were set up by using the shape characteristics of the trough proposals and the ratio of the corner point to the image width and height to separate the irregular feeding area; second, a transformer module model was introduced based on YOLOV5 for highly accurate head detection; and third, the feeding behavior was recognized and counted by calculating the proportion of the head in the located feeding area. The pig head dataset was constructed, including 5040 training sets with 54,670 piglet head boxes, and 1200 test sets, and 25,330 piglet head boxes. The improved model achieves a 5.8% increase in the mAP and a 4.7% increase in the F1 score compared with the YOLOV5s model. The model is also applied to analyze the feeding pattern of group-housed nursery pigs in 24 h continuous monitoring and finds that nursing pigs have different feeding rhythms for the day and night, with peak feeding periods at 7:00–9:00 and 15:00–17:00 and decreased feeding periods at 12:00–14:00 and 0:00–6:00. The model provides a solution for identifying and quantifying pig feeding behaviors and offers a data basis for adjusting the farm feeding scheme.
An Engineered Mouse Model That Generates a Diverse Repertoire of Endogenous, High-Affinity Common Light Chain Antibodies
Authors: Rong Yinghui , Chen I-Ling , Larrabee Lance , Sawant Manali S. , Fuh Germaine , Koenig Patrick
Published: February 2024

Bispecific antibodies have gained increasing popularity as therapeutics as they enable novel activities that cannot be achieved with monospecific antibodies. Some of the most popular bispecific formats are molecules in which two Fab arms with different antigen specificities are combined into one IgG-like molecule. One way to produce these bispecific molecules requires the discovery of antibodies against the two antigens of interest that share a common light chain. Here, we present the generation and characterization of a common light chain mouse model, in which the endogenous IGKJ cluster is replaced with a prearranged, modified murine IGKV10-96/IGKJ1 segment. We demonstrate that genetic modification does not impact B-cell development. Upon immunization with ovalbumin, the animals generate an antibody repertoire with VH gene segment usage of a similar diversity to wildtype mice, while the light chain diversity is restricted to antibodies derived from the prearranged IGKV10-96/IGKJ1 germline. We further show that the clonotype diversity of the common light chain immune repertoire matches the diversity of immune repertoire isolated from wildtype mice. Finally, the common light chain anti-ovalbumin antibodies have only slightly lower affinities than antibodies isolated from wildtype mice, demonstrating the suitability of these animals for antibody discovery for bispecific antibody generation.
Evaluation of Bioactive Effects of Five Plant Extracts with Different Phenolic Compositions against Different Therapeutic Targets
Authors: Villegas-Aguilar María del Carmen , Sánchez-Marzo Noelia , Fernández-Ochoa Álvaro , Del Río Carmen , Montaner Joan , Micol Vicente , Herranz-López María , Barrajón-Catalán Enrique , Arráez-Román David , Cádiz-Gurrea María de la Luz , Segura-Carretero Antonio
Published: February 2024

Plant extracts rich in phenolic compounds have been reported to exert different bioactive properties. Despite the fact that there are plant extracts with completely different phenolic compositions, many of them have been reported to have similar beneficial properties. Thus, the structure–bioactivity relationship mechanisms are not yet known in detail for specific classes of phenolic compounds. In this context, this work aims to demonstrate the relationship of extracts with different phenolic compositions versus different bioactive targets. For this purpose, five plant matrices (Theobroma cacao, Hibiscus sabdariffa, Silybum marianum, Lippia citriodora, and Olea europaea) were selected to cover different phenolic compositions, which were confirmed by the phytochemical characterization analysis performed by HPLC-ESI-qTOF-MS. The bioactive targets evaluated were the antioxidant potential, the free radical scavenging potential, and the inhibitory capacity of different enzymes involved in inflammatory processes, skin aging, and neuroprotection. The results showed that despite the different phenolic compositions of the five matrices, they all showed a bioactive positive effect in most of the evaluated assays. In particular, matrices with very different phenolic contents, such as T. cacao and S. marianum, exerted a similar inhibitory power in enzymes involved in inflammatory processes and skin aging. It should also be noted that H. sabdariffa and T. cacao extracts had a low phenolic content but nevertheless stood out for their bioactive antioxidant and anti-radical capacity. Hence, this research highlights the shared bioactive properties among phenolic compounds found in diverse matrices. The abundance of different phenolic compound families highlights their elevated bioactivity against diverse biological targets.
Introduction of Internal Circulation-Based Cooling Methods and Green Coolants in Milling via Cutting Tool and Tooling Design
Authors: Radchenko Ilia , Takahashi Wataru , Takahashi Hidebumi , Abe Taro , Sasahara Hiroyuki
Published: February 2024

This paper describes the process of the design and verification of a milling tool and tooling that may contribute to the renouncement of the flood cooling method when mineral oils and oil-in-water emulsions are used as coolants. The proposed solutions are based on the idea of coolant supply in internal channels created inside of a cutting tool. As an alternative to the aforementioned mineral oil-based coolants, liquids with higher cooling efficiency and environmental friendliness (green coolants) were considered. Given coolants’ possible lack of lubricating properties and negative (corrosive, etc.) influence on a machine tool’s units, tooling delivers these coolants to the cutting tool and bypasses the standard machine tool’s supply system. The geometry of the milling tool (a cutting insert with an internal channel) was tested in the framework of a stress simulation. To perform it, cutting force components Fz, Fy, and Fx were determined empirically and then applied to the simulated area of contact between the tool and the workpiece. Based on the obtained principal stress values P1, P2, and P3, the factor of safety was calculated with the Mohr–Coulomb, P1 max, and P3 min failure criteria. The proposed milling tool, equipped with a novel type of labyrinth seal with no friction between its components, was experimentally tested to confirm its ability to maintain leak-tightness at different values of spindle speed (200~2000 rpm) and coolant supply volume (1.0~10.0 L/min). Based on the results of the stress simulation and the leak-tightness experiment, conclusions were drawn about further modernization and utilization prospects of the proposed milling tool and tooling design.
An Overview of Biopolymers for Drug Delivery Applications
Authors: Opriș Ocsana , Mormile Cristina , Lung Ildiko , Stegarescu Adina , Soran Maria-Loredana , Soran Albert
Published: February 2024

Nowadays, drug delivery has an important role in medical therapy. The use of biopolymers in developing drug delivery systems (DDSs) is increasingly attracting attention due to their remarkable and numerous advantages, in contrast to conventional polymers. Biopolymers have many advantages (biodegradability, biocompatibility, renewability, affordability, and availability), which are extremely important for developing materials with applications in the biomedical field. Additionally, biopolymers are appropriate when they improve functioning and have a number of positive effects on human life. Therefore, this review presents the most used biopolymers for biomedical applications, especially in drug delivery. In addition, by combining different biopolymers DDSs with tailored functional properties (e.g., physical properties, biodegradability) can be developed. This review summarizes and provides data on the progress of research on biopolymers (chitosan, alginate, starch, cellulose, albumin, silk fibroin, collagen, and gelatin) used in DDSs, their preparation, and mechanism of action.
Fault Detection in Active Magnetic Bearings Using Digital Twin Technology
Authors: Hu Yefa , Taha Omer W. , Yang Kezhen
Published: February 2024

Active magnetic bearings (AMBs) are widely used in different industries to offer non-contact and high-velocity rotational support. The AMB is prone to failures, which may result in system instability and decreased performance. The efficacy and reliability of magnetic bearings can be significantly affected by failures in the sensor and control systems, leading to system imbalance and possible damage. A digital twin is an advanced technology that has been increasingly used in different industrial fields. It allows for the creation and real-time monitoring of virtual replicas of physical systems. This paper proposes a novel method for fault detection of Active Magnetic Bearings (AMBs) using digital twin technology and a neural network. The digital twin model serves as a virtual representation that accurately replicates the actual AMB system’s efficiency and features, allowing continuous real-time monitoring and detection of faults. The conventional neural network (CNN) is used as the primary tool for identifying faults in the Active Magnetic Bearing (AMB) within a digital twin model. Experiments proved the effectiveness and robustness of the suggested approach method to fault detection in the AMB.
FPGA-Based Hardware Implementation of a Stable Inverse Source Problem Algorithm in a Non-Homogeneous Circular Region
Authors: Oliveros-Oliveros José Jacobo , Conde-Sánchez José Rubén , Hernández-Gracidas Carlos Arturo , Morín-Castillo María Monserrat , Conde-Mones José Julio
Published: February 2024

Objective: This work presents an implementation of a stable algorithm that recovers sources located at the boundary separating two homogeneous media in field-programmable gate arrays. Two loop unrolling architectures were developed and analyzed for this purpose. This inverse source problem is ill-posed due to numerical instability, i.e., small errors in the measurement can produce significant changes in the source location. Methodology: To handle the numerical instability when recovering these sources, the Tikhonov regularization method in combination with the Fourier series truncation method are applied in the stable algorithm. This stable algorithm is implemented in two different architectures developed in this work: The first architecture (Mode 1) allows for different operating speeds, which is an advantage depending on whether we work with fast or slow signals. The second one (Mode 2) reduces resource consumption by exploiting the characteristics of the source identification algorithm, which is an advantage for multichannel problems such as inverse electrocardiography or electroencephalography. Results: The architectures were tested on four devices of the 7 Series of Xilinx: Spartan-7 xc7s100fgga484, Virtex-7 xc7v585tffg1157, Kintex-7 xc7k70tfbg484, and Artix-7 xc7a35tcpg236. The two hardware implementations of the stable algorithm were validated using synthetic examples implemented in MATLAB, which shows the advantages of each architecture. Contributions: We developed two efficient architectures based on a loop unrolling design for source identification problems. These are effective strategies to divide and assign tasks to the configurable hardware, and they appear as an appropriate technique for implementing the algorithm. The first one is simple and allows for different operating speeds. The second one uses a control system based on multiplexors that reduce resource consumption and complexity of the design and can be used for multichannel problems. From the numerical test, we found the regularization parameters. The synthetic examples developed here can be considered for similar problems and can be extended to concentric spheres.
Immuno-PET for Glioma Imaging: An Update
Authors: De Feo Maria Silvia , Granese Giorgia Maria , Conte Miriam , Palumbo Barbara , Panareo Stefano , Frantellizzi Viviana , De Vincentis Giuseppe , Filippi Luca
Published: February 2024

Despite significant advances in glioma diagnosis and treatment, overall outcomes remain suboptimal. Exploring novel therapeutic avenues show promise in advancing the field. Theranostics, an evolving discipline integrating diagnosis and therapy, emerges as a particularly auspicious approach. However, an unmet need exists for glioma-associated biomarkers as theranostic targets. Immuno-positron emission tomography (Immuno-PET), a pioneering method uniting PET diagnostic precision with antibody specificity, holds potential for identifying cancer-associated biomarkers. This review aims to provide an updated overview of immuno-PET applications in gliomas. Notably, [44Sc]-CHX-A″-DTPA-Cetuximab-Fab targeting Epidermal Growth Factor Receptor (EGFR) has displayed promise in glioma xenografts, enabling potential imaging at 4 h post-injection. Similarly, [89Zr]-bevacizumab targeting vascular endothelial growth factor (VEGF) yielded encouraging results in preclinical models and a pioneering clinical trial for pediatric patients with diffuse intrinsic pontine glioma (DIPG). Several cell differentiation markers, including CD146, indicative of tumor aggressiveness, and CD11b, reflecting tumor-associated myeloid cells (TAMCs), proved effective targets for immuno-PET. Additionally, immuno-PET directed at prostate-specific antigen (PSMA) demonstrated efficacy in imaging glioma-associated neovasculature. While holding promise for precise diagnosis and treatment guidance, challenges persist in achieving target specificity and selecting suitable radionuclides. Further studies are imperative to advance the field and bridge a translational gap from bench to bedside.
Bedding Slope Destabilization under Rainfall: A Case Study of Zhuquedong Slope in Hunan Province, China
Authors: Xu Fan , Hu Huihua , Lin Hang , Xie Linglin
Published: February 2024

The soft interlayer and rock structure play a significant role in controlling the deformation of the bedding slope, and it is necessary to consider the phenomenon of the sudden change of local response in these key parts under rainfall conditions, and then to clarify the mechanism of rainfall infiltration and damage mechanism of such slopes. In this paper, a large red-layered flat-dipping bedding landslide was selected as the research object, and numerical calculations based on the Van Genuchten model for saturated–unsaturated flow were performed in order to investigate the hydrological response and distribution patterns of water within the slope during rainfall. Moreover, stability analysis was performed based on the seepage field results and secondary development of FLAC3D, and the landslide evolution process was simulated and reproduced using the constitutive model of double-variables and the strength reduction method (SRM). The results showed that the effects of heavy rainfall on the water distribution and stability of the highway slope are significant, while the effects on the natural slope are not significant. There are three phases of the slope destabilization: flexure and uplift state, deformation exacerbation state and shear failure state. The slope destabilization mechanism is a typical “sliding-bending-shearing” type. The results of the study can provide a theoretical basis for the study of the seepage, stability analysis and destabilization mechanism of bedding slopes.
Diurnal Variations and Driving Factors of CO2 Flux at Water–Air Interfaces in the Open-Flow Sections of Karst Underground Rivers
Authors: Li Danyang , Li Canfeng , Huang Chao , Li Hong , Xu Xiongwei , Peng Xuefeng , Chen Guiren , Zhang Liankai
Published: February 2024

The high-intensity partial pressure of CO2 (pCO2) in karst underground rivers rapidly releases in open-flow sections. This is an important process affecting the global karst carbon cycle. This study focuses on the diurnal variation patterns and driving factors of CO2 exchange flux at the water–air interface in the open-flow sections of typical karst underground rivers in southwestern China. The inorganic carbon in water and water–air interface exchange fluxes are observed. Three representative survey stations, i.e., the outlet of the underground river (Q1), the river sections without submerged plants (H1), and the river sections with submerged plants (H2), are selected to study the CO2 exchange process and its influencing factors. The results show that the CO2 release flux at Q1 exhibits high pressure in the daytime and low pressure in the nighttime, while H1 and H2 exhibit the opposite pattern. The photosynthesis of submerged plants significantly inhibits the carbon release flux of the river, and in the river sections where submerged plants are distributed, their biological effects have inhibited approximately 0.131 Tg C/yr of carbon emissions. This study emphasizes the significant contribution of submerged plants in restraining the release of CO2, thereby promoting carbon sequestration and storage in karst water systems.
Deciphering Autonomous Vehicle Regulations with Machine Learning
Authors: Bridgelall Raj , Tolliver Denver
Published: February 2024

The emergence of autonomous vehicles (AVs) presents a transformative shift in transportation, promising enhanced safety and economic efficiency. However, a fragmented legislative landscape across the United States hampers AV deployment. This fragmentation creates significant challenges for AV manufacturers and stakeholders. This research contributes by employing advanced machine learning (ML) techniques to analyze state data, aiming to identify factors associated with the likelihood of passing AV-friendly legislation, particularly regarding the requirement for human backup drivers. The findings reveal a nuanced interplay of socio-economic, political, demographic, and safety-related factors influencing the nature of AV legislation. Key variables such as democratic electoral college votes per capita, port tons per capita, population density, road fatalities per capita, and transit agency needs significantly impact legislative outcomes. These insights suggest that a combination of political, economic, and safety considerations shape AV legislation, transcending traditional partisan divides. These findings offer a strategic perspective for developing a harmonized regulatory approach, potentially at the federal level, to foster a conducive environment for AV development and deployment.
Network Traffic Characteristics and Analysis in Recent Mobile Games
Authors: Moon Daekyeong
Published: February 2024

The landscape of mobile gaming has evolved significantly over the years, with profound changes in network reliability and traffic patterns. In the early 2010s, mobile games faced challenges due to unreliable networks and primarily featured asynchronous gameplay. However, in the current era, modern mobile games benefit from robust network connectivity, mirroring PC gaming experiences by relying on persistent connections to game servers. This shift prompted us to conduct an in-depth traffic analysis of two mobile games that represent opposite ends of the genre spectrum: a massively multiplayer game resembling PC MMORPGs with tightly synchronized gameplay, and a single-player puzzle game that incorporates asynchronous social interactions. Surprisingly, both games exhibited remarkably similar traffic footprints; small packets with short inter-packet arrival times, indicating their high expectations for network reliability. This suggests that game developers now prioritize network quality similarly to their PC gaming counterparts. Additionally, our analysis of packet lengths unveiled that recent mobile games predominantly employ short packets dominated by a few key packet types closely tied to player actions, which conforms to observations from PC online games. However, the self-similarity in traffic patterns, a notable feature in PC online games, only partially explains the traffic in mobile games, varying across genres. These findings shed light on the evolving traffic patterns in mobile games and emphasize the need for further research in this dynamic domain.
Lightweight YOLOv7 Algorithm for Multi-Object Recognition on Contrabands in Terahertz Images
Authors: Ge Zihao , Zhang Yuan , Jiang Yuying , Ge Hongyi , Wu Xuyang , Jia Zhiyuan , Wang Heng , Jia Keke
Published: February 2024

With the strengthening of worldwide counter-terrorism initiatives, it is increasingly important to detect contrabands such as controlled knives and flammable materials hidden in clothes and bags. Terahertz (THz) imaging technology is widely used in the field of contraband detection due to its advantages of high imaging speed and strong penetration. However, the terahertz images are of poor qualities and lack texture details. Traditional target detection methods suffer from low detection speeds, misdetection, and omission of contraband. This work pre-processes the original dataset using a variety of image processing methods and validates the effect of these methods on the detection results of YOLOv7. Meanwhile, the lightweight and multi-object detection YOLOv7 (LWMD-YOLOv7) algorithm is proposed. Firstly, to meet the demand of real-time for multi-target detection, we propose the space-to-depth mobile (SPD_Mobile) network as the lightweight feature extraction network. Secondly, the selective attention module large selective kernel (LSK) network is integrated into the output of the multi-scale feature map of the LWMD-YOLOv7 network, which enhances the effect of feature fusion and strengthens the network’s attention to salient features. Finally, Distance Intersection over Union (DIOU) is used as the loss function to accelerate the convergence of the model and to have a better localisation effect for small targets. The experimental results show that the YOLOv7 algorithm achieves the best detection results on the terahertz image dataset after the non-local mean filtering process. The LWMD-YOLOv7 algorithm achieves a detection accuracy P of 98.5%, a recall R of 97.5%, and a detection speed of 112.4 FPS, which is 26.9 FPS higher than that of the YOLOv7 base network. The LWMD-YOLOv7 achieves a better balance between detection accuracy and detection speed. It provides a technological reference for the automated detection of contraband in terahertz images.
Applying Process Mining: The Reality of a Software Development SME
Authors: Urrea-Contreras Silvia Jaqueline , Astorga-Vargas Maria Angelica , Flores-Rios Brenda L. , Ibarra-Esquer Jorge Eduardo , Gonzalez-Navarro Felix F. , Garcia Pacheco Ivan , Pacheco Agüero Carla Leninca
Published: February 2024

One of the challenges the organizations confront is to extract data from the information systems to know the reality of their processes to improve their efficiency. In this study, the application of Process Mining is addressed as an opportunity in the specific context of an SME dedicated to software development, implementing the L* life cycle model methodology from a layered Software Engineering approach. This research is carried out based on process improvement in an initial SME project. Subsequently, it is compared with a second project, using different Process Mining perspectives such as control flow, case, organization, and time, with the aim of extending the process model. This holistic view allows not only to better understand the processes involved, but also to identify and analyze the similarities and differences between the two projects. As a result, the Process Mining analysis shows crucial aspects such as the representation of integrated models, traces on sequences of actions, and the interaction of activities with specific roles and deviations in the flow of activities that compromise the quality of the process and the product. At the same time, the challenges that emerged during the improvement cycle are highlighted. These challenges cover issues such as data extraction, fluid communication between those involved, and the documentation associated with the processes. This study contributes to the body of knowledge of Process Mining. Likewise, the case study results offer a vision for other SMEs seeking to incorporate Process Mining as part of their improvement strategies.
Improved Patch Packing and Refining Segmentation for the V-PCC Standard
Authors: Luo Hao , Chi Yirong , Lu Shiyu , Ding Yang , Han Cheng
Published: February 2024

High-performance coding solutions are urgently needed for the storage and transmission of 3D point clouds due to the development of 3D data acquisition facilities and the increasing scale of acquired point clouds. Video-based point cloud compression (V-PCC) is the most advanced international standard for compressing dynamic point clouds. However, it still has serious issues of time consumption and the large size of the occupancy map. Considering the aforementioned issues, based on V-PCC, we propose the Voxel Selection-based Refining Segmentation (VS-RS), which is used to accelerate the refining segmentation process of the point cloud. Furthermore, the data-adaptive patch packing (DAPP) is proposed to reduce the size of the occupancy map. In order to specify the effect of the improvement, we also designed novel evaluation indicators. Experimental results show that the proposed method achieves a Bjøntegaard Delta rate (BD-rate) gain of −1.58% in the V-PCC benchmark. Additionally, it reduces encoding time by up to 31.86% and reduces the size of the occupancy map by up to 20.14%.
Collecting and Organizing the Influencing Factors of Team Communications to Handle Nuclear Power Plant Emergencies
Authors: Salim Shelly , Yeom Seon-Yeong , Ham Dong-Han
Published: February 2024

A nuclear power plant (NPP), as a complex safety-critical system, requires qualified operators working in teams. Interactions between operators in the main control room (MCR) team are important to ensure safe operation. Since communication is the basis of the operators’ interactions, team communication is a significant factor affecting teamwork performance. Especially during NPP emergencies, poor team communication may lead to incorrect decisions and countermeasures, causing deterioration toward accidents. Moreover, in an emergency situation, emergency response teams are assembled. This multi-team and critical work condition further emphasizes the need for effective and accurate team communication. We collected the factors influencing team communication in NPP emergencies using a literature review combined with text mining. Our method for extracting the influencing factors consists of four steps; then, by applying topic modeling from text mining, we complemented the influencing factors. The resulting list of influencing factors of team communications for handling NPP emergencies is organized into five elements: individual, team, communication, NPP tasks, and external elements. Discussions on the team communication model, applicability, communication errors, and emergency response teams are also presented.
Analytical Solution of Ice–Rock-Model Stress Field and Stress Intensity Factors in Inhomogeneous Media
Authors: Cao Feifei , Jing Laiwang , Peng Shaochi
Published: February 2024

The stress distribution and fracture parameter calibration of ice–rock models are important aspects of studying rock properties at high altitudes and latitudes. However, progress in ice–rock modeling has been slow and singular, and it is limited due to the discrete nature of rocks and the applicability of fracture mechanics. In this study, a circular inhomogeneous ice–rock model is proposed for the first time, and a method is provided for calculating the stress field of the model under biaxial loading. A method for calculating the single-crack stress intensity factor of the model subjected to biaxial compressive loading is also provided. The novelty of this work is that the inhomogeneous ice–rock model is treated as a superposition of two models, namely, a circular pore plate and circular ice, according to the superposition principle. The key is that the stress field distribution law of the ice–rock model is obtained based on the basis of the displacement continuity of the ice–rock interface. The analytical and approximate solutions of the stress intensity factor of a single crack were also obtained by considering the normal phase effect of the crack surface and combining the stress distribution law of the ice–rock model. Comparison with the CAE method was made to verify the correctness of the stress field and stress intensity factor calculation methods. The evolution laws of lateral pressure coefficients, the elastic modulus ratio of ice and rock on the stress field, and the stress intensity factor were analyzed. The effects of lateral pressure coefficients, elastic modulus ratios, and crack distributions on the failure modes were investigated using the extended finite element method (XFEM). This study can provide a theoretical basis for the evaluation of mechanical properties and prediction of the failure modes of frozen rock bodies.
Methodological Approach to Assessing the Current State of Organizations for AI-Based Digital Transformation
Authors: Aldoseri Abdulaziz , Al-Khalifa Khalifa N. , Hamouda Abdel Magid
Published: February 2024

In an era defined by technological disruption, the integration of artificial intelligence (AI) into business processes is both strategic and challenging. As AI continues to disrupt and reshape industries and revolutionize business processes, organizations must take proactive steps to assess their readiness and capabilities to effectively leverage AI technologies. This research focuses on the assessment elements required to evaluate an organization’s current state in preparation for AI-based digital transformation. This research is based on a literature review and practical insights derived from extensive experience in industrial system engineering. This paper outlines the key assessment elements that organizations should consider to ensure successful and sustainable AI-based digital transformation. This emphasizes the need for a comprehensive approach to assess the organization’s data infrastructure, governance practices, and existing AI capabilities. Furthermore, the research work focuses on the evaluation of AI talent and skills within the organization, considering the significance of fostering an innovative culture and addressing change management challenges. The results of this study provide organizations with elements to assess their current state for AI-based digital transformation. By adopting and implementing the proposed guidelines, organizations can gain a holistic perspective of their current standing, identify strategic opportunities for AI integration, mitigate potential risks, and strategize a successful path forwards in the evolving landscape of AI-driven digital transformation.
An Analysis of the Instability Conditions and Water Vapor Transport Characteristics during a Typical Rainstorm in the Tarim Basin
Authors: Jin Chen , He Qing , Huang Qian , Chen Ze
Published: February 2024

In order to deepen the understanding of the occurrence mechanism and water vapor transport characteristics of the heavy rain process in the extreme arid region of Xinjiang, a rare heavy rain process in the Tarim Basin during the period of 18–22 July 2021 was comprehensively analyzed by using multi-source data. The results show that the upper tropospheric South Asian high was distributed in a “west-high-east-low” pattern during the rainstorm process, and the rainstorm area was located on the right side of the upper jet stream entrance area, while the middle-level Iranian high pressure, Baikal high pressure and Central Asian low pressure formed a “two-highs and one-low” circulation situation. The coupling of the high and low air jets and the strong vertical upward motion provided favorable dynamic conditions. Rainstorm water vapor mainly comes from the Mediterranean Sea, Central Asia and the Indian Ocean, and it enters the basin in four paths: west, east to west, west to east, and southwest and south. The water vapor mainly flows into the middle layer of the western boundary and the southern boundary and the lower layer of the eastern boundary, and it flows out from the middle and upper layer of the eastern boundary. The negative moist potential vorticity region at a low level has a strong indicator significance for the occurrence and development of heavy rain, and the superposition of positive and negative moist potential vorticity regions at vertical height is conducive to the occurrence and development of heavy rain.