bluuebunny/crossref_metadata_2025 huggingface dataset:
How Competitive Is the Agriculture in European Union Member States? An Assessment of Changes over the Past Decade
Authors: Nowak Anna , Zakrzewska Aneta
Published: April 2024

The need to monitor changes in the level of agricultural competitiveness of EU member states necessitates the development of an appropriate set of indicators to answer the question of whether a country is improving its competitive position. This paper proposes a synthetic measure for assessing the agricultural competitiveness in the member states of the European Union (EU) from 2012 to 2021. Statistics on the respective indicators were derived from the Eurostat database and Agri-Food Data Portal. The survey was based on linear ordering using a standardised sums approach. This implies that member states vary in competitiveness in the agricultural sector. From 2012 to 2021, the most competitive countries were Denmark, the Netherlands, and Belgium. Simultaneously, the rankings of agricultural competitiveness changed over the 10 years under review. The highest improvement rates were recorded in Finland, Slovakia, and Ireland. However, the differences between the old and new member states are still explicit. None of the new member states of the EU were in the top ten with regard to their agricultural competitiveness. Many areas associated with agriculture in these countries require considerable improvement. We also carried out a principal component analysis (PCA), which allowed us to identify the parameters conveying relevant information on the analysed phenomenon and unveil the hidden structures in the primary data set. We discovered that the share of the EU’s agricultural output in the EU exports of agricultural products explained variations in the agricultural competitiveness within member states.
Use of a Calving Blind That Imitates a Natural Environment
Authors: Olsen Hannah E. , Vogel Kurt D. , Creutzinger Kate C.
Published: April 2024

Many indoor-housed cows isolate at calving when given the opportunity, and calving behaviors vary by blind and pen design. The objectives of this study were to determine if cows preferred calving in a visibly separated (blind) or an open area of a group maternity pen, and if there was a preference for the degree of seclusion provided by the blind (50% vs. 100% coverage). Two calving blinds were provided in a group calving pen, and the amount of visibility through the blinds was created using firehoses secured from the top of a metal frame that lined the entire front of the blind (100%) or with every other hose rolled up (50%). Holstein cows and heifers (n = 79) were enrolled into a dynamic group calving pen 21 ± 3 d before calving. Calving location, the difference in blind use prior to calving compared to a baseline period, and social behaviors were recorded using video observation. There was no difference in the number of cows that calved in or outside of a blind (28 vs. 37 calvings, respectively). Cows were more likely to calve in a blind during the day than at night and as the number of cows in the pen increased. For cows who calved in a blind, there was no preference for calving in the 50% or 100% blind (10 vs. 18, respectively). Providing a varied environment for intensively managed cattle can improve their welfare by allowing cows the opportunity to perform natural behaviors and choice over their environment.
Addition of Butyric Acid and Lauric Acid Glycerides in Nursery Pig Feed to Replace Conventional Growth Promoters
Authors: Ficagna Cássio Antônio , Galli Gabriela Miotto , Zatti Emerson , Zago Isadora , Amaral Marco Aurélio Fritzen Dias do , de Vitt Maksuel Gatto , Paiano Diovani , da Silva Aleksandro Schafer
Published: April 2024

(1) Background: This study determined whether adding butyric acid and lauric acid glycerides in nursing pigs’ feed would improve growth performance, proteinogram, biochemical parameters, and antioxidant status. (2) Methods: Ninety male pigs were divided into five groups with six repetitions per group: NC, negative control (no additive); TRI-BUT, addition of tributyrin in the basal ration; MDT-BUT, addition of mono-, di-, and triglycerides of butyric acid in the basal feed; MDT-LAU, the addition of mono-, di-, and triglycerides of lauric acid in the basal feed; and PC, positive control (addition of gentamicin in the basal feed). (3) Results: PC, TRI-BUT, and MDT-LAU resulted in a high average daily WG from days 1 to 39 (p < 0.01). MDT-LAU, MDT-BUT, and PC resulted in a greater feed:gain from days 1 to 39 than the NC (p = 0.03). Great concentrations of the gamma globulin fraction in all groups were observed than in the NC (p = 0.01). Ceruloplasmin, haptoglobin, and C-reactive protein concentrations were lower in all groups than in the NC (p < 0.05). Higher serum glutathione S-transferase activity was observed in the TRI-BUT and MDT-BUT than in the PC (p = 0.04). (4) Conclusions: The addition of butyric acid and lauric acid glycerides in the diet of pigs in the nursery phase can replace growth promoters since the products improve the growth performance, reduce acute-phase proteins, and increase gamma globulin concentrations.
Open-Set Recognition of Individual Cows Based on Spatial Feature Transformation and Metric Learning
Authors: Wang Buyu , Li Xia , An Xiaoping , Duan Weijun , Wang Yuan , Wang Dian , Qi Jingwei
Published: April 2024

The automated recognition of individual cows is foundational for implementing intelligent farming. Traditional methods of individual cow recognition from an overhead perspective primarily rely on singular back features and perform poorly for cows with diverse orientation distributions and partial body visibility in the frame. This study proposes an open-set method for individual cow recognition based on spatial feature transformation and metric learning to address these issues. Initially, a spatial transformation deep feature extraction module, ResSTN, which incorporates preprocessing techniques, was designed to effectively address the low recognition rate caused by the diverse orientation distribution of individual cows. Subsequently, by constructing an open-set recognition framework that integrates three attention mechanisms, four loss functions, and four distance metric methods and exploring the impact of each component on recognition performance, this study achieves refined and optimized model configurations. Lastly, introducing moderate cropping and random occlusion strategies during the data-loading phase enhances the model’s ability to recognize partially visible individuals. The method proposed in this study achieves a recognition accuracy of 94.58% in open-set scenarios for individual cows in overhead images, with an average accuracy improvement of 2.98 percentage points for cows with diverse orientation distributions, and also demonstrates an improved recognition performance for partially visible and randomly occluded individual cows. This validates the effectiveness of the proposed method in open-set recognition, showing significant potential for application in precision cattle farming management.
Infrared Thermography in Assessment of Facial Temperature of Racing Sighthound-Type Dogs in Different Environmental Conditions
Authors: Budny-Walczak Anna , Wilk Martyna , Kupczyński Robert
Published: April 2024

The aim of the study was to assess the usefulness of IRT measurements of selected regions of interest (ROI), i.e., the eyeball and the nose of whippet dogs, before and after coursing competitions taking place in various environmental conditions, thereby enabling the assessment of well-being and the level of heat stress. The research was carried out over two different periods with different thermal humidity indexes (THIs). In the first period, the THI was 59.27 (Run 1), while in the second period, the THI was 63.77 (Run 2). The experimental subjects comprised 111 sighthound-type dogs—whippets—that were photographed with a thermal imaging camera to determine their eye temperature (ET) and nose temperature (NT). The average minimum and maximum eye temperatures were statistically lower after running in both measurements. Increased minimum and maximum nose temperatures were also demonstrated after both runs. The nasal temperature values were statistically higher for Run 2, for which the THI was higher, compared to Run 1. Eyeball temperature may be a marker of thermoregulation ability, regardless of the ambient temperature. The value of ETmax decreased on average by 2.23 °C and 0.4 °C, while NTmax increased uniformly by 2 °C after both runs. A correlation was found between the IRT measurements and physiological indicators.
Metagenomic Analysis Reveals A Gut Microbiota Structure and Function Alteration between Healthy and Diarrheic Juvenile Yaks
Authors: Zhao Hongwen , Mo Quan , Kulyar Muhammad Fakhar-e-Alam , Guan Jiuqiang , Zhang Xiangfei , Luo Xiaolin , Li Jiakui
Published: April 2024

Diarrhea-induced mortality among juvenile yaks is highly prevalent in the pastoral areas of the Qinghai-Tibet plateau. Although numerous diseases have been linked to the gut microbial community, little is known about how diarrhea affects the gut microbiota in yaks. In this work, we investigated and compared changes in the gut microbiota of juvenile yaks with diarrhea. The results demonstrated a considerable drop in the alpha diversity of the gut microbiota in diarrheic yaks, accompanied by Eysipelatoclostridium, Parabacteroides, and Escherichia-Shigella, which significantly increased during diarrhea. Furthermore, a PICRust analysis verified the elevation of the gut–microbial metabolic pathways in diarrhea groups, including glycine, serine, and threonine metabolism, alanine, aspartate, oxidative phosphorylation, glutamate metabolism, antibiotic biosynthesis, and secondary metabolite biosynthesis. Taken together, our study showed that the harmful bacteria increased, and beneficial bacteria decreased significantly in the gut microbiota of yaks with diarrhea. Moreover, these results also indicated that the dysbiosis of the gut microbiota may be a significant driving factor of diarrhea in yaks.
Beyond Tolerance: Mitigating Human–Wildlife Conflict with Hospitality
Authors: Serenari Christopher
Published: April 2024

Tolerance has become a central position in wildlife conservation thought, and a goal in and of itself. Appeals to tolerance are expected to grow as the planet becomes more crowded, species are lost, and habitat is degraded. The concept has been uncritically adopted in wildlife conservation to mitigate human–wildlife conflicts (HWCs). However, scholars have demonstrated that tolerance is burdened with limitations, paradoxes, and shortcomings. Thus, blind adherence to it is not expected to produce a coexistence design necessary to sustain wildlife populations in the long term. This paper is a conceptual scoping project that engages a summary and critique of tolerance as a design principle within wildlife conservation governance. After introducing a resultant theory of dysfunctional human–wildlife coexistence, a pathway toward hospitality as a social institution is outlined via several commitments societies can make to transition to an era of normalizing a process of sincere welcoming, care, and support. The transition from tolerance to hospitality will entail shifting responsibility to humans to modify their behavior to help keep wildlife invisible where it is essential, learning about what wildlife want and need, and ensuring wildlife is not injured for being themselves.
Preliminary Results on the Effects of Soybean Isoflavones on Growth Performance and Ruminal Microbiota in Fattening Goats
Authors: Shao Yuexin , Xu Junhong , Wang Mengyu , Ren Yalun , Wei Manhong , Tian Bowen , Luo Jun , Loor Juan J. , Shi Huaiping
Published: April 2024

Soybean isoflavones (SIFs), a group of secondary metabolites, have antioxidant, anti-inflammatory, and hormone-like activities. Supplementation with SIFs in the diet was reported to promote lactation performance in ruminants. The present study was performed to further decipher the effect of various concentrations of SIFs on growth and slaughter performance, serum parameters, meat quality, and ruminal microbiota in fattening goats. After a two-week acclimation, a total of 27 5-month-old Guanzhong male goats (18.29 ± 0.44 kg) were randomly assigned to control (NC), 100 mg/d SIF (SIF1), or 200 mg/d SIF (SIF2) groups. The experimental period lasted 56 days. The weight of the large intestine was greater (p < 0.05) in the SIF1 and SIF2 groups compared with the NC group. Meat quality parameters indicated that SIF1 supplementation led to lower (p < 0.05) cooking loss and shear force (0.05 < p < 0.10). The 16S rRNA sequencing analysis demonstrated that SIF1 supplementation led to lower (p < 0.05) proportions of Papillibacter and Prevotellaceae_UCG-004 but greater (p < 0.05) CAG-352 abundance in the rumen; these responses might have contributed to the improvement in production performance. In conclusion, meat quality and ruminal microbiome could be manipulated in a positive way by oral supplementation with 100 mg/d of SIFs in fattening goats. Thus, this study provides new insights and practical evidence for the introduction of SIFs as a novel additive in goat husbandry.
A Biochemical and Histological Assessment of Postmortem Changes to the Eyes of Domestic Pigs: A Preliminary Study
Authors: Palić Magdalena , Šoštarić Zuckermann Ivan-Conrado , Džaja Petar , Ljubić Blanka Beer , Severin Krešimir
Published: April 2024

The Postmortem Interval (PMI) is the time from the death of an animal to its discovery. From a veterinary forensic standpoint, an accurate estimation of the PMI is of particular importance, especially with the observed increase in deaths of domestic and wild animals. A preliminary study was conducted using the eyes of domestic pigs. A biochemical analysis was conducted on the vitreous humor of the eye, whilst a histological analysis was conducted on the retina. The eyes were stored at +4 °C and changes were assessed at time intervals of 0, 12, 24, 48, and 120 h. The biochemical analysis during the PMI established a decrease in sodium, chlorine, and glucose concentrations, and a rise in potassium concentration. Accordingly, a simple linear regression showed a significant correlation between changes in concentrations of sodium (Na+), potassium (K+), chloride (Cl−), and glucose, in relation to the PMI. The histological analysis showed evident morphological changes in the retina, which included homogenization of the rod and cone cells, pyknosis of the outer nuclear layer, homogenization of the outer plexiform layer, pyknosis of the inner nuclear layer, homogenization of the inner plexiform layer, and pyknosis of the nuclei of the ganglion layer of the retina.
Polyphenolic Composition and Antimicrobial, Antioxidant, Anti-Inflammatory, and Antihyperglycemic Activity of Different Extracts of Teucrium montanum from Ozren Mountain
Authors: Sailović Pero , Odžaković Božana , Bodroža Darko , Vulić Jelena , Čanadanović-Brunet Jasna , Zvezdanović Jelena , Danilović Bojana
Published: April 2024

Teucrium montanum has widespread use in folk medicine on the Balkan peninsula. In order to scientifically justify this use, the composition and biological activity of aqueous, ethanol, and acetone extract were investigated in this study. Moreover, acetone and ethanol extracts were obtained from the plant material previously exhausted by water extraction. A total of 27 compounds were detected in extracts by UHPLC-DAD-MS/MS analysis, with all of them present in acetone and ethanol extracts. Consequentially, the acetone and ethanol extracts showed higher contents of total phenols of 23% and 18%, respectively, compared to the water extract. The results indicated high biological potential in the investigated extracts. Among all extracts, the aqueous extract showed slightly higher antimicrobial potential, especially against Gram-positive strains, probably due to the release of components soluble in water from the dry unexhausted plant material. On the other hand, the acetone and ethanol extracts had significantly higher antioxidative (by 20%), anti-inflammatory activity (up to 3 and 4 times higher, respectively), and α-glucosidase inhibitory potential (3 times higher) than the aqueous extract. The results of this investigation reveal the great potential of the use of T. montanum in various branches of food, cosmetics, and the pharmaceutical industry. An important part of this research is a confirmation that, once exhausted by water extraction, for example by hydrodistillation, T. montanum plant material can be reused for obtaining valuable products with a wide range of biological activities.
Effect of Face Masks on Automatic Speech Recognition Accuracy for Mandarin
Authors: Li Xiaoya , Ni Ke , Huang Yu
Published: April 2024

Automatic speech recognition (ASR) has been widely used to realize daily human–machine interactions. Face masks have become everyday wear in our post-pandemic life, and speech through masks may have impaired the ASR. This study explored the effects of different kinds of face masks (e.g., surgical mask, KN95 mask, and cloth mask) on the Mandarin word accuracy of two ASR systems with or without noises. A mouth simulator was used to play speech audio with or without wearing a mask. Acoustic signals were recorded at distances of 0.2 m and 0.6 m. Recordings were mixed with two noises at a signal-to-noise ratio of +3 dB: restaurant noise and speech-shaped noise. Results showed that masks did not affect ASR accuracy without noise. Under noises, masks did not significantly influence ASR accuracy at 0.2 m but had significant effects at 0.6 m. The activated-carbon mask had the most significant impact on ASR accuracy at 0.6 m, reducing the accuracy by 18.5 percentage points compared to that without a mask, whereas the cloth mask had the least effect on ASR accuracy at 0.6 m, reducing the accuracy by 0.9 percentage points. The acoustic attenuation of masks on the high-frequency band at around 3.15 kHz of the speech signal attributed to the effects of masks on ASR accuracy. When training ASR models, it may be important to consider mask robustness.
Improvement of Multimodal Emotion Recognition Based on Temporal-Aware Bi-Direction Multi-Scale Network and Multi-Head Attention Mechanisms
Authors: Wu Yuezhou , Zhang Siling , Li Pengfei
Published: April 2024

Emotion recognition is a crucial research area in natural language processing (NLP), aiming to identify emotional states such as happiness, anger, and sadness from various sources like speech, text, and facial expressions. In this paper, we propose an improved MMER (multimodal emotion recognition) method using TIM-Net (Temporal-Aware Bi-Direction Multi-Scale Network) and attention mechanisms. Firstly, we introduce the methods for extracting and fusing the multimodal features. Then, we present the TIM-Net and attention mechanisms, which are utilized to enhance the MMER algorithm. We evaluate our approach on the IEMOCAP and MELD datasets, and compared to existing methods, our approach demonstrates superior performance. The weighted accuracy recall (WAR) on the IEMOCAP dataset is 83.9%, and the weighted accuracy recall rate on the MELD dataset is 62.7%. Finally, the impact of the TIM-Net model and the attention mechanism on the emotion recognition performance is further investigated through ablation experiments.
Topic Modelling of Management Research Assertions to Develop Insights into the Role of Artificial Intelligence in Enhancing the Value Propositions of Early-Stage Growth-Oriented Companies
Authors: Tanev Stoyan , Keen Christian , Bailetti Tony , Hudson David
Published: April 2024

The article suggests a Value Proposition (VP) framework that enables analysis of the beneficial impact of Artificial Intelligence (AI) resources and capabilities on specific VP activities. To develop such a framework, we examined existing business and management research publications to identify and extract assertions that could be used as a source of actionable insights for early-stage growth-oriented companies. The extracted assertions were assembled into a corpus of texts that was subjected to topic modelling analysis—a machine learning approach to natural language processing that is used to identify latent themes in large corpora of text documents. The topic modelling resulted in the identification of seven topics. Each topic is defined by a set of most frequent words co-occurring in a distinctive subset of texts that could be interpreted in terms of activities constituting the core elements of the VP framework. We then examined each activity in terms of its potential to be enhanced by employing AI resources and capabilities. The interpretation of the topic modelling results led to the identification of seven topics: (1) Value created; (2) Stakeholder value propositions; (3) Foreign market entry; (4) Customer base; (5) Continuous improvement; (6) Cross-border operations; and (7) Company image. The uniqueness of the adopted topic modelling approach consists in the quality of the assertions and the interpretation of the seven topics as an activity framework, i.e., in its capacity to generate actionable insights for practitioners. The additional analysis suggests that there is a potential for AI to enhance the emerging four core elements of the VP framework: Value created, Stakeholder value propositions, Foreign market entry, and Customer base. More importantly, we found that the greatest number of assertions related to activities that could be enhanced by AI are part of the Customer base topic, i.e., the topic that is most directly related to the growth potential of the companies. In addition, the VP framework suggests that a company’s customer base growth is continuously enhanced through a positive loop enabled by activities focused on the Continuous improvement of the activities and the amount of Value created, the alignment of Stakeholder value propositions, and companies’ Foreign market entry. Thus, the multiple-stakeholder perspective on VP development and foreign market entry appears as a factor that helps in understanding the beneficial impact of AI on the enhancement of the VP of early-stage growth-oriented companies.
PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction
Authors: Zhang Deyu , Wang Changying , Huang Baoxiang , Ren Jing , Zhao Junli , Hou Guojia
Published: April 2024

Arctic sea ice concentration plays a key role in the global ecosystem. However, accurate prediction of Arctic sea ice concentration remains a challenging task due to its inherent nonlinearity and complex spatiotemporal correlations. To address these challenges, we propose an innovative encoder–decoder pyramid dilated convolutional long short-term memory network (DED-ConvLSTM). The model is constructed based on the convolutional long short-term memory network (ConvLSTM) and, for the first time, integrates the encoder–decoder architecture of ConvLSTM (ED-ConvLSTM) with a pyramidal dilated convolution strategy. This approach aims to efficiently capture the spatiotemporal properties of the sea ice concentration and to enhance the identification of its nonlinear relationships. By applying convolutional layers with different dilation rates, the PDED-ConvLSTM model can capture spatial features at multiple scales and increase the receptive field without losing resolution. Further, the integration of the pyramid convolution module significantly enhances the model’s ability to understand complex spatiotemporal relationships, resulting in notable improvements in prediction accuracy and generalization ability. The experimental results show that the sea ice concentration distribution predicted by the PDED-ConvLSTM model is in high agreement with ground-based observations, with the residuals between the predictions and observations maintained within a range from −20% to 20%. PDED-ConvLSTM outperforms other models in terms of prediction performance, reducing the RMSE by 3.6% compared to the traditional ConvLSTM model and also performing well over a five-month prediction period. These experiments demonstrate the potential of PDED-ConvLSTM in predicting Arctic sea ice concentrations, making it a viable tool to meet the requirements for accurate prediction and provide technical support for safe and efficient operations in the Arctic region.
Identification and Characterization of a Novel Thermostable GDSL Lipase LipGt6 from Geobacillus thermoleovorans H9
Authors: Qin Lirong , Lin Min , Zhan Yuhua , Jiang Shijie , Zhou Zhengfu , Wang Jin
Published: April 2024

Lipases are versatile biocatalysts for various biological reactions. In the detergent industry, lipases must exhibit high activity in environments with high temperature, high pH values, metal ions, and organic solvents. Therefore, researchers are intensively searching for more stable and efficient lipases. A new thermophilic lipase, LipGt6, was identified in Geobacillus thermoleovorans H9, a new thermophilic strain isolated from ultrahigh-temperature compost. A structural model of LipGt6 was constructed using an esterase from Geobacillus thermodenitrificans as a template, and site-directed mutagenesis confirmed the predicted active site residues. LipGt6 exhibited the highest activity towards medium- and long-chain fatty acids (C8–C14), and the optimum temperature and pH were 50 °C and 9.0, respectively. LipGt6 was found to be thermostable up to 70 °C. In the presence of 1% H2O2 and sodium deoxycholate, LipGt6 retained 70 to 75% relative activity. These findings reveal that LipGt6 is potentially useful for the industrial production of detergent. Based on comparison of the amino acid sequences, the enzyme belongs to a new subfamily called lipolytic enzyme family II. The catalytic residues Ser and His were more critical than Asp, and the Asp221 catalytic residue is not likely critical for the lipolytic reaction of LipGt6.
Ensuring the Quality of the Analytical Process in a Research Laboratory
Authors: Surleva Andriana , Angelova Lyudmila , Ilieva Darya , Ivanova Vladislava , Surleva Olya , Chavdarova Katrin
Published: April 2024

This paper discusses approaches for verification of methods of measurements of chemical and physical characteristics of specific samples. The limitations of well-known approaches are discussed. Some examples of alternatives are given to demonstrate specific issues encountered in the research laboratory analyzing new materials or characterizing new properties of materials. Application of sequential procedure using lower quantities of samples and reagents is presented. A standard addition to solid samples is discussed. The approach of control charts for estimation of method uncertainty for determination of plant available phosphorus is presented. The method comparison is applied as an approach to verification of alkaline reactivity by inductively coupled plasma–optical emission spectroscopy (ICP-OES) measurement, as well as density of newly synthesized chalcogenide glass materials. The presented examples demonstrated that alternative approaches are needed in order to verify the methods applied due to the great variety of activities and corresponding tasks in a research laboratory.
Knowledge Distillation Based on Fitting Ground-Truth Distribution of Images
Authors: Li Jianze , Tang Zhenhua , Chen Kai , Cui Zhenlei
Published: April 2024

Knowledge distillation based on the features from the penultimate layer allows the student (lightweight model) to efficiently mimic the internal feature outputs of the teacher (high-capacity model). However, the training data may not conform to the ground-truth distribution of images in terms of classes and features. We propose two knowledge distillation algorithms to solve the above problem from the directions of fitting the ground-truth distribution of classes and fitting the ground-truth distribution of features, respectively. The former uses teacher labels to supervise student classification output instead of dataset labels, while the latter designs feature temperature parameters to correct teachers’ abnormal feature distribution output. We conducted knowledge distillation experiments on the ImageNet-2012 and Cifar-100 datasets using seven sets of homogeneous models and six sets of heterogeneous models. The experimental results show that our proposed algorithms improve the performance of penultimate layer feature knowledge distillation and outperform other existing knowledge distillation methods in terms of classification performance and generalization ability.
MSACN: A Cloud Extraction Method from Satellite Image Using Multiscale Soft Attention Convolutional Neural Network
Authors: Gao Lin , Gai Chenxi , Lu Sijun , Zhang Jinyi
Published: April 2024

In satellite remote sensing images, the existence of clouds has an occlusion effect on ground information. Different degrees of clouds make it difficult for existing models to accurately detect clouds in images due to complex scenes. The detection and extraction of clouds is one of the most important problems to be solved in the further analysis and utilization of image information. In this article, we refined a multi-head soft attention convolutional neural network incorporating spatial information modeling (MSACN). During the encoder process, MSACN extracts cloud features through a concurrent dilated residual convolution module. In the part of the decoder, there is an aggregating feature module that uses a soft attention mechanism. It integrates the semantic information with spatial information to obtain the pixel-level semantic segmentation outputs. To assess the applicability of MSACN, we compare our network with Transform-based and other traditional CNN-based methods on the ZY-3 dataset. Experimental outputs including the other two datasets show that MSACN has a better overall performance for cloud extraction tasks, with an overall accuracy of 98.57%, a precision of 97.61%, a recall of 97.37%, and F1-score of 97.48% and an IOU of 95.10%.
Hybrid Strategy Improved Beetle Antennae Search Algorithm and Application
Authors: Shan Xiaohang , Lu Shasha , Ye Biqing , Li Mengzheng
Published: April 2024

The multi-dimensional optimization of mechanisms is a typical optimization problem encountered in mechanical design. Herein, the Hybrid strategy improved Beetle Antennae Search (HSBAS) algorithm is proposed to solve the multi-dimensional optimization problems encountered in structural design. To solve the problems of local optimization and low accuracy of the high-dimensional solution of the Beetle Antennae Search (BAS) algorithm, the algorithm adopts the adaptive step strategy, multi-directional exploration strategy, and Lens Opposition-Based Learning strategy, significantly reducing the probability of the algorithm falling into the local optimum and improving its global search capability. Comparative experiments of the improved algorithm are carried out by selecting eleven benchmark test functions. HSBAS can reach 1 × 10−22 accuracy from the optimal value when dealing with low-dimensional functions. It can also obtain 1 × 10−2 accuracy when dealing with high-dimensional functions, significantly improving the algorithm’s capability. According to Friedman’s ranking test result, HSBAS ranks first, which proves that HSBAS is superior to the other three algorithms. The HSBAS algorithm is further used to optimize the design of the altitude compensation module of the gravity compensation device for solar wings, controlling the fluctuation of bearing capacity within 0.25%, which shows that the algorithm can be used as an effective tool for engineering structural optimization problems.
Weak Magnetic Fields Regulate the Ability of High Dilutions of Water to Enhance ROS Production by Neutrophils
Authors: Fesenko Evgeny E. , Yablokova Elena V. , Novikov Vadim V.
Published: April 2024

The influence of magnetic fields on the physico–chemical properties of water and aqueous solutions is well known. We have previously shown that weak combined magnetic fields with a 60 µT static component and a 100 nT (at 12.6 Hz) variable component are able to activate neutrophils, both directly and indirectly, through water pre-incubated in these fields. The ability to influence the activity of neutrophils was retained in serial dilutions of water, but only when a mechanical effect (shaking) was applied at each dilution step. Here, we confirm that combined magnetic fields are required for the formation of the stimulatory activity of water on ROS production by neutrophils. For the first time, we determined the threshold values of a constant magnetic field (at least 350–550 nT) necessary to maintain this activity in a series of successive dilutions. Additionally, the biophysical properties of various dilutions appeared to be not identical. This confirms that the number of technological steps (successive dilutions with physical influence) is a key factor that determines the activity of highly diluted samples.
Transient Phenomena of Dynamic Contact Angle in Micro Capillary Flows
Authors: Kim Young Bae , Sung Jaeyong
Published: April 2024

This study is devoted to investigating the dynamics of liquid driven by capillary force in a circular tube. A microscope was used to visualize the meniscus movement and the contact angle. The experiments were carried out with glycerin–water mixtures with viscosity ranging from 0.21 to 1.36 Pa∙s by filling the test liquid in a borosilicate glass tube with an inner diameter of 200 μm. The wetting distances of the meniscus with time were compared with the theoretical solution by considering the dynamic variation of contact angle. The results show that the theoretical solution agrees well with experimental data due to the reflection of the actual dynamic contact angle for the transient motions in the developing entrance region. In view of momentum balance, variations of dominant force according to the time were determined by separated inertial periods, such as inertial, inertial-viscous, and viscous time stages.
Aspects of Lighting and Color in Classifying Malignant Skin Cancer with Deep Learning
Authors: Santos Alan R. F. , Aires Kelson R. T. , Veras Rodrigo M. S.
Published: April 2024

Malignant skin cancers are common in emerging countries, with excessive sun exposure and genetic predispositions being the main causes. Variations in lighting and color, resulting from the diversity of devices and lighting conditions during image capture, pose a challenge for automated diagnosis through digital images. Deep learning techniques emerge as promising solutions to improve the accuracy of identifying malignant skin lesions. This work aims to investigate the impact of lighting and color correction methods on automated skin cancer diagnosis using deep learning architectures, focusing on the relevance of these characteristics for accuracy in identifying malignant skin cancer. The developed methodology includes steps for hair removal, lighting, and color correction, defining the region of interest, and classification using deep neural network architectures. We employed deep learning techniques such as LCDPNet, LLNeRF, and DSN for lighting and color correction, which still need to be tested in this context. The results emphasize the importance of image preprocessing, especially in lighting and color adjustments, where the best results show an accuracy increase of between 3% and 4%. We observed that different deep neural network architectures react variably to lighting and color corrections. Some architectures are more sensitive to variations in these characteristics, while others are more robust. Advanced lighting and color correction can thus significantly improve the accuracy of malignant skin cancer diagnosis.
Entropy Wavelet-Based Method to Increase Efficiency in Highway Bridge Damage Identification
Authors: Machorro-Lopez Jose M. , Yanez-Borjas Jesus J. , Valtierra-Rodriguez Martin , Amezquita-Sanchez Juan P.
Published: April 2024

Highway bridges are crucial civil constructions for the transport infrastructure, which require proper attention from the corresponding institutions of each country and constant financing for their adequate maintenance; this is important because different types of damage can be generated within these structures, caused by natural disasters, among other sources, and the heavy loads they transport every day. Therefore, the development of simple, efficient, and low-cost methods is of vital importance, allowing us to identify damage in a timely manner and avoid bridges collapsing. As reported in a previous work, the wavelet energy accumulation method (WEAM) and its corresponding application in the Rio Papaloapan Bridge (RPB) represented an important advance within the field. Despite identifying damage in bridges with precision and at a low cost, there are several aspects to improve in that method. Therefore, in this work, that method was improved, eliminating several steps, and meaningfully reducing the computational burden by implementing an algorithm based on the Shannon entropy, thus giving way to the new entropy wavelet-based method (EWM). This new method was applied directly with regard to the real-life RPB, in both its healthy and damaged conditions. Also, its corresponding numerical model based on the finite element method in its healthy condition and different damage scenarios were carried out. The results indicate that the new EWM retains the advantages of WEAM, and it allows for damage identification to be completed more efficiently, increasing the precision by approximately 0.11%, and significantly reducing the computing time required to obtain results by 5.67 times.
Physical and Chemical Methods to Assess Performance of TPO-Modified Asphalt Binder
Authors: Herndon Rebecca M. , Balasubramanian Jay , Woelk Klaus , Abdelrahman Magdy
Published: April 2024

The demand for effective asphalt additives is growing as road infrastructure ages and more sustainable pavement solutions are needed. Tire pyrolysis oil (TPO) is an example material that has been gaining attention as a potential asphalt additive. While physical performance grade (PG) temperatures are the predominant performance requirements for asphalt binders, chemical properties are also significant in the evaluation of asphalt performance. There is a need to chemically characterize the aging of asphalt binders modified with TPO and link chemical changes in binder components to binder performance. This study compares 2%, 4%, and 8% TPO and asphalt binder blends via dynamic shear rheometry (DSR), Fourier-transform infrared (FTIR) spectroscopy, and nuclear magnetic resonance (NMR) relaxometry. The variability in the modified blends was seen by both physical and chemical testing during four different blending times (1, 60, 120, and 240 min). After blending, high and intermediate PGs were determined by physical testing. The 8% TPO blend reduced the high PG of the binder from 64 °C to 58 °C. This effect was confirmed by chemical testing through changes in carbonyl indices and NMR relaxation times. With more oil present in the binder matrix, the binder’s resistance to rutting was reduced. While the high PG was hindered, the intermediate PG remained unchanged for all TPO blends. This physical similarity was mirrored in chemical testing. The chemical and physical variability along with the hindrance of the high PG temperature indicate that more treatment may be needed before TPO can be effectively applied to asphalt binders. This study suggests a correlation between physical performance and key chemical indicators.
Optimization and Evaluation of the Weather Research and Forecasting (WRF) Model for Wind Energy Resource Assessment and Mapping in Iran
Authors: Saadatabadi Abbas Ranjbar , Hamzeh Nasim Hossein , Kaskaoutis Dimitris G. , Ghasabi Zahra , Penchah Mohammadreza Mohammadpour , Sotiropoulou Rafaella-Eleni P. , Habibi Maral
Published: April 2024

This study aims to optimize the Weather Research and Forecasting (WRF) model regarding the choice of the best planetary boundary layer (PBL) physical scheme and to evaluate the model’s performance for wind energy assessment and mapping over the Iranian territory. In this initiative, five PBL and surface layer parameterization schemes were tested, and their performance was evaluated via comparison with observational wind data. The study used two-way nesting domains with spatial resolutions of 15 km and 5 km to represent atmospheric circulation patterns affecting the study area. Additionally, a seventeen-year simulation (2004–2020) was conducted, producing wind datasets for the entire Iranian territory. The accuracy of the WRF model was assessed by comparing its results with observations from multiple sites and with the high-resolution Global Wind Atlas. Statistical parameters and wind power density were calculated from the simulated data and compared with observations to evaluate wind energy potential at specific sites. The model’s performance was sensitive to the horizontal resolution of the terrain data, with weaker simulations for wind speeds below 3 m/s and above 10 m/s. The results confirm that the WRF model provides reliable wind speed data for realistic wind energy assessment studies in Iran. The model-generated wind resource map identifies areas with high wind (wind speed > 5.6 m/s) potential that are currently without wind farms or Aeolic parks for exploitation of the wind energy potential. The Sistan Basin in eastern Iran was identified as the area with the highest wind power density, while areas west of the Zagros Mountains and in southwest Iran showed high aeolian potential during summer. A novelty of this research is the application of the WRF model in an area characterized by high topographical complexities and specific geographical features. The results provide practical solutions and valuable insights for industry stakeholders, facilitating informed decision making, reducing uncertainties, and promoting the effective utilization of wind energy resources in the region.
Comparison of CAD Software for Designing Cellular Structures for Additive Manufacturing
Authors: Geyer Sebastian , Hölzl Christian
Published: April 2024

Additive manufacturing (AM) technologies provide significant design freedom, which is highly desirable in today’s fast-paced product design processes. However, most of the parametric CAD software tools used today do not fully utilize this potential for freedom of form and design. Design mechanisms, such as topology optimization (TO), generative design (GD), and lattice structures, are available on the market to help designers minimize weight and material cost while maximizing the stiffness and flexibility of planned designs. This paper proposes a benchmarking approach for designers and engineers to select a suitable software tool for lattice structure generation for their specific applications. The approach includes preselecting software tools based on a weighted point evaluation of seven significant criteria. The tools are then evaluated based on key metrics such as computing time and file size of exported structures, as well as the following six distinctive attributes: usability, reliability, availability, performance, support, and cost. The evaluation process considers a total of 32 defined features. The investigation produced clear recommendations regarding overall performance, reliability, and user experience. The findings indicate that the option of a comprehensive support offering, as well as the initial and operational costs, are significant drivers in the decision-making process.
Development of Heart Simulator and Analysis of Valve Dysfunction in Tricuspid Regurgitation
Authors: Joo Hwan Yi , Ahn Seokyoung , Jung Su-Jin , Chon Min-Ku
Published: April 2024

Tricuspid regurgitation (TR) resulting from valve abnormalities necessitates precise diagnostic tools and interventions. We employed a simulated heart movement device to examine the performance of heart valve functions and analyzed internal pressure changes to provide a quantitative guide for TR treatment. We developed a simulator capable of replicating the flow profile, mimicking natural heart movements, with sensors installed for measuring internal pressure changes. We conducted an ex vivo experiment on a porcine heart to assess tricuspid valve functionality. An endoscope was installed, with a sensor and endoscopic images to detect abnormalities. TR became evident when the heart rate spectrum exceeded an average of 85.2 bpm (standard deviation, 1.3 bpm) and showed an amplitude higher than an average of 12.3 mmHg (standard deviation, 3.2 mmHg). This critical threshold consistently indicated TR onset. The application of the Pivot-TR attenuated this specific spectral area. We confirmed TR disappearance by reducing the intensity of the Tricuspid Regurgitation Generator or employing the Pivot-TR. The Pivot-TR’s ability to attenuate specific spectral areas associated with TR onset and its effectiveness in restoring normal heart functionality has implications for managing and treating TR, particularly that resulting from age-related structural changes in the heart.
Reliability Estimation for Burr XII Distribution under the Weighted Q-Symmetric Entropy Loss Function
Authors: He Kexin , Gui Wenhao
Published: April 2024

Considering that the choice of loss function plays a significant role in the derivation of Bayesian estimators, we propose a novel asymmetric loss function named the weighted Q-symmetric entropy loss for computing the estimates of the parameter and reliability function of the Burr XII distribution. This paper covers the classical maximum-likelihood, uniformly minimum-variance unbiased, and Bayesian estimation methods under the squared error loss, general entropy loss, Q-symmetric entropy loss, and new loss functions. Through Monte Carlo simulation, the respective performances of the considered estimators for the reliability function are evaluated, indicating that the Bayesian estimator under the new loss function is more efficient than those under other loss functions. Finally, a real data set is used to demonstrate the practicality of the presented estimators.
Toxic and Environmental Effects of Neonicotinoid Based Insecticides
Authors: Shareefdeen Zarook , Elkamel Ali
Published: April 2024

The insecticide known as neonicotinoid has negative impacts on the ecosystem, human health, and the environment; specifically, its effects on the relationship between crop yields and the death rate of natural pollinators, such as bees, affect food security. The active ingredients in neonicotinoids include imidacloprid, clothianidin, thiamethoxam, acetamiprid, sulfoxaflor, and thiacloprid, which are sold under various trade names. For many of the components of these toxic insecticides, patents have been expired; however, farmers and consumers who continue to use these chemicals are unaware of the products’ toxicity and the environmental effects they have. Thus, agricultural industries are required to consider diverse methods to minimize neonicotinoid use in farming operations and move away from the current prevailing methods. In this short review, the negative effects of neonicotinoid use; the toxic components, health effects, and environmental regulations of neonicotinoids; and sustainable methods to minimize their use are examined.