bluuebunny/crossref_metadata_2025 huggingface dataset:
AtZAT10/STZ1 improves drought tolerance and increases fiber yield in cotton
Authors: Qin Lixia , He Hehe , Yang Liqun , Zhang Huanyang , Li Jing , Zhu Yonghong , Xu Jianguo , Jiao Gaili , Xiang Chengbin , Wang Chuangyun , Wu Shenjie
Published: October 2024

Drought poses a significant challenge to global crop productivity, necessitating innovative approaches to bolster plant resilience. Leveraging transgenic technology to bolster drought tolerance in crops emerges as a promising strategy for addressing the demands of a rapidly growing global populace. AtZAT10/STZ1, a C2H2-type zinc finger protein transcription factor has shown to significantly improve Arabidopsis’ tolerance to various abiotic stresses. In this study, we reports that AtSTZ1 confers notable drought resistance in upland cotton (Gossypium hirsutum), amplifying cotton fiber yield under varying conditions, including irrigated and water-limited environments, in field trials. Notably, AtSTZ1-overexpressing transgenic cotton showcases enhanced drought resilience across critical growth stages, including seed germination, seedling establishment, and reproductive phases. Morphological analysis reveals an expanded root system characterized by an elongated taproot system, increased lateral roots, augmented root biomass, and enlarged cell dimensions from transgenic cotton plants. Additionally, higher contents of proline, chlorophyll, soluble sugars, and enhanced ROS-scavenging enzyme activities are observed in leaves of transgenic plants subjected to drought, underscoring improved physiological adaptations. Furthermore, transgenic lines exhibit heightened photosynthetic rate, increased water use efficiency, and larger stomatal and epidermal cell sizes, coupled with a decline in leaf stomatal conductance and density, as well as diminished transpiration rates compared to the wild type counterparts. Transcriptome profiling unveils 106 differentially expressed genes in transgenic cotton leaves post-drought treatment, including protein kinases, transcription factors, aquaporins, and heat shock proteins, indicative of an orchestrated stress response. Collectively, these findings underscore the capacity of AtSTZ1 to augment the expression of abiotic stress-related genes in cotton following drought conditions, thus presenting a compelling candidate for genetic manipulation aimed at enhancing crop resilience.
Effect of an early music intervention on emotional and neurodevelopmental outcomes of preterm infants at 12 and 24 months
Authors: Filippa Manuela , Lordier Lara , Lejeune Fleur , De Almeida Joana Sa , Hüppi Petra Susan , Barcos-Munoz Francisca , Monaci Maria Grazia , Borradori-Tolsa Cristina
Published: October 2024

Background

Few studies have found long-term effects of early musical environmental enrichment in the NICU on preterm infant’s development. This study examines how early music enrichment affects emotional development and effortful control abilities in 12- and 24-month-old very preterm (VPT) infants.

Methods

One hundred nineteen newborns were recruited, including 83 VPTs and 36 full-term (FT) infants. The VPT infants were randomly assigned to the music intervention (44 VPT-Music) or control (39 VPT-control) groups. VPT-Music infants listened specifically designed music intervention from the 33rd week of gestation until hospital discharge. At 12 and 24 months, children were clinically evaluated using the Bayley-III Scales of Infant and Toddler Development and the Laboratory Temperament Assessment Battery, and at 24 months, with 3 additional episodes of the Effortful Control Battery.

Results and discussion

Our analysis showed that during a fear eliciting task, the VPT-Music group expressed lower level of fear reactivity and higher positive motor actions than VPT-controls and FT infants. At 24 months, the VPT-music group had lower scores for negative motor actions in the joy task, compared to both VPT-control and FT groups. In addition, both FT and VPT-music had higher scores of sustained attention compared to VPT-controls, but the contrasts were not significant. No significant effects on mental, language and motor outcomes were identified and for all three dimensions of the ECBQ.

Conclusion

The present study suggests that an early music intervention in the NICU might influence preterm children’s emotional processing at 12 and 24 months. Limitations and suggestions for future research are highlighted.
The association between mindfulness, resilience, and academic achievement of pharmacy students in Saudi Arabia
Authors: Almanasef Mona , Almaghaslah Dalia
Published: October 2024

Background

Literature suggests that mindfulness and resilience positively impact academic performance. This study was conducted to assess mindfulness and resilience levels among pharmacy students. The study also aimed to explore the relationship between mindfulness and resilience, as well as their effects on GPA, which serves as an indicator of academic performance.

Methods

The study utilized the Five Facet Mindfulness Questionnaire (FFMQ), a validated tool consisting of 39 items across five domains: observing, describing, acting with awareness, non-judging of inner experience, and non-reactivity. Additionally, the Brief Resilience Scale (BRS), a validated questionnaire with 6 items using a 5-point Likert scale, was employed to assess resilience.

Results

The average scores obtained from the mindfulness and resilience scales were 3.00 (SD = 0.36) and 3.00 (SD = 0.65), indicating intermediate levels of resilience and mindfulness, respectively. No statistically significant differences were found in mindfulness scores between male and female students, or between students with and without a previous diagnosis of mental health issues. However, a statistically significant higher level of resilience was observed among students who have never been diagnosed with mental health issues compared to those who reported a previous diagnosis. However, females scored significantly higher in the observing domain of mindfulness compared to males, while males scored significantly higher in the acting with awareness domain. Students who had never been diagnosed with mental health issues scored significantly higher in acting with awareness. A significant positive association was found between resilience and mindfulness scores of the students (r = 0.45, p < 0.001). However, significant positive correlation was found between resilience and describing as well as acting with awareness mindfulness, p < 0.05. Assessing the association between student GPA and mindfulness as well as resilience scores showed a weak association.

Discussion and conclusion

Tailored interventions and programs could be implemented to foster resilience, enhance students’ ability to cope, and equip them with tools to bounce back in the face of adversity. Further research could explore other factors that might influence the strength of the relationships between mindfulness and resilience, and student GPA.
The relationship between square dance exercise and quality of life in middle-aged and older adults: chain mediated effects of negative emotions and attitudes toward aging
Authors: Ouyang Ting , Qu Yujia , Du Xinyu , Fan Ming , Wang Yan
Published: October 2024

Background

With the continuous development of society and the economy, population aging has become an inevitable global development trend, triggering a series of social problems and having a more serious impact on the physical and mental health of middle-aged and older adults. Physical exercise is one of the key factors for improving the overall health and quality of life of individuals, but the role of square dance exercise and the quality of life of middle-aged and older adults have not yet been clarified.

Purpose

This study aimed to investigate the associations between square dance exercise and the quality of life of middle-aged and older adults and its mechanism of action, including its effects on physical health and mental health, with negative emotions and attitudes towards aging as mediating variables.

Method

This study adopted a cross-sectional research method, using the Physical Activity Rating Scale (PARS-3), Depression-Anxiety-Stress Scale in Simplified Chinese (DASS-21), Attitudes toward Aging Questionnaire (AAQ), and 12-item Short Form of the Health Survey (SF-12), and launched an online survey on the QuestionStar platform from August–September 2023 for 4,636 middle and older adults aged 45–74 years. The survey results were analyzed via Pearson correlation analysis via SPSS 27.0 and structural equation modeling analysis via Mplus 8.3 to test the model fit and mediation effects.

Results

Square dance exercise was significantly correlated with negative emotions, attitudes towards aging, and quality of life (r = −0.042–0.878, p < 0.01). Square dance exercise significantly and positively predicted quality of life (β = 0. 202, p < 0.001). Negative emotions and attitudes towards aging are chain mediators of square dance exercise and quality of life, and the mediation consists of three pathways: separate mediation of negative emotions, separate mediation of attitudes towards aging, and chain mediation of negative emotions-attitudes towards aging.

Conclusion

This study is dedicated to deepening the scientific role of how square dancing, as a health-promoting activity, can optimize the quality of life of middle-aged and older adults through the mediating mechanism of negative emotion alleviation and positive attitudes towards aging and then optimize the quality of life of middle-aged and older adults. This process not only enriches the theoretical framework of the complex interactions between psychosocial factors and healthy aging but also provides a solid empirical foundation for the development of health intervention strategies aimed at enhancing the overall well-being and quality of life of the middle-aged and older adults.
Different sources of alfalfa hay alter the composition of rumen microbiota in mid-lactation Holstein cows without affecting production performance
Authors: La Shaokai , Li Hao , Zhang Yan , Abaidullah Muhammad , Niu Jiakuan , Gao Zimin , Liu Boshuai , Ma Sen , Cui Yalei , Li Defeng , Shi Yinghua
Published: October 2024

Alfalfa hay is a commonly used and important feed ingredient in dairy production. To better expand the alfalfa supply market, it is of great significance to explore the impact of alfalfa hay from different sources on dairy cow production performance. This study compared the effects of imported alfalfa hay from America (AAH) and Spain (SAH) on lactation performance and rumen microbiota of cows. Three hundred and sixty healthy mid-lactation Holstein cows with similar body weight, milk yield, and parity were randomly divided into two groups fed diets based on AAH or SAH for a 70-day experimental period. Each group was composed of four pens, with 45 cows in each pen. Daily records were kept for MY per cow and dry matter intake per pen. Twelve randomly selected cows per group were sampled to collect milk, feces, rumen fluid, and blood. The findings revealed no significant differences between the two groups in terms of production performance, nutrient apparent digestibility, serum biochemical indices, or rumen fermentation parameters. However, rumen microbial composition differed significantly between the two groups of cows based on β-diversity. On the genus level, the relative abundance of Prevotella, Succinivibrionaceae_UCG-002 increased while that of NK4A214_group, Ruminococcus, norank_f_F082 and Lachnospiraceae_NK3A20_group decreased in the SAH group compared with AAH group. There was no significant correlation between these core differential bacteria and the molar proportions of acetate and propionate, the concentration of total volatile fatty acids, and milk yield. In conclusion, the feeding effects of SAH were similar to those of AAH. These findings provided a reference for the application of alfalfa hay from different sources and for the improvement of the economic benefit of dairy farms.
Patterns of animal rabies in the Nizhny Novgorod region of Russia (2012–2022): the analysis of risk factors
Authors: Zakharova Olga I. , Liskova Elena A.
Published: October 2024

Introduction

Animal rabies is a viral disease that poses a significant threat to domestic and wild animal populations, with devastating consequences for animal health and human life. Understanding and assessing the risk factors associated with the transmission and persistence of the rabies virus in wild and domestic animal populations is crucial for developing effective strategies to control and mitigate cases. Studies of the spatial and temporal distribution of rabies cases in the Nizhny Novgorod region during 2012-2022 provided epidemiological evidence of the circulation of infection between animals in the presence of vaccination. Among the wild animals in the area, red foxes play a major role in the spread of rabies, accounting for 96.4% of all wild animal cases.

Methods

We used spatiotemporal cluster analysis and a negative binomial regression algorithm to study the relationships between animal rabies burden by municipality and a series of environmental and sociodemographic factors.

Results

The spatiotemporal cluster analysis suggests the concentration of wild animal rabies cases in the areas of high fox population density and insufficient vaccination rates. The regression models showed satisfactory performance in explaining the observed distribution of rabies in different animals (R2 = 0.71, 0.76, and 0.79 in the models for wild, domestic and all animals respectively), with rabies vaccination coverage and fox population density being among the main risk factors.

Conclusion

We believe that this study can provide valuable information for a better understanding of the geographical and temporal patterns of rabies distribution in different animal species, and will provide a basis for the development of density-dependent planning of vaccination campaigns.
Pathogenicity potential of enterococci isolated from a Veterinary Biological Isolation and Containment Unit
Authors: Geraldes Catarina , Araújo Catarina , Pinheiro Ana Catarina , Afonso Mónica , Carapeto Sandra , Verdial Cláudia , Cunha Eva , Abreu Raquel , Tavares Luís , Chambel Lélia , Gil Solange , Oliveira Manuela
Published: October 2024

Introduction

Enterococcus are considered an important genus in terms of Hospital-Acquired Infections (HAIs), which means that their characterization regarding resistance and virulence profiles in the hospital environment is of extreme importance. This article addresses this issue through the characterization of enterococci collected from a Veterinary Biological Isolation and Containment Unit (BICU).

Methods

A total of 73 isolates, collected from different surfaces of a Veterinary BICU, were identified as Enterococcus through PCR at species level, after which 34 isolates were selected as representatives using (GTG)5 fingerprinting. These isolates were further characterized phenotypically in terms of antimicrobial resistance through disk diffusion and of virulence factors’ expression.

Results

The majority of the enterococci isolated presented resistance to erythromycin (79.4%), ampicillin (73.5%), amoxicillin-clavulanic acid (70.6%), tetracycline (67.6%), ciprofloxacin (58.8%) and levofloxacin (50.0%), and were able to produce hemolysin (88.2%) and biofilm (82.3%). Furthermore, in terms of pathogenicity, three isolates (8.8%) were classified as high threats and two (5.9%) as moderate threats.

Discussion

The degree of resistance, production of virulence factors and the percentage of isolates classified as moderate or high threat means that a constant vigilance of such strains in veterinary units, but also in clinics and hospitals in general, is an important tool in terms of infection prevention and consequent reduction of HAIs.
Szájüregi leukoplakiák előfordulása és hisztopatológiai vizsgálata
Authors: Bukovszky Botond , Simonffy László , Gyulai-Gaál Szabolcs , Jákob Noémi , Dobó-Nagy Csaba
Published: October 2024

Bevezetés: A szájüregi potenciálisan rosszindulatú elváltozások közül az egyik leggyakoribb lézió a leukoplakia. Fontostisztázni, hogy a klinikailag leukoplakiának diagnosztizált esetek mögött milyen hisztopatológiai diagnózis áll, amely döntőenmeghatározza a prognózist és a szükséges terápiát.Anyag és módszer: 2021. április és 2024. április között 75 szájüregi leukoplakiával diagnosztizált betegnél történt szövettanimintavétel a Semmelweis Egyetem Orális Diagnosztikai Tanszék Dento-alveolaris Sebészeti Osztályán. A szövettaniminták hisztopatológiai elemzése hematoxilin-eozin festett minták alapján történt a Semmelweis Egyetem Patológiaiés Kísérleti Rákkutató Intézetében, bizonyos esetekben immunhisztokémiai vizsgálattal kiegészítve.Eredmények és megbeszélés: A 75 betegnél a leukoplakiák előfordulási helye, gyakoriság szerinti csökkenő sorrendben:gingiva/fogatlan állcsontgerinc (n = 26), bucca (n = 16), szájfenék (n = 12), nyelv (n = 11), szájpad (n = 6), ajak (n = 2).Ritka esetben multifocalis megjelenés is előfordult (n = 2). A szövettani mintavétel általában próba excisio volt, nem történtmeg az elváltozás teljes kimetszése. A hisztopatológiai eredmény 51 esetben hyperkeratosis (dysplasia nélkül),19 esetben enyhe dysplasia, 5 esetben mérsékelt dysplasia volt. Súlyos dysplasiás eset nem volt. Klinikai homogenitásttekintve 61 esetben homogén és 14 esetben non-homogén volt a leukoplakia. A nem dysplasiás és dysplasiás betegcsoportokattekintve szignifikánsan gyakrabban állt dysplasia az elváltozás mögött, amennyiben klinikailag non-homogénmegjelenést mutatott a leukoplakia (p = 0,0088). A vizsgált beteganyagon 75 beteg közül 32 dohányzik, 43 nem dohányzik.Eredményeink alapján a dohányzás nem volt szignifikáns hatással a dysplasia jelenlétére és súlyosságára.A betegek követése folyamatos (félévenkénti kontrollvizsgálat). Átlagos követési idő: 17,4 hónap (tartomány: 1–38 hónap).Következtetés: A szájüregi leukoplakiával diagnosztizált betegek esetében szövettani mintavétel szükséges a hisztopatológiaidiagnózis felállításához, valamint hosszú távú követés javasolt a késői rosszindulatú átalakulás elkerülésére.
An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems
Authors: Bairooz Jalal Jabbar , Mardukhi Farhad
Published: October 2024

Metaheuristic algorithms have gained popularity in the past decade due to their remarkable ability to address various optimization challenges. Among these, the JAYA algorithm has emerged as a recent contender that demonstrates strong performance across different optimization problems, largely attributed to its simplicity. However, real-world problems have become increasingly complex in today’s era, creating a demand for more robust and effective solutions to tackle these intricate challenges and achieve outstanding results. This article proposes an enhanced JAYA (EJAYA) method that addresses its inherent shortcomings, resulting in improved convergence and search capabilities when dealing with diverse problems. The current study evaluates the performance of the proposed optimization methods on both continuous and discontinuous problems. Initially, EJAYA is applied to solve 20 prominent test functions and is validated by comparison with other contemporary algorithms in the literature, including moth–flame optimization, particle swarm optimization, the dragonfly algorithm, and the sine–cosine algorithm. The effectiveness of the proposed approach in discrete scenarios is tested using feature selection and compared to existing optimization strategies. Evaluations across various scenarios demonstrate that the proposed enhancements significantly improve the JAYA algorithm’s performance, facilitating escape from local minima, achieving faster convergence, and expanding the search capabilities.
Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning
Authors: Gouton Pierre , Ayikpa Kacoutchy Jean , Mamadou Diarra
Published: October 2024

Eight-band one-shot MSFA (multispectral filter array) cameras are innovative technologies used to capture multispectral images by capturing multiple spectral bands simultaneously. They thus make it possible to collect detailed information on the spectral properties of the observed scenes economically. These cameras are widely used for object detection, material analysis, and agronomy. The evolution of one-shot MSFA cameras from 8 to 32 bands makes obtaining much more detailed spectral data possible, which is crucial for applications requiring delicate and precise analysis of the spectral properties of the observed scenes. Our study aims to develop models based on deep learning to estimate the spectral response of this type of camera and provide images close to the spectral properties of objects. First, we prepare our experiment data by projecting them to reflect the characteristics of our camera. Next, we harness the power of deep super-resolution neural networks, such as very deep super-resolution (VDSR), Laplacian pyramid super-resolution networks (LapSRN), and deeply recursive convolutional networks (DRCN), which we adapt to approximate the spectral response. These models learn the complex relationship between 8-band multispectral data from the camera and 31-band multispectral data from the multi-object database, enabling accurate and efficient conversion. Finally, we evaluate the images’ quality using metrics such as loss function, PSNR, and SSIM. The model evaluation revealed that DRCN outperforms others in crucial performance. DRCN achieved the lowest loss with 0.0047 and stood out in image quality metrics, with a PSNR of 25.5059, SSIM of 0.8355, and SAM of 0.13215, indicating better preservation of details and textures. Additionally, DRCN showed the lowest RMSE 0.05849 and MAE 0.0415 values, confirming its ability to minimize reconstruction errors more effectively than VDSR and LapSRN.
A Non-Contact AI-Based Approach to Multi-Failure Detection in Avionic Systems
Authors: Liu Chengxin , Ferlauto Michele , Yuan Haiwen
Published: October 2024

The increasing electrification and integration of advanced controls in modern aircraft designs have significantly raised the number and complexity of installed printed circuit boards (PCBs), posing new challenges for efficient maintenance and rapid failure detection. Despite self-diagnostic features in current avionics systems, circuit damage and multiple simultaneous failures may arise, compromising safety and diagnostic accuracy. To address these challenges, this paper aims to develop a fast, accurate, and non-destructive, multi-failure diagnosis algorithm for PCBs. The proposed method combines a self-attention mechanism with an adaptive graph convolutional neural network to enhance diagnostic precision. A convolutional neural network with residual connections extracts features from scalar magnetic field data, ensuring robust input diversity. The model was tested on a typical dual-phase amplitude boosting circuit with up to four different simultaneous failures, achieving the experimental results of 99.08%, 98.50%, 98.78%, 98.01%, 98.93%, 98.25%, 97.03%, and 99.77% across metrics including overall precision, per-class precision, overall recall, per-class recall, overall F1 measure, and per-class F1 measure. The results demonstrated its effectiveness and feasibility in diagnosing complex PCBs with multiple failures, indicating the algorithm’s potential to improve failure diagnosis performance and offer a promising PCB diagnosis solution in aerospace applications.
Performance of Small-Scale Hermetic Storage Systems Under Periodic Access
Authors: Tatum Jaden , Shah Ajay
Published: October 2024

This study characterizes the grain management performance of a novel integrated grain drying and storage system (iGDSS) adapted from 208 L drums to combat postharvest loss in developing countries through providing in situ mechanized drying and hermetic storage. The six-month storage trials of 14% moisture content maize compared different access mechanisms and two levels of pest pressure: 0 and 10 maize weevils/kg grain. This experiment allowed comparisons of differential oxygen consumption rates in small-scale hermetic systems with and without storage pests, which has not been widely reported in the literature. The iGDSS system was found to maintain grain quality parameters in dry grains with and without storage pests. After six months of storage, the results demonstrated no statistically significant difference in the moisture content, test weight, germination, proportion of broken and damaged kernels, and presence of colony-forming units between inoculated and non-inoculated systems. The iGDSS was also found to maintain oxygen intrusion rates of 0.10–0.13% O2/day, below recommended thresholds of 0.15% required to maintain benefits of modified atmosphere storage. These results indicate that the iGDSS can provide safe and reliable grain storage to smallholder farmers in developing countries, and that the drying functions of iGDSS can promote outcomes in hermetic storage.
Semantic Segmentation Model-Based Boundary Line Recognition Method for Wheat Harvesting
Authors: Wang Qian , Qin Wuchang , Liu Mengnan , Zhao Junjie , Zhu Qingzhen , Yin Yanxin
Published: October 2024

The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and wheat stubble color interference in the field, make it challenging to identify the wheat harvest boundary line accurately and quickly. Therefore, this paper proposes a harvest boundary line recognition model for wheat harvesting based on the MV3_DeepLabV3+ network framework, which can quickly and accurately complete the identification in complex environments. The model uses the lightweight MobileNetV3_Large as the backbone network and the LeakyReLU activation function to avoid the neural death problem. Depth-separable convolution is introduced into Atrous Spatial Pyramid Pooling (ASPP) to reduce the complexity of network parameters. The cubic B-spline curve-fitting method extracts the wheat harvesting boundary line. A prototype harvester for wheat harvesting boundary recognition was built, and field tests were conducted. The test results show that the wheat harvest boundary line recognition model proposed in this paper achieves a segmentation accuracy of 98.04% for unharvested wheat regions in complex environments, with an IoU of 95.02%. When the combine harvester travels at 0~1.5 m/s, the normal speed for operation, the average processing time and pixel error for a single image are 0.15 s and 7.3 pixels, respectively. This method could achieve high recognition accuracy and fast recognition speed. This paper provides a practical reference for the autonomous harvesting operation of a combine harvester.
Potential Impacts of Diversification of Food Retail Working Hours on Consumer Behaviour and the Benefits for Local Producers in Latvia
Authors: Proskina Liga , Janmere Lana , Cerina Sallija , Pilvere Irina , Pilvere Aija , Nipers Aleksejs , Proskina Daniela
Published: October 2024

The capability of large food retail chains to respond quickly to changes in consumer behaviour and their dominant market position affects all food market players and often conflicts with the interests of national food producers, which can reduce the presence of locally sourced products in the food product mix in the country. Accordingly, the present research aims to identify the impacts of the diversification of opening hours of food supermarkets on consumer shopping habits and the implications for creating an advantage for small and medium agri-food producers in selling their products. The research applied a quantitative approach to identify the main trends in society (n = 2738), with a survey including 31 variables to quantify consumer behaviour, values, and opinions and seven socio-demographic variables. If a decision was made in Latvia to close grocery shops on Sundays or reduce their opening hours on weekends, 85% of consumers indicated that they would be unlikely to change their usual shopping location and would plan to shop at a supermarket on other days. The choice between farmers’ markets and local food shops on Sundays would be made by 45% of consumers, with more than half (53%) of them shopping at local food shops at least a few times a month. The research uniquely investigated the impact of reducing supermarket opening hours on the competitive advantage of small and medium-sized agri-food producers. The findings revealed that reducing supermarket opening hours does not confer a competitive advantage to the producers or significantly shift consumer preferences towards their products.
Fruit Distribution Density Estimation in YOLO-Detected Strawberry Images: A Kernel Density and Nearest Neighbor Analysis Approach
Authors: Jiang Lili , Wang Yunfei , Wu Chong , Wu Haibin
Published: October 2024

Precise information on strawberry fruit distribution is of significant importance for optimizing planting density and formulating harvesting strategies. This study applied a combined analysis of kernel density estimation and nearest neighbor techniques to estimate fruit distribution density from YOLOdetected strawberry images. Initially, an improved yolov8n strawberry object detection model was employed to obtain the coordinates of the fruit centers in the images. The results indicated that the improved model achieved an accuracy of 94.7% with an mAP@0.5~0.95 of 87.3%. The relative error between the predicted and annotated coordinates ranged from 0.002 to 0.02, demonstrating high consistency between the model predictions and the annotated results. Subsequently, based on the strawberry center coordinates, the kernel density estimation algorithm was used to estimate the distribution density in the strawberry images. The results showed that with a bandwidth of 200, the kernel density estimation accurately reflected the actual strawberry density distribution, ensuring that all center points in high-density regions were consistently identified and delineated. Finally, to refine the strawberry distribution information, a comprehensive method based on nearest neighbor analysis was adopted, achieving target area segmentation and regional density estimation in the strawberry images. Experimental results demonstrated that when the distance threshold ϵ was set to 600 pixels, the correct grouping rate exceeded 94%, and the regional density estimation results indicated a significant positive correlation between the number of fruits and regional density. This study provides scientific evidence for optimizing strawberry planting density and formulating harvesting sequences, contributing to improved yield, harvesting efficiency, and reduced fruit damage. In future research, this study will further explore dynamic models that link fruit distribution density, planting density, and fruit growth status.
Mineral Composition of Fifteen Species of Asteraceae Family Growing in the Republic of Moldova Using Neutron Activation Analysis
Authors: Ciocarlan Alexandru , Shvetsova Margarita , Zinicovscaia Inga , Chaligava Omari , Grozdov Dmitrii , Aricu Aculina , Ciocarlan Nina
Published: October 2024

Members of Asteraceae family have properties enabling their application for medical purposes. The major- and microelement content in 15 medicinal species of the Asteraceae family growing in the National Botanical Garden, Republic of Moldova was determined by instrumental neutron activation analysis. A total of 31 chemical elements, Na, Mg, Al, Cl, K, Ca, Sc, V, Cr, Mn, Fe, Co, Ni, Zn, As, Se, Br, Rb, Sr, Mo, Sb, Cs, Ba, La, Ce, Sm, Tb, Hf, Ta, Th and U, were identified in herbal samples. Potassium was found to be the most abundant major element; its content in plants ranged from 20,700 mg/kg in Artemisia lerchiana to 58,000 mg/kg in Matricaria recutita. The content of other major elements existed in the following ranges: Ca from 4700 to 14,200 mg/kg and Mg from 1710 to 3870 mg/kg. The maximum content of Mg, K and Ca in analyzed plants was higher compared to data presented in the literature. Among essential microelements the most abundant were Fe (83–910 mg/kg), Mn (23–150 mg/kg) and Zn (27–76 mg/kg). The daily intake of metal and the health risk index for selected elements (As, Ni, Sb, V, Mn, Cr, Co, U, Sr, Al, Fe, and Zn) were calculated. Health risk indexes were obtained for V and Co in Calendula officinalis, Tanacetum balsamita, Achillea clypeolata, Artemisia balchanorum, Artemisia lerchiana, Helichrysum arenarium, and Matricaria recutita. The principal component analysis showed three associations of elements, which can be defined as physiological, geological and anthropogenic sources of elements.
Understanding the Feeding Behavior and Identifying the Plant Parts Preferences of Fall Armyworm on Peanut Seedlings
Authors: Cheng Yuanyuan , Liu Lulu , Li Hongmei , Yang Xianming , Shang Suqin
Published: October 2024

Fall armyworm (FAW), Spodoptera frugiperda, has posed a serious threat to global food security since its discovery in Africa in 2016. Intercropping peanuts with maize is a very common cultivation practice, which can result in a high possibility of peanut damage by FAW. Our study investigated the feeding behavior, plant part preferences, and damage symptoms of FAW larvae on peanuts throughout the larval period, considering changes in population densities and the passage of time over the number of investigations. The results indicated that FAW larvae frequently inhabited peanut leaves, particularly the undersides of the leaves. Larvae moved from the leaves to the soil in the seedling pot to complete development. Furthermore, FAW larvae tended to feed on peanut leaves rather than stems regardless of population densities. Based on the damage symptoms, the feeding preferences of FAW larvae tended to be heart leaves, followed by mature leaves and stems. The most frequent damage symptoms caused by FAW to peanuts were “window panes”, followed by “leafless”. This study provides a reference for the integrated management of FAW in peanut fields.
Estimation of the Weight and Volume of Lime (Citrus aurantifolia (Christm.) Swingle) Fruit Using Computer Vision Based on Traditional Machine Learning and Deep Learning
Authors: Onmankhong Jiraporn , Poonpakdee Pasu , Lapcharoensuk Ravipat
Published: October 2024

The post-harvest process is important to increasing the market value of limes and requires focus. During this process, limes are graded and categorized based on size, weight, and volume. Therefore, identifying efficient means of estimating these properties is very important and remains an open research area. This study applies the concept of computer vision based on traditional machine learning algorithms (partial least square regression (PLS), epsilon-support vector regression (ε-SVR), decision tree (DT), random forest (RF), adaptive boosting (AB), gradient boosting (GB), Bagging meta-estimator (BME), and extremely randomized trees (ERTs)) and pre-trained deep learning (InceptionV3, MoblieNetV2, ResNet50, and VGG-16) for estimating the weight and volume of limes. Our findings showed that the BME and ResNet50 could yield the highest performance for estimating the weight and volume of limes. The BME produced Rtest2 values of 0.954 and 0.882 for weight and volume, respectively, while the Rtest2 values of ResNet50 models were between 0.951 and 0.957 for weight and volume, respectively. This study concluded that computer vision based on both traditional machine learning and deep learning could be used to estimate the weight and volume of limes. The approach proposed in this study can be adopted for applications related to computer vision in the post-harvest process.
Astaxanthin Supplementation Effects in Right Ventricle of Rats Exposed to Chronic Intermittent Hypobaric Hypoxia
Authors: Pena Eduardo , El Alam Samia , Gonzalez Constanza , Cortés Isaac , Aguilera Diego , Flores Karen , Arriaza Karem
Published: October 2024

In Chile, individuals are commonly exposed to high altitude due to the work shift system, involving days of exposure to high altitude followed by days at sea level over the long term, which can result in chronic intermittent hypobaric hypoxia (CIHH). CIHH can cause high-altitude pulmonary hypertension (HAPH), the principal manifestation of which is right ventricular hypertrophy (RVH), in some cases leading to heart failure and eventually death. Studies have shown the contribution of oxidative stress and inflammation to RVH development. Recently, it was determined that the pigment astaxanthin has high antioxidant capacity and strong anti-inflammatory and cardioprotective effects. Therefore, the aim of this study was to determine the effects of astaxanthin on RVH development in rats subjected to CIHH. Methods: Thirty two male Wistar rats were randomly assigned to the following groups (n = 8 per group): the normoxia with vehicle (NX), normoxia with astaxanthin (NX + AS), chronic intermittent hypobaric hypoxia with vehicle (CIHH), and chronic intermittent hypobaric hypoxia with astaxanthin (CIHH + AS) groups. CIHH was simulated by 2 days in a hypobaric chamber followed by 2 days at sea level for 29 days. Results: Exposure to CIHH induced RVH and increased lipid peroxidation (MDA), Nox2 expression, and SOD activity, however, it decreased pro-IL-1β expression. Astaxanthin restored oxidative stress markers (Nox2 and MDA), increased GPx activity, and decreased RVH compared to CIHH. Conclusion: Astaxanthin alleviates RVH and reduces Nox2 and MDA levels while increasing GPx activity in rats subjected to CIHH. These findings provide new insights of astaxanthin as a new nutraceutical against high-altitude effects.
Effect of Different Salinities on the Biochemical Properties and Meat Quality of Adult Freshwater Drum (Aplodinotus grunniens) During Temporary Rearing
Authors: Chen Wanwen , Miraji Sharifa Mohamed , Tian Yu , Ma Xueyan , Jin Wu , Wen Haibo , Xu Gangchun , Xu Pao , Cheng Hao
Published: October 2024

Salinity is a significant environmental component that affects the physiological state of aquatic species. This study aimed to investigate whether water salinity had an impact on the biochemical properties and meat quality of adult Aplodinotus grunniens during temporary rearing of 7 days. Salinity caused increased osmotic pressure and antioxidant enzyme activities of Aplodinotus grunniens, which were attributed to the increase in the content of alanine and glutamate. It raised the hardness and shear force with an increase in salinity, leading to an increase in water-holding capacity. Salinity enhanced the DHA ratio with a decrease in the atherosclerotic index and thrombosis index. Combined with the increase in flavor amino acids and nucleotides, salinity enhanced the umami taste of Aplodinotus grunniens. These findings suggest that temporary rearing in salinity might be a practical approach to improving the meat quality of adult Aplodinotus grunniens.
Comparative Analysis of Shear Bond Strength in Orthodontic Brackets Between Milled and 3D-Printed Definitive CAD/CAM Restorations
Authors: Aldosari Mohammad A. , Anany Noha M. , Alaqeely Razan , Alsenaidi Jawaher , Sufyan Aref , Talaat Sameh , Bourauel Christoph , Elshazly Tarek M. , Alhotan Abdulaziz
Published: October 2024

Objective: The objective of this study was to evaluate the effect of different surface treatment methods on the shear bond strength (SBS) of metal brackets bonded to two types of CAD/CAM composite restorations: milled and 3D-printed. Materials and Methods: A total of 160 flat-shaped specimens (10 × 10 × 2 mm3) were prepared from four different CAD/CAM composites; two milled (Lava Ultimate™ [LU] and Grandio™ [GR]) and two 3D-printed (Crowntec™ [CT] and C&B Permanent™ [CB]). These specimens underwent thermocycling (5000 cycles at 5–55 °C), then were categorized based on the surface treatment into four groups (n = 10): Group C (control, no surface treatment), Group HF (treated with 9.6% hydrofluoric acid), Group DB (mechanical roughening by a diamond bur), and Group SB (sandblasting using aluminum oxide). Metal brackets were bonded using Transbond XT Primer and universal adhesive, stored in artificial saliva for 24 h, then thermocycled again. Shear bond strength (SBS) was tested using a universal testing machine until bracket debonding occurred. The adhesive remnant index (ARI) was assessed using a stereomicroscope to quantify the residual adhesive following debonding. Result: Regarding material, GR and LU restorations had significantly higher SBS values compared to CT and CB, ranging from 13.90 MPa to 20.35 MPa. Regarding surface treatment, SB and HF groups showed significantly higher SBS values. The ARI scores showed different adhesive modes of failure, with higher instances of scores 0 and 1, which indicate no or minimal adhesive remaining. Conclusions: Both milled and 3D-printed materials had adequate SBS for clinical use, with milled materials showing superior results. Surface treatments like sandblasting and HF significantly improved bond strength, with adhesive failure being common.
Probabilistic Analysis of Low-Emission Hydrogen Production from a Photovoltaic Carport
Authors: Małek Arkadiusz , Dudziak Agnieszka , Caban Jacek , Matijošius Jonas
Published: October 2024

This article presents a 3D model of a yellow hydrogen generation system that uses the electricity produced by a photovoltaic carport. The 3D models of all key system components were collected, and their characteristics were described. Based on the design of the 3D model of the photovoltaic carport, the amount of energy produced monthly was determined. These quantities were then applied to determine the production of low-emission hydrogen. In order to increase the amount of low-emission hydrogen produced, the usage of a stationary energy storage facility was proposed. The Metalog family of probability distributions was adopted to develop a strategic model for low-emission hydrogen production. The hydrogen economy of a company that uses small amounts of hydrogen can be based on such a model. The 3D modeling and calculations show that it is possible to design a compact low-emission hydrogen generation system using rapid prototyping tools, including the photovoltaic carport with an electrolyzer placed in the container and an energy storage facility. This is an effective solution for the climate and energy transition of companies with low hydrogen demand. In the analytical part, the Metalog probability distribution family was employed to determine the amount of monthly energy produced by 6.3 kWp photovoltaic systems located in two European countries: Poland and Italy. Calculating the probability of producing specific amounts of hydrogen in two European countries is an answer to a frequently asked question: In which European countries will the production of low-emission hydrogen from photovoltaic systems be the most profitable? As a result of the calculations, for the analyzed year 2023 in Poland and Italy, specific answers were obtained regarding the probability of monthly energy generation and monthly hydrogen production. Many companies from Poland and Italy are taking part in the European competition to create hydrogen banks. Only those that offer low-emission hydrogen at the lowest prices will receive EU funding.
Prediction Models for the Milling of Heat-Treated Beech Wood Based on the Consumption of Energy
Authors: Koleda Peter , Čuchor Tomáš , Koleda Pavol , Rajko Ľubomír
Published: October 2024

This article is focused mainly on verifying the suitability of data from the experimental milling of heat-treated beech wood and on investigating the effects of the technical and technological parameters of milling on the energy consumption of this process. The independent parameters of the machining process are the cutting speed, feed speed, rake angle, and hydrothermal modification of the experimental wood material. Based on analysis of variance, it can be argued that the cutting speed and rake angle of the tool have the greatest statistically significant effect on energy consumption, while the feed speed has the least influence. The measured data on cutting power during milling were used to build a regression model and validate it, and the most suitable type of model, with a correlation of 87%, is the classification and regression tree, followed by a model created using the random forest method.
Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST
Authors: Li Qingwen , Tang Waifan , Li Zhaobin
Published: October 2024

The Fourth Industrial Revolution, also known as Industry 4.0, which is the intensified digitalization and automation in industry, embraces cyber–physical systems, the Internet of Things (IoT), and artificial intelligence, among others. This study utilizes Fuzzy Integration–Rough Set Theory (FI-RST) analysis to quantify the impacts of the imperative Industry 4.0 technologies for manufacturing firms located in Fujian Province, China, namely, Manufacturing Execution Systems (MES), the Industrial Internet of Things (IIoT), and Additive Manufacturing (AM), on the sustainable development performance of firms. The findings of the study indicate that these technologies greatly improve the effectiveness of the utilization of resources, reduce the costs of operations, and reduce the impact on the environment. In addition, they have a favorable influence on social considerations, such as preserving the well-being of employees and the outcome of training programs. This research work has convincingly provided an underlying strategic adoption of these technologies for sustainability production by raising important insights that could be valuable for industry managers and policymakers, especially those seeking sustainability at the global level.
Optimizing the Scheduling of Teaching Activities in a Faculty
Authors: Diallo Francis Patrick , Tudose Cătălin
Published: October 2024

To maximize resource usage, minimize disputes, and improve academic experience, professors must schedule teaching activities efficiently. This study provides an optimized automated schedule creation technique. The system generates schedules that aim to be conflict-free and efficient, utilizing evolutionary algorithms along with multi-objective optimization. Resource usage, scheduling problems, and faculty/student satisfaction are the goals of the research. The system optimizes scheduling based on room limitations, instructor availability, and student preferences. The project uses system design, model creation, algorithm implementation, and performance analysis to solve the difficult timetable-generating problem. This research should save administrators time, improve academic operations, and improve staff and student academic experiences. Scalability and flexibility allow the system to be used in multiple faculties and incorporate new limits and requirements. This paper presents a complete approach to faculty scheduling, including insights and recommendations for future study and application in educational institutions.
Design of a Compact, Planar, Wideband, Overlapped, Bow-Tie Antenna in a Single Layer with Stable Bi-Directional Radiation Patterns
Authors: Smith Lauryn , Lim Sungkyun
Published: October 2024

In this paper, a planar, bow-tie antenna with an enhanced bandwidth and a bi-directional radiation pattern is proposed. The concept of multi-resonance is applied by implementing the superposition of three different bow-tie components with various radii and flare angles in an overlapped fashion into a single element, resulting in a significant increase in bandwidth. The antenna has an electrical size, kr, of 1.10, calculated at the lowest frequency of operation. The proposed antenna is simulated, and a prototype is fabricated for verification by measurement. The result is a wide −10 dB bandwidth of 80.3% (1.23–2.88 GHz) from the measurements. The antenna also maintains a bi-directional radiation pattern, with a negligible difference between the forward and backward realized gains, within the entire −10 dB bandwidth. The measured realized gain values in the forward and backward directions are between 1.5 dBi and 3.7 dBi over the −10 dB bandwidth. The comparison of the measurement and simulation results shows good agreement.