bluuebunny/crossref_metadata_2025 huggingface dataset:
Factors associated with cognitive flexibility in people with opioid-use disorder: a pilot study
Authors: Regier Paul S. , Macedo de Arruda Thais Costa , Sinko Laura , Teitelman Anne M. , Childress Anna Rose
Published: December 2024

The ability to adapt to changing circumstances has strong survival value. Individuals with substance use disorders tend to get “stuck” over-responding to drug-reward signals and pursuing drugs despite negative consequences. A lack of flexibility may be tied to impairments in neurocognition, including learning, memory, and executive function. However, results are often mixed, potentially due to heterogeneity in factors such as mental health, personality traits, or prior adversity. This study aimed to identify which factors influence neurocognitive variations within the opioid use disorder (OUD) population. Based on prior literature, we hypothesized that individuals with OUD would show deficits (vs. controls) in one or more neurocognitive domains, and that these cognitive difficulties might be greater in individuals with other known contributors to impaired cognition. This pilot project included 32 individuals receiving medication for OUD and 15 non-substance using controls (NSC). Questionnaires assessed addiction and relapse risk factors, such as impulsiveness, social function, depressive symptoms, and childhood adversity. Neurocognitive performance was measured via the Penn Computerized Neurocognitive Battery (P-CNB), including tasks that probe attention, working memory, episodic memory, cognitive flexibility, and complex cognition, and was compared between the OUD and NSC groups. OUD participants (vs. NSCs) exhibited significantly lower performance on the conditional exclusion task (CET) (Accuracy: 1.11 vs. 2.38, p < 0.001) and the n-Back task (NBT) (F1 Scores: 83% vs. 95%, p < 0.001). Impulsiveness, social function, and depressive symptoms were highly inter-related; however, only higher impulsiveness (r = -.48, p = 0.006) and more social impairment (r = -.47, p = 0.007) significantly correlated with decreased CET (but not n-Back) performance. This pilot study suggests that working memory and cognitive flexibility are impaired in people with OUD and that impulsiveness and social function are key factors in cognitive flexibility impairments in people with OUD. These results may offer insights for larger-scale investigations and potential interventions to reduce relapse risk.
Effects of feces storage conditions for host-microbiota screenings in C. elegans
Authors: Caron Laury , Alonzo De la Rosa Claudia Miriam , Diop Khoudia , Miard Stéphanie , Taubert Stefan , Marette André , Picard Frédéric
Published: December 2024

Background and aims

Current research on host-gut microbiota interactions is hindered by almost infinite bacterial combinations depending on intrinsic characteristics, environment, and health status, which prevents large-scale screenings in mammals. For these reasons, the bacterivore model organism C. elegans has been developed to test the effects of gut microbiota extracts from mammals. This study tested whether storage conditions of mouse feces and fecal extracts modify normal C. elegans healthspan.

Methods

Feces from mice were processed for microbiota extraction after collection or after one or twelve months at -80 °C and compared to microbiota extracted six months before and left at room temperature. Extracts were probed for bacterial composition, viability, and nutritional content and tested in synchronized wild-type (strain N2) worms for food preferences and intake, development, fat accumulation, brood size, and maximal lifespan.

Results

Long-term freezing of feces before microbiota extraction modified composition but did not negatively impact subsequent worm development, fat accumulation, reproduction, and maximal lifespan, whereas using samples extracted and left at room temperature after a long period of time resulted in robust avoidance and was detrimental for normal growth.

Conclusions

Using frozen feces to test for impacts of microbiota in C. elegans appears an appropriate method since it did not affect normal biology and healthspan, which supports protocols with already existing feces stored in biobanks for high-throughput phenotype screenings.
Comparison of postoperative pain and stress using a multimodal approach in cats: open vs. laparoscopic-assisted ovariohysterectomy
Authors: Jeong Changwoo , Yi Kangwoo , Yu Yong , Heo Suyoung
Published: December 2024

Introduction

Laparoscopic surgery is increasingly utilized for its minimally invasive nature, leading to reduced postoperative pain and faster recovery. This study aimed to compare postoperative pain, surgical complications, and recovery between laparoscopic-assisted ovariohysterectomy (LAOHE) and open ovariohysterectomy (OHE) in cats.

Methods

A total of 40 healthy female cats were randomly assigned to undergo either LAOHE (n = 20) or OHE (n = 20). Pain scores were assessed using the Glasgow Composite Pain Scale at 1, 4, 8, 12, and 24 h postoperatively. Blood samples were collected to measure cortisol levels as a stress biomarker. Complications were recorded intraoperatively and postoperatively.

Results

Cats in the LAOHE group exhibited significantly lower pain scores compared to the OHE group at 1, 4, and 8 h postoperatively (P < 0.05). Cortisol levels were also significantly lower in the LAOHE group (P < 0.05). There were no significant differences in surgical time or postoperative complications between the two groups.

Discussion

The findings suggest that LAOHE results in reduced postoperative pain and stress in cats compared to OHE, without increasing surgical time or complications. LAOHE may be a preferable technique for elective ovariohysterectomy in cats.
GAGAN: Enhancing Image Generation Through Hybrid Optimization of Genetic Algorithms and Deep Convolutional Generative Adversarial Networks
Authors: Konstantopoulou Despoina , Zacharia Paraskevi , Papoutsidakis Michail , Leligou Helen C. , Patrikakis Charalampos
Published: December 2024

Generative Adversarial Networks (GANs) are highly effective for generating realistic images, yet their training can be unstable due to challenges such as mode collapse and oscillatory convergence. In this paper, we propose a novel hybrid optimization method that integrates Genetic Algorithms (GAs) to improve the training process of Deep Convolutional GANs (DCGANs). Specifically, GAs are used to evolve the discriminator’s weights, complementing the gradient-based learning typically employed in GANs. The proposed GAGAN model is trained on the CelebA dataset, using 2000 images, to generate 128 × 128 images, with the generator learning to produce realistic faces from random latent vectors. The discriminator, which classifies images as real or fake, is optimized not only through standard backpropagation, but also through a GA framework that evolves its weights via crossover, mutation, and selection processes. This hybrid method aims to enhance convergence stability and boost image quality by balancing local search from gradient-based methods with the global search capabilities of GAs. Experiments show that the proposed approach reduces generator loss and improves image fidelity, demonstrating that evolutionary algorithms can effectively complement deep learning techniques. This work opens new avenues for optimizing GAN training and enhancing performance in generative models.
Development and Validation of Prediction Model for Exhaust Emissions During Tractor Plow Tillage
Authors: Lim Ryu-Gap , Kim Tae-Bum , Kim Wan-Soo , Baek Seung-Yun , Jeon Hyeon-Ho , Ham Jee-Young , Yoo Chul , Kim Yong-Joo
Published: December 2024

In this study, to compensate for the constraints of high unit cost of portable emission measurement system (PEMS) and measurement environment, we developed a tractor operation-based emission prediction model. We also evaluated the developed prediction model using validation metrics. In addition to engine load data, correlation analysis was conducted on engine temperature and fuel consumption variables. The results showed a high correlation of more than 0.5 between emissions and engine temperature, and a high correlation of more than 0.5 between emissions and fuel consumption for emissions except CO and THC. The R2 values of the CO, THC, NOx, and PM emission prediction models were 0.81, 0.82, 0.85, and 0.97, respectively, showing good overall predictive performance. The prediction models for CO, THC, NOx, and PM emissions developed using the third-order regression analysis all showed excellent performance with an average absolute percentage error of around 2%. Therefore, the developed emission regression model can be used to predict tractor emissions using various variables. Through the exhaust emissions prediction model developed in this study, eco-friendly technology according to the optimal engine design is expected to increase. In addition, it is expected that agricultural machinery prices will be stabilized and export competitiveness will be secured.
Emerging Arboviruses in Europe
Authors: Papa Anna
Published: December 2024

Viruses transmitted by arthropods (arboviruses) pose a global public health threat. Sporadic cases or outbreaks caused by West Nile virus, Crimean–Congo haemorrhagic fever virus, tick-borne encephalitis virus, and phleboviruses are often reported in Europe. Recently, they expanded their distribution in geographic areas where they had never been observed before, while tropical viruses, like Dengue, Chikungunya, and Zika, started to cause autochthonous cases and outbreaks following the return of viraemic travellers from endemic countries. The primary or secondary vectors of these viruses are established in Europe, and the incidence of arboviral diseases is expected to increase due to several anthropogenic and/or environmental factors (mainly climate change, which affects the survival and amplification of the arthropod vectors). This is an update on the emerging arboviruses in Europe, associated challenges, and future perspectives.
Parasite Screening in Wild Passerines: Enhancing Diagnostic Approaches in Wildlife Rehabilitation Centers
Authors: Rebelo Catarina Ferreira , Ruiz Alicia Carrero , Alvarado-Piqueras Alberto , González Fernando González , Carvalho Luís Madeira de
Published: December 2024

The order Passeriformes is the richest and most abundant group of birds, but despite numerous parasites being identified in wild birds, this order has received limited focus. This study analyzed 17 passerines admitted to the Grupo de Rehabilitación de la Fauna Autóctona y su Hábitat (GREFA), a wildlife rehabilitation center in Spain, during October to December 2022. Necropsies were conducted to determine the presence of parasites, and intestinal contents were analyzed using fecal smear, flotation and sedimentation techniques and the McMaster method. Sixteen passerines (94.1%) were positive for parasites. Identified species included Monojoubertia microhylla and the genera Ornithonyssus sp., Diplotriaena spp., Serratospiculum sp., Porrocaecum sp., Capillaria spp., Syngamus sp., Strongyloides sp. and Brachylecithum sp., besides cestodes and coccidia. The comparative analysis of parasitological diagnostic techniques showed that the Willis flotation technique was effective for detecting coccidia. However, to obtain more accurate results for other parasites, it is important to complement this technique with direct examination or sedimentation techniques. Among the 12 passerines positive for coccidia, oocyst counts per gram of intestinal contents ranged from 100 to 30,450, with a median of 7350. This study provides valuable insights into the parasitic fauna of Passeriformes, serving as a cornerstone for future research and enhancing our understanding of these avian species.
Connecting Students’ Attitudes Toward Birds with Conservation Attitudes, Beliefs, and Knowledge Regarding the Grey Partridge (Perdix perdix)
Authors: Mohorič Ljubomir , Kos Ivan , Mihelič Oražem Vesna , Tomažič Iztok
Published: December 2024

As a farmland species, the grey partridge is facing a drastic decline all over Europe. In Vipava Valley (Slovenia), the species was last observed around 20 years ago. In this region, an initiative for reintroducing grey partridges was put forward, with much effort dedicated to breeding partridges and monitoring breeding success in a closed area. One of the initiative’s goals was to assess the local community’s attitudes, beliefs, and knowledge regarding birds and grey partridges. In this study, we investigated students’ attitudes toward birds in general, their attitudes toward and knowledge of grey partridges, and their beliefs about the feasibility of grey partridge reintroduction. Students generally supported the conservation of birds and grey partridges. However, they were neutral regarding learning about birds and undecided about the success of their reintroduction. Their beliefs regarding partridge reintroduction from the standpoint of natural heritage protection were slightly positive. The results showed that students were not familiar with the species. The study programme, willingness to volunteer, and amount of knowledge showed the most significant effects on students’ attitudes and beliefs. This study indicates that formal education should focus more on informing students about local biodiversity and related issues to raise local community awareness and engage them in conservation actions.
Efficient Cow Body Condition Scoring Using BCS-YOLO: A Lightweight, Knowledge Distillation-Based Method
Authors: Zheng Zhiqiang , Wang Zhuangzhuang , Weng Zhi
Published: December 2024

Monitoring the body condition of dairy cows is essential for ensuring their health and productivity, but traditional BCS methods—relying on visual or tactile assessments by skilled personnel—are subjective, labor-intensive, and impractical for large-scale farms. To overcome these limitations, we present BCS-YOLO, a lightweight and automated BCS framework built on YOLOv8, which enables consistent, accurate scoring under complex conditions with minimal computational resources. BCS-YOLO integrates the Star-EMA module and the Star Shared Lightweight Detection Head (SSLDH) to enhance the detection accuracy and reduce model complexity. The Star-EMA module employs multi-scale attention mechanisms that balance spatial and semantic features, optimizing feature representation for cow hindquarters in cluttered farm environments. SSLDH further simplifies the detection head, making BCS-YOLO viable for deployment in resource-limited scenarios. Additionally, channel-based knowledge distillation generates soft probability maps focusing on key body regions, facilitating effective knowledge transfer and enhancing performance. The results on a public cow image dataset show that BCS-YOLO reduces the model size by 33% and improves the mean average precision (mAP) by 9.4%. These advances make BCS-YOLO a robust, non-invasive tool for consistent and accurate BCS in large-scale farming, supporting sustainable livestock management, reducing labor costs, enhancing animal welfare, and boosting productivity.
Effects of Dietary Gallic Acid on Growth Performance, Meat Quality, Antioxidant Capacity, and Muscle Fiber Type-Related Gene Expression in Broiler Chickens Challenged with Lipopolysaccharide
Authors: Xiong Taidi , Chen Zhilong , Hassan Mubashar , Zhu Cui , Wang Junyan , Tan Shujun , Ding Fayuan , Cheng Zhonggang , Ye Jinling , Fan Qiuli , Xu Danlei , Jiang Shouqun , Ruan Dong
Published: December 2024

In this study, broilers were selected as the research object to investigate the effects and mechanisms of dietary gallic acid (GA) supplementation on growth performance, meat quality, antioxidant capacity, and muscle fiber-related gene expression. A total of 750 one-day-old healthy 817 male crossbred broiler chickens were divided into five treatment groups, with six replicates per group. Birds in the control (CON) group and LPS-challenged treatment (LPS) group were fed a basal diet, and birds in the other three treatment groups received the basal diet with 150, 300, or 450 mg/kg added GA (GA150, GA300, GA450). On days 14, 17, and 20, chickens in the LPS, GA150, GA300, and GA450 groups received intramuscular injections of LPS, while chickens in the CON group received saline. The results showed that the addition of GA to the diet could effectively increase the average daily gain (ADG) of broilers from 1 to 50 days of age, and had a trend (p = 0.078) of increasing the average daily feed intake (ADFI). Adding 450 mg/kg GA to the diet significantly reduced (p < 0.05) the drip loss and pH value of pectoral muscles 45 min after slaughter, and significantly increased (p < 0.05) the lightness value of pectoral muscles 45 min post-slaughter. With an increase in GA level, the content of total volatile basic nitrogen (TVB-N) in pectoral muscles decreased linearly (p < 0.05), and the concentration of C22:6n-3 increased linearly (p < 0.05). GA effectively improved (p < 0.05) the antioxidant capacity of muscles and significantly increased (p < 0.05) the activity of total superoxide dismutase (T-SOD) in pectoral muscles after LPS stimulation, exhibiting linear and quadratic changes (p < 0.05). It also significantly increased (p < 0.05) the activity of hydrogen peroxide and decreased the activity of glutathione peroxidase (GSH-Px), while it linearly decreased (p < 0.05) the content of malondialdehyde (MDA). In addition, the dietary supplementation of GA significantly increased (p < 0.05) the expression levels of myosin heavy chain (MyHC) I and MyHC IIa in pectoral muscles and significantly decreased (p < 0.05) the expression level of MyHC IIx. In summary, the dietary addition of GA can alleviate the effect of the stress response on the growth performance of broiler chickens and improve antioxidant capacity and meat quality. The appropriate amount of dietary GA at each stage was 300 mg/kg.
Proteomic and Metabolomic Profiling Reveals Alterations in Boar X and Y Sperm
Authors: Cheng Jia , Hao Xu , Zhang Weijing , Sun Chenhao , Yuan Xiameng , Yang Yiding , Zeng Wenxian , Zhu Zhendong
Published: December 2024

Sex-controlled sperm combined with artificial insemination allows animals to reproduce offspring according to the desired sex, accelerates the process of animal genetics and breeding and promotes the development of animal husbandry. However, the molecular markers for sexual sperm sorting are unusual. To identify the molecular markers of boar sperm sorting, proteomics and metabolomics techniques were applied to analyze the differences in proteins and metabolism between X and Y sperm. Label-free quantitative proteomics identified 254 differentially expressed proteins (DEPs) in the X and Y sperm of boars, including 106 proteins that were highly expressed in X sperm and 148 proteins that were highly expressed in Y sperm. Among the differential proteins, COX6A1, COX1, CYTB, FUT8, GSTK1 and PFK1 were selected as potential biological markers for X and Y sperm sorting. Moreover, 760 metabolites from X and Y sperm were detected. There were 439 positive ion mode metabolites and 321 negative ion mode metabolites identified. The various metabolites were phosphoenolpyruvate, phytosphingosine, L-arginine, N-acetylputrescine, cytidine-5′-diphosphate and deoxyuridine. These metabolites were mainly involved in the TCA cycle, oxidative phosphorylation pathway, glycolysis pathway, lipid metabolism pathway, amino acid metabolism pathway, pentose phosphate pathway and nucleic acid metabolism pathway. The differential proteins and differential metabolites obtained by the combined proteomics and metabolomics analysis were projected simultaneously to the KEGG pathway, and a total of five pathways were enriched, namely oxidative phosphorylation pathway, purine metabolism, unsaturated fatty acid biosynthesis, ABC transporters and peroxisomes. In summary, COX6A1 and CYTB were identified as potential biomarkers for boar X and Y sperm sorting.
Berries and Their Active Compounds in Prevention of Age-Related Macular Degeneration
Authors: Li Xiang , Zhao Lingda , Zhang Bowei , Wang Shuo
Published: December 2024

Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly, significantly diminishing quality of life. Currently, there is no available treatment to reverse retinal degeneration and neuronal loss, prompting a focus on interventions that slow the progression of intermediate AMD and geographic atrophy. Berries are rich in bioactive compounds, including flavonoids, anthocyanins, carotenoids, and resveratrol, known for their antioxidant, anti-inflammatory, and anti-angiogenic properties. Preclinical studies suggest that extracts from various berries, such as aronia, honeysuckle, black currant, goji, and bilberry, can improve retinal health by reducing oxidative stress and inflammation. Although clinical trials are limited, emerging evidence indicates that dietary intake of these compounds may enhance visual function and slow the progression of AMD. This review summarizes findings from both animal studies and clinical trials to identify specific berries that have been validated to prevent or delay AMD progression, as well as those with potential therapeutic value. Furthermore, we examine the key phytochemicals present in these berries, their mechanisms of action on macular degeneration, and their distinct properties for therapeutic application. A deeper understanding of these characteristics could enable the rational appliance of berries, especially wolfberry, and berry-derived components, such as carotenoids and anthocyanins, to optimize better therapeutic outcomes in AMD management.
Antioxidant Potential of Xanthohumol in Disease Prevention: Evidence from Human and Animal Studies
Authors: Piekara Jakub , Piasecka-Kwiatkowska Dorota
Published: December 2024

Xanthohumol (XN) is a phenolic compound found in the largest amount in the flowers of the hop plant, but also in the leaves and possibly in the stalks, which is successfully added to dietary supplements and cosmetics. XN is known as a potent antioxidant compound, which, according to current research, has the potential to prevent and inhibit the development of diseases, i.e., cancer and neurodegenerative diseases. The review aims to examine the antioxidant role of XN in disease prevention, with an emphasis on the benefits and risks associated with its supplementation. The regulation by XN of the Nrf2/NF-kB/mTOR/AKT (Nuclear factor erythroid 2-related factor 2/Nuclear factor kappa-light-chain-enhancer of activated B cells/Mammalian target of rapamycin/Protein Kinase B) pathways induce a strong antioxidant and anti-inflammatory effect, among others the acceleration of autophagy through increased synthesis of Bcl-2 (B-cell lymphoma 2) proteins, inhibition of the synthesis of VEGF (Vascular-endothelial growth factor) responsible for angiogenesis and phosphorylation of HKII (Hexokinase II). It is the key function of XN to ameliorate inflammation and to promote the healing process in organs. However, existing data also indicate that XN may have adverse effects in certain diseases, such as advanced prostate cancer, where it activates the AMPK (activated protein kinase) pathway responsible for restoring cellular energy balance. This potential risk may explain why XN has not been classified as a therapeutic drug so far and proves that further research is needed to determine the effectiveness of XN against selected disease entities at a given stage of the disease.
Ciclopirox and Ciclopirox Olamine: Antifungal Agents in Dermatology with Expanding Therapeutic Potential
Authors: Mucha Paulina , Borkowski Bartłomiej , Erkiert-Polguj Anna , Budzisz Elzbieta
Published: December 2024

Ciclopirox (CPX) and its ethanolamine salt, ciclopirox olamine (CPO), are synthetic hydroxypyridone derivatives with a wide range of antimicrobial activity, making them valuable in dermatology for treating fungal infections. Their mechanism of action is multifaceted, impacting iron-dependent enzymes and disrupting mitochondrial function, cellular energy production, and membrane integrity. The compounds’ favorable physicochemical properties allow effective skin absorption, while the olamine salt enhances solubility and bioavailability. Research is ongoing to explore therapeutic uses beyond dermatology, including applications in autoimmune diseases, cancer, and neurodegenerative disorders. In cosmetics, ciclopirox is used primarily in anti-dandruff and skincare products, combining therapeutic effects with minimal side effects.
Evaluation of Blue®m Mouthwash vs. Chlorhexidine Mouthwash in Patients with Gingivitis—A Pilot Study
Authors: Kumar Manoj , Meher Bishnudev , Mishra Lora , Panda Saurav , Sokolowski Krzysztof , Lapinska Barbara
Published: December 2024

Chlorhexidine is widely regarded as a gold standard antiplaque agent, and it is used commonly in periodontal patients. However, due to its side effects, a suitable alternative agent has been searched for several years. The present study compared the efficacy of a novel, oxygen-releasing, natural mouthwash Blue®m (Bluem Europe, Zwolle, The Netherlands) with chlorhexidine in patients with gingivitis. A total of 50 patients were selected and randomly divided into two groups who received a baseline scaling by a trained periodontist. The test group received Blue®m mouthwash as an adjunct for 2 weeks, whereas the control group received chlorhexidine mouthwash for 2 weeks. The primary outcomes of the Full-Mouth Gingival Index, Full-Mouth Plaque Index, and a percentage of the bleeding sites were recorded at the baseline. The same parameters were recorded after 2 weeks and 1 month from the baseline. Both the study and control groups exhibited equal efficacy in terms of reduction in the gingival index and bleeding percentage, both at 2 weeks and 1 month post scaling. However, the Blue®m group displayed statistically significant reduction in plaque index at 1 month as compared with chlorhexidine. As per the objectives of the study, it can be concluded that Blue®m reported a better antiplaque efficacy compared with chlorhexidine.
Evaluation of 3D Concrete Printing Extrusion Efficiency
Authors: Maroszek Marcin , Rudziewicz Magdalena , Hutyra Adam , Dziura Paweł , Hebda Marek
Published: December 2024

In recent years, three-dimensional concrete printing (3DCP) has garnered significant attention within the scientific community. The ongoing development and widespread adoption of this technology in the near future will be nearly inevitable. However, to ensure the safe and effective use of 3DCP in building construction, standardization of its processes is essential. A critical component of this standardization involves guaranteeing the repeatability of element production, regardless of variations in shape or material composition. Therefore, the present study focuses on analyzing factors that influence extrusion efficiency, incorporating measurements of material dispensing rates relative to input parameters. Based on the results, it was found that reducing the printer nozzle size from 20 mm to 12 mm significantly decreases the extrusion efficiency, resulting in a 15% reduction. The addition of polypropylene fibers to the concrete mixture has an even greater impact, reducing extrusion efficiency by up to 28%. On the other hand, changing the mixture’s flow from 150 mm to 130 mm had relatively minor effect, decreasing process efficiency by only 4.5%. To achieve consistent repeatability in the 3DCP process, introducing corrective factors adjusted for variations in input parameters appears to be a necessary step. The development and implementation of such adjustments will be a critical aspect of future research efforts aimed at optimizing 3D concrete printing technology.
Research on the Effect of Working Memory Training on the Prevention of Situation Awareness Failure in Shearer Monitoring Operations
Authors: Yuan Xiaofang , Song Ruyi , Sun Linhui
Published: December 2024

The digitization of the instrument control system in monitoring operations makes the problem of the operator’s situational awareness failure more prominent. In order to better prevent this occurrence, this paper explores the failure of situational awareness from the perspective of cognitive function. The subjects were randomly divided into two groups: a working memory training group and a control group. Working memory measurements and coal mining machine monitoring simulation system operation tasks were performed before and after training, and the task performance, situational awareness scale, and EEG index data were recorded. The results showed that, after the training, there was a significant improvement in the task performance of the monitoring operation and the scores of the situational awareness scale, and there were different degrees of activation in the θ, α2, and β1 frequency bands. It was demonstrated that working memory training could help to improve the rapid reaction and decision-making abilities of operators in complex or emergency situations, thus preventing the failure of situational awareness. This study provides a new direction for research on the prevention of situational awareness failure.
New Insights into Cement-Soil Mixtures with the Addition of Fluidized Bed Furnace Bottom Ashes
Authors: Kaczmarczyk Grzegorz Piotr , Wałach Daniel
Published: December 2024

This research is the result of work on implementing a closed-loop economy in geotechnics, which aligns with the broader concept of a circular economy in construction by promoting the use of waste materials and reducing environmental impact. The research presented in the article focuses on the use of fluidized bed furnace bottom ashes, a by-product of coal combustion in fluidized bed boilers, in the production of cement-soil jet grouting slabs. Samples were analyzed for their structural and mechanical properties to assess their suitability for geotechnical applications. The mixtures were distinguished between those using CEM I and those using CEM II. Mixes based on two types of cements had an additional division based on the amount of additives: reference mix, 5% ash, 15% ash, and 10% ash + 5% microsilica. The conducted experiments aim to determine the physico-mechanical parameters of the new mixtures, highlighting the potential of these materials in mining and geotechnical technologies. The research took into account the impact of time over a period of two years for mortars and 28 days for cement-soil. The authors’ studies included determining the compressive strength, bending strength, and imaging using computed tomography. Computed tomography allowed imaging of the internal structure and porosity analysis. Employing CEM II as the primary binding material slows early strength gain, but adding microsilica significantly enhances strength, compaction, and durability. Despite improved properties, CT imaging revealed increased cracking in mixtures with CEM II, indicating reduced water tightness and highlighting areas for further study.
Measuring Electromagnetic Properties of Vegetal Soil for Wireless Underground Sensor Networks in Precision Agriculture
Authors: Said Maroua , Fattahi Jaouhar , Ghnimi Said , Ghayoula Ridha , Boulejfen Noureddine
Published: December 2024

This research examines and analyzes the measured electromagnetic characteristics of vegetal soil for Wireless Underground Sensor Networks applied to precision agriculture. For this, we used Wireless Underground Sensor Network (WUSN) technology, which consists of sensors that communicate through the soil to collect data on irrigation, such as temperature and humidity, for good plant growth. However, underground communication channels and signal transmission are required to travel through a dense and heterogeneous soil mixture. For the measurement results of the vegetal soil dielectric parameters, a precision domain sensing probe operating at 433 Mhz was used. Moreover, the different choices of capacitance, inductance, and varactor were included, with a reasonable estimation of the dielectric permittivity, ranging from 2 to 15, and an unlimited range of conductivities. Despite promising results in predicting the dielectric permittivities, several improvements were made to the mode for low permittivity values, and it was designed to accommodate a wide range of dielectric permittivities.
A Study on the Stress Field Around an Elliptical Wellbore with an Eccentric Casing in Deep Wells
Authors: Zhu Xiuxing , Han Bing , Xia Yuanbo , Xu Yantao , Zhou Bo , Wang Chengwen
Published: December 2024

Casing stress distribution is one of the most relevant topics in the study of the wellbore stress field. Due to factors such as uneven geostress, the wellbore is often elliptical, and the casing may not necessarily be in the center of the wellbore. This article adopts the theory of elasticity and uses the finite element method for calculation. The main research focuses on the effects of ovality and eccentricity on the overall stress distribution of the casing and the stress distribution at the interface between the casing and cement sheath (i.e., the outer wall of the casing). The research results indicated that when the casing is centered, higher ellipticity is beneficial for improving the casing’s response to non-uniform stress. However, when the casing is eccentric, higher ellipticity significantly increases the non-uniformity of the stress distribution and the risk of stress concentration in the casing. The risk of casing failure increases with increasing non-uniform stress. Additionally, a lower elastic modulus and higher Poisson ratio of the cement can improve the casing’s response to stress. This research result has a certain reference value for implementing high-quality cementing construction and ensuring the normal production of oil and gas wells.
Correlation Data Augmentation-Based YOLO-Integrated Object Detection of Thermal-Equalization Video Using Line Scanning Inductive Thermography
Authors: Lee Seung-Ju , Kim Won-Tae , Suh Hyun-Kyu
Published: December 2024

Active infrared thermography (IRT) in non-destructive testing is an attractive technique used to detect wide areas in real-time on site. Most of the objects inspected on site generally have rough surfaces and foreign substances, which significantly affects their detectability. To solve this problem, in this study, line scanning (LS)-based induction thermography was used to acquire thermal image data of a specimen containing foreign substances. The heat distribution caused by foreign substances was removed using the Gaussian filtering-based Fast Fourier Transform (FFT) algorithm. After that, the data augmentation was performed by analyzing the correlation, and crack detection for the images was performed using you only look once (YOLO) deep learning. This study presents a method for removing non-uniform heat sources using the FFT algorithm, securing virtual data augmentation, and a detection mechanism for moving inspection objects using AI deep learning.
An Electricity Sale Package Recommendation Method Based on Prospect Strengths and Weaknesses Degree and Choquet Integral
Authors: Wu Yufei , Qiu Lifan , Ma Yuanqian
Published: December 2024

Given that existing methods for recommending electricity sale packages primarily consider scenarios where customers are familiar with all package attributes, they overlook psychological factors, attribute correlations, and the determination of attribute weights during decision-making. To address these limitations, this paper proposes a recommendation method for electricity sale package based on prospect strengths and weaknesses degree and the Choquet integral. The details are as follows: Firstly, a label system for evaluating electricity sale packages and customer clustering is utilized to identify similar customers to a new customer. Secondly, a set of similar customers is identified, and N similar customers are selected as experimental customers. Their decision-making information is aggregated using the Choquet integral to construct a customer group decision-making matrix. Next, to account for customers’ psychological risk preferences, the Prospect Theory is integrated into the preference difference function of the classical Superiority–Inferiority Ranking method, resulting in the degree of prospect strengths and weaknesses. Building on this, and addressing the challenges of attribute correlation and weight determination, a model is constructed using the Choquet integral and comprehensive attribute weights. This model ranks electricity sale packages based on the degree of prospect strengths and weaknesses, capturing the differences between various schemes through the prospect strengths and weaknesses flow. The ranking of packages for recommendation is then derived from this flow. Finally, a case analysis is conducted with customers in a western Zhejiang region in China to verify the accuracy and effectiveness of the proposed recommendation method.
Enhancing Human–Robot Interaction: Development of Multimodal Robotic Assistant for User Emotion Recognition
Authors: Garcia Sergio , Gomez-Donoso Francisco , Cazorla Miguel
Published: December 2024

This paper presents a study on enhancing human–robot interaction (HRI) through multimodal emotional recognition within social robotics. Using the humanoid robot Pepper as a testbed, we integrate visual, auditory, and textual analysis to improve emotion recognition accuracy and contextual understanding. The proposed framework combines pretrained neural networks with fine-tuning techniques tailored to specific users, demonstrating that high accuracy in emotion recognition can be achieved by adapting the models to the individual emotional expressions of each user. This approach addresses the inherent variability in emotional expression across individuals, making it feasible to deploy personalized emotion recognition systems. Our experiments validate the effectiveness of this methodology, achieving high precision in multimodal emotion recognition through fine-tuning, while maintaining adaptability in real-world scenarios. These enhancements significantly improve Pepper’s interactive and empathetic capabilities, allowing it to engage more naturally with users in assistive, educational, and healthcare settings. This study not only advances the field of HRI but also provides a reproducible framework for integrating multimodal emotion recognition into commercial humanoid robots, bridging the gap between research prototypes and practical applications.
Parametric Analysis Towards the Design of Micro-Scale Wind Turbines: A Machine Learning Approach
Authors: Mansour Raneem , Osama Seifelden , Ahmed Hazem , Nasser Mohamed , Mahmoud Norhan , Elkodama Amira , Ismaiel Amr
Published: December 2024

Wind turbine design is an iterative process. Many aspects are considered when designing a wind turbine, including aerodynamic and power performance, structural loads and behavior, and control techniques. In the preliminary design stages, the governing equations of each design aspect are used to calculate the different loads and performance outputs while optimizing between them. This is usually made using wind turbine simulation software. This work presents a data-based machine learning (ML) approach towards the design of a micro-scale wind turbine. Extensive simulations are made on a 45 cm diameter rotor while performing parametric analysis using the QBlade wind turbine simulation tool. Different design parameters and wind conditions were changed one at a time, and data were collected to be further analyzed and used to train the ML models. The measurable outputs of the models are the coefficient of power (CP), loads normal and tangential to the blade at midspan (FN and FT), and the torque (T) on the rotor. Linear regression was found unsuitable for predicting CP due to its high nonlinearity; however, it gave satisfactory results for the blade loads. Ensemble models were found to give the highest accuracy for predicting all the desired outputs. The model accuracy is measured in terms of the coefficient of determination (R2), where the model could predict Cp, FN, FT, and T with R2 values of 0.999, 0.984, 0.984, and 0.986 respectively.
Efficient Newton-Type Solvers Based on for Finding the Solution of Nonlinear Algebraic Problems
Authors: Jebreen Haifa Bin , Wang Hongzhou , Chalco-Cano Yurilev
Published: December 2024

The purpose of this study is to improve the computational efficiency of solvers for nonlinear algebraic problems with simple roots. To this end, a multi-step solver based on Newton’s method is utilized. Divided difference operators are applied at two substeps in various forms to enhance the convergence speed and, consequently, the solver’s efficiency index. Attraction basins for the proposed solvers and their competitors are presented, demonstrating that the proposed solvers exhibit large attraction basins in the scalar case while maintaining high convergence rates. Theoretical findings are supported by numerical experiments.
Analysis of Optimal Prediction Under Stochastically Restricted Linear Model and Its Subsample Models
Authors: Güler Nesrin
Published: December 2024

This paper provides a study on optimal prediction problems in a linear model and its subsample models with linear stochastic restrictions, using matrix theory for precise analytical solutions. It focuses on deriving analytical expressions using block matrix inertia and rank methods to determine which of the best linear unbiased predictors (BLUPs) of a general vector of unknown parameters is superior to others under a stochastically restricted linear model and its subsample models. Additionally, this study examines the comparative results of the best linear unbiased estimators of unknown parameters. The comparisons in the study are based on the mean squared error matrix (MSEM) criterion. Finally, a numerical example is given to illustrate the theoretical results.
Arabic Opinion Classification of Customer Service Conversations Using Data Augmentation and Artificial Intelligence
Authors: Al-Mutawa Rihab Fahd , Al-Aama Arwa Yousuf
Published: December 2024

Customer satisfaction is not just a significant factor but a cornerstone for smart cities and their organizations that offer services to people. It enhances the organization’s reputation and profitability and drastically raises the chances of returning customers. Unfortunately, customer support service through online chat is often not rated by customers to help improve the service. This study employs artificial intelligence and data augmentation to predict customer satisfaction ratings from conversations by analyzing the responses of customers and service providers. For the study, the authors obtained actual conversations between customers and real agents from the call center database of Jeddah Municipality that were rated by customers on a scale of 1–5. They trained and tested five prediction models with approaches based on logistic regression, random forest, and ensemble-based deep learning, and fine-tuned two pre-trained recent models: ArabicT5 and SaudiBERT. Then, they repeated training and testing models after applying a data augmentation technique using the generative artificial intelligence, GPT-4, to improve the unbalance in customer conversation data. The study found that the ensemble-based deep learning approach best predicts the five-, three-, and two-class classifications. Moreover, data augmentation improved accuracy using the ensemble-based deep learning model with a 1.69% increase and the logistic regression model with a 3.84% increase. This study contributes to the advancement of Arabic opinion mining, as it is the first to report the performance of determining customer satisfaction levels using Arabic conversation data. The implications of this study are significant, as the findings can be applied to improve customer service in various organizations.
Advances in Natural-Product-Based Fluorescent Agents and Synthetic Analogues for Analytical and Biomedical Applications
Authors: Joshi Soniya , Moody Alexis , Budthapa Padamlal , Gurung Anita , Gautam Rachana , Sanjel Prabha , Gupta Aakash , Aryal Surya P. , Parajuli Niranjan , Bhattarai Narayan
Published: December 2024

Fluorescence is a remarkable property exhibited by many chemical compounds and biomolecules. Fluorescence has revolutionized analytical and biomedical sciences due to its wide-ranging applications in analytical and diagnostic tools of biological and environmental importance. Fluorescent molecules are frequently employed in drug delivery, optical sensing, cellular imaging, and biomarker discovery. Cancer is a global challenge and fluorescence agents can function as diagnostic as well as monitoring tools, both during early tumor progression and treatment monitoring. Many fluorescent compounds can be found in their natural form, but recent developments in synthetic chemistry and molecular biology have allowed us to synthesize and tune fluorescent molecules that would not otherwise exist in nature. Naturally derived fluorescent compounds are generally more biocompatible and environmentally friendly. They can also be modified in cost-effective and target-specific ways with the help of synthetic tools. Understanding their unique chemical structures and photophysical properties is key to harnessing their full potential in biomedical and analytical research. As drug discovery efforts require the rigorous characterization of pharmacokinetics and pharmacodynamics, fluorescence-based detection accelerates the understanding of drug interactions via in vitro and in vivo assays. Herein, we provide a review of natural products and synthetic analogs that exhibit fluorescence properties and can be used as probes, detailing their photophysical properties. We have also provided some insights into the relationships between chemical structures and fluorescent properties. Finally, we have discussed the applications of fluorescent compounds in biomedical science, mainly in the study of tumor and cancer cells and analytical research, highlighting their pivotal role in advancing drug delivery, biomarkers, cell imaging, biosensing technologies, and as targeting ligands in the diagnosis of tumors.
Research Progress of Phospholipid Vesicles in Biological Field
Authors: Zhang Na , Song Jie , Han Yuchun
Published: December 2024

Due to their high biocompatibility, biodegradability, and facile surface functionalization, phospholipid vesicles as carriers have garnered significant attention in the realm of disease diagnosis and treatment. On the one hand, phospholipid vesicles can function as probes for the detection of various diseases by encapsulating nanoparticles, thereby enabling the precise localization of pathological changes and the monitoring of disease progression. On the other hand, phospholipid vesicles possess the capability to selectively target and deliver therapeutic agents, including drug molecules, genes and immune modulators, to affected sites, thereby enhancing the sustained release of these agents and improving therapeutic efficacy. Recent advancements in nanotechnology have led to an increased focus on the application of phospholipid vesicles in drug delivery, biological detection, gene therapy, and cell mimics. This review aims to provide a concise overview of the structure, characteristics, and preparation techniques of phospholipid vesicles of varying sizes. Furthermore, we will summarize the latest research developments regarding their use as nanomedicines and gene carriers in disease treatment. Additionally, we will elucidate the potential of phospholipid vesicles in facilitating the internalization, controlled release, and targeted delivery of therapeutic substrates. Through this review, we aspire to enhance the understanding of the evolution of phospholipid vesicles within the biological field, outline prospective research, and address the forthcoming challenges associated with phospholipid vesicles in disease diagnosis and treatment.