bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of a high dose atorvastatin as added-on therapy on symptoms and serum AMPK/NLRP3 inflammasome and IL-6/STAT3 axes in patients with major depressive disorder: randomized controlled clinical study
Authors: Aldossary Khlood Mohammad , Ali Lashin Saad , Abdallah Mahmoud S. , Bahaa Mostafa M. , Elmasry Thanaa A. , Elberri Eman I. , Kotkata Fedaa A. , El Sabaa Ramy M. , Elmorsi Yasmine M. , Kamel Mostafa M. , Negm Walaa A. , Elberri Aya Ibrahim , Hamouda Amir O. , AlRasheed Hayam Ali , Salahuddin Muhammed M. , Yasser Mohamed , Hamouda Manal A.
Published: May 2024

Background

Neuroinflammation pathways have been associated with the development of major depressive disorders (MDD). The anti-inflammatory characteristics of statins have been demonstrated to have significance in the pathophysiology of depression.

Aim

To investigate the mechanistic pathways of high dose atorvastatin in MDD.

Patients and methods

This trial included 60 patients with MDD who met the eligibility requirements. Two groups of patients (n = 30) were recruited by selecting patients from the Psychiatry Department. Group 1 received 20 mg of fluoxetine plus a placebo once daily. Group 2 received fluoxetine and atorvastatin (80 mg) once daily. All patients were assessed by a psychiatrist using the Hamilton Depression Rating Scale (HDRS). A HDRS score of ≤7 indicates remission or partial remission [HDRS<17 and>7]. Response was defined as ≥ 50% drop in the HDRS score. The serum concentrations of nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3 (NLRP-3), interleukin-6 (IL-6), adenosine monophosphate activated protein kinase (AMPK), and signal transducer and activator of transcription factor-3 (STAT-3) were measured.

Results

The atorvastatin group showed a significant reduction in the levels of all measured markers along with a statistical increase in the levels of AMPK when compared to the fluoxetine group. The atorvastatin group displayed a significant decrease in HDRS when compared to its baseline and the fluoxetine group. The response rate and partial remission were higher in the atorvastatin group than fluoxetine (p = 0.03, and p = 0.005), respectively.

Conclusion

These results imply that atorvastatin at high doses may be a promising adjuvant therapy for MDD patients by altering the signaling pathways for AMPK/NLRP3 and IL-6/STAT-3.

Clinical Trial Registration

clinicaltrials.gov, identifier NCT05792540.
Alzheimer’s disease and its treatment–yesterday, today, and tomorrow
Authors: Kim A. Y. , Al Jerdi S. , MacDonald R. , Triggle C. R.
Published: May 2024

Alois Alzheimer described the first patient with Alzheimer’s disease (AD) in 1907 and today AD is the most frequently diagnosed of dementias. AD is a multi-factorial neurodegenerative disorder with familial, life style and comorbidity influences impacting a global population of more than 47 million with a projected escalation by 2050 to exceed 130 million. In the USA the AD demographic encompasses approximately six million individuals, expected to increase to surpass 13 million by 2050, and the antecedent phase of AD, recognized as mild cognitive impairment (MCI), involves nearly 12 million individuals. The economic outlay for the management of AD and AD-related cognitive decline is estimated at approximately 355 billion USD. In addition, the intensifying prevalence of AD cases in countries with modest to intermediate income countries further enhances the urgency for more therapeutically and cost-effective treatments and for improving the quality of life for patients and their families. This narrative review evaluates the pathophysiological basis of AD with an initial focus on the therapeutic efficacy and limitations of the existing drugs that provide symptomatic relief: acetylcholinesterase inhibitors (AChEI) donepezil, galantamine, rivastigmine, and the N-methyl-D-aspartate receptor (NMDA) receptor allosteric modulator, memantine. The hypothesis that amyloid-β (Aβ) and tau are appropriate targets for drugs and have the potential to halt the progress of AD is critically analyzed with a particular focus on clinical trial data with anti-Aβ monoclonal antibodies (MABs), namely, aducanumab, lecanemab and donanemab. This review challenges the dogma that targeting Aβ will benefit the majority of subjects with AD that the anti-Aβ MABs are unlikely to be the “magic bullet”. A comparison of the benefits and disadvantages of the different classes of drugs forms the basis for determining new directions for research and alternative drug targets that are undergoing pre-clinical and clinical assessments. In addition, we discuss and stress the importance of the treatment of the co-morbidities, including hypertension, diabetes, obesity and depression that are known to increase the risk of developing AD.
The coexistence of arbuscular mycorrhizal fungi and dark septate endophytes synergistically enhanced the cadmium tolerance of maize
Authors: Wang Zhaodi , Wang Lei , Liang Xinran , Zhang Guangqun , Li Zuran , Yang Zhixin , Zhan Fangdong
Published: May 2024

Introduction

Arbuscular mycorrhizal fungi (AMF) and dark septate endophytic fungi (DSEs) generally coexist in the roots of plants. However, our understanding of the effects of their coexistence on plant growth and stress resistance is limited.

Methods

In the present study, the effects of single and dual inoculation of AMF and DSE on the growth, photosynthetic physiology, glutathione (GSH) metabolism, endogenous hormones, and cadmium (Cd) content of maize under 25 mg•kg-1 Cd stress were investigated.

Results

Compared with that after the non-inoculation treatment, AMF+DSE co-inoculation significantly increased the photosynthetic rate (Pn) of maize leaves; promoted root GSH metabolism; increased the root GSH concentration and activity of γ-glutamyl cysteine synthase (γ-GCS), ATP sulfatase (ATPS) and sulfite reductase (SIR) by 215%, 117%, 50%, and 36%, respectively; and increased the concentration of endogenous hormones in roots, with increases in zeatin (ZR), indole-3 acetic acid (IAA), and abscisic acid (ABA) by 81%, 209%, and 72%, respectively. AMF inoculation, DSE inoculation and AMF+DSE co-inoculation significantly increased maize biomass, and single inoculation with AMF or DSE increased the Cd concentration in roots by 104% or 120%, respectively. Moreover, significant or highly significant positive correlations were observed between the contents of ZR, IAA, and ABA and the activities of γ-GCS, ATPS, and SIR and the glutathione (GSH) content. There were significant or highly significant positive interactions between AMF and DSE on the Pn of leaves, root GSH metabolism, and endogenous hormone contents according to two-way analysis of variance. Therefore, the coexistence of AMF and DSE synergistically enhanced the Cd tolerance of maize.
Hyperspectal imaging technology for phenotyping iron and boron deficiency in Brassica napus under greenhouse conditions
Authors: Li Hui , Wan Long , Li Chengsong , Wang Lihong , Zhu Shiping , Chen Xinping , Wang Pei
Published: May 2024

Introduction

The micronutrient deficiency of iron and boron is a common issue affecting the growth of rapeseed (Brassica napus). In this study, a non-destructive diagnosis method for iron and boron deficiency in Brassica napus (genotype: Zhongshuang 11) using hyperspectral imaging technology was established.

Methods

The recognition accuracy was compared using the Fisher Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) recognition models. Recognition results showed that Multiple Scattering Correction (MSC) could be applied for the full band hyperspectral data processing, while the LDA models presented better performance on establishing the leaf iron and boron deficiency symptom recognition than the SVM models.

Results

The recognition accuracy of the training set reached 96.67%, and the recognition rate of the prediction set could be 91.67%. To improve the model accuracy, the Competitive Adaptive Reweighted Sampling algorithm (CARS) was added to construct the MSC-CARS-LDA model. 33 featured wavelengths were selected via CARS. The recognition accuracy of the MSC-CARS-LDA training set was 100%, while the recognition accuracy of the MSC-CARS-LDA prediction set was 95.00%.

Discussion

This study indicates that, it is capable to identify the iron and boron deficiency in rapeseed using hyperspectral imaging technology.
Predicting the potential distribution of Dendrolimus punctatus and its host Pinus massoniana in China under climate change conditions
Authors: Wang Yijie , Zhao Youjie , Miao Guangting , Zhou Xiaotao , Yu Chunjiang , Cao Yong
Published: May 2024

Introduction

Dendrolimus punctatus, a major pest endemic to the native Pinus massoniana forests in China, displays major outbreak characteristics and causes severe destructiveness. In the context of global climate change, this study aims to investigate the effects of climatic variations on the distribution of D. punctatus and its host, P. massoniana.

Methods

We predict their potential suitable distribution areas in the future, thereby offering a theoretical basis for monitoring and controlling D. punctatus, as well as conserving P. massoniana forest resources. By utilizing existing distribution data on D. punctatus and P. massoniana, coupled with relevant climatic variables, this study employs an optimized maximum entropy (MaxEnt) model for predictions. With feature combinations set as linear and product (LP) and the regularization multiplier at 0.1, the model strikes an optimal balance between complexity and accuracy.

Results

The results indicate that the primary climatic factors influencing the distribution of D. punctatus and P. massoniana include the minimum temperature of the coldest month, annual temperature range, and annual precipitation. Under the influence of climate change, the distribution areas of P. massoniana and its pests exhibit a high degree of similarity, primarily concentrated in the region south of the Qinling−Huaihe line in China. In various climate scenarios, the suitable habitat areas for these two species may expand to varying degrees, exhibiting a tendency to shift toward higher latitude regions. Particularly under the high emission scenario (SSP5-8.5), D. punctatus is projected to expand northwards at the fastest rate.

Discussion

By 2050, its migration direction is expected to closely align with that of P. massoniana, indicating that the pine forests will continue to be affected by the pest. These findings provide crucial empirical references for region-specific prevention of D. punctatus infestations and for the rational utilization and management of P. massoniana resources.
Cognitive influences on biosecurity measure compliance during a global pandemic
Authors: Jara-Rizzo María F. , Soria-Miranda Nadia , Friehs Maximilian A. , Leon-Rojas Jose E. , Rodas Jose A.
Published: May 2024

Introduction

During the first years of the pandemic, COVID-19 forced governments worldwide to take drastic measures to reduce the spread of the virus. Some of these measures included mandatory confinements, constant use of masks, and social distancing. Despite these measures being mandatory in many countries and the abundance of evidence on their effectiveness at slowing the spread of the virus, many people failed to comply with them.

Methods

This research explored the role of cognitive factors in predicting compliance with COVID-19 safety measures across two separate studies. Building on earlier work demonstrating the relevance of cognitive processes in health behaviour, this study aimed to identify key predictors of adherence to safety guidelines during the pandemic. Utilising hierarchical regression models, we investigated the influence of age, sex, cognitive control, cognitive flexibility (Study 1), working memory, psychological health, and beliefs about COVID-19 (Study 2) on compliance to biosafety measures.

Results

Demographic variables and cognitive control were significant predictors of compliance in both studies. However, cognitive flexibility and working memory did not improve the models’ predictive capacities. In Study 2, integrating measures of psychological health and beliefs regarding COVID-19 severity significantly improved the model. Further, interaction effects between age and other variables also enhanced the predictive value.

Discussion

The findings emphasise the significant role cognitive control, age, psychological health, and perceptions about COVID-19 play in shaping compliance behaviour, highlighting avenues for targeted interventions to improve public health outcomes during a pandemic.
School belonging mediates the association between negative school climate and depressive symptoms among Chinese adolescents: a national population-based longitudinal study
Authors: Yin Yongtian , Su Qingxiang , Li Shaojie
Published: May 2024

Background

A negative school climate is an important factor affecting students’ mental health. However, few studies have focused on the mechanisms underlying the relationship. This study aimed to explore the mediating effect of school belonging on the association between negative school climate and depressive symptoms among Chinese adolescents, using a nationwide longitudinal survey.

Methods

We conducted a longitudinal study using data from the 2013 (T1) and 2014 (T2) waves of the China Education Panel Survey (CEPS). A negative school climate was assessed by school administrators’ reports. School belonging and depressive symptoms were evaluated using adolescents’ self-reports. We used a cross-lagged panel model to explore the mediating effect of school belonging on the association between negative school climate and depressive symptoms, adjusting for a set of covariates.

Results

In total, 7,049 Chinese adolescents with a mean age of 12.9 years were included in this study. The results of the cross-lagged model showed that negative school climate at T1 was significantly negatively associated with school belonging at T2 (β = −0.089, 95%CI = −0.111–−0.067, p < 0.001), and was positively associated with depressive symptoms at T2 (β = 0.032, 95%CI = 0.012–0.054, p = 0.002). In addition, school belonging at T1 was significantly negatively associated with depressive symptoms at T2 (β = −0.025, 95%CI = −0.050–−0.001, p = 0.045). Mediation analysis showed that school belonging played a mediating role in the association between negative school climate and depressive symptoms (β = 0.002, 95%CI = 0.001–0.005, p = 0.041).

Conclusion

Among Chinese adolescents, a negative school climate is associated with a greater risk of depressive symptoms. Improving school belonging may be helpful in decreasing the impact of a negative school climate on depressive symptoms in adolescents.
Factorial structure of the Comprehensive Assessment of At-Risk Mental States in help-seeking individuals: mapping the structure and the prediction of subsequent transition to psychosis
Authors: Montemagni Cristiana , Carluccio Anna , Brasso Claudio , Vischia Flavio , Rocca Paola
Published: May 2024

Objectives

The aim of the current study was 3-fold: 1) to examine the factorial structure of the Comprehensive Assessment of At-Risk Mental States (CAARMS) in help-seeking individuals undergoing an assessment on suspicion of psychosis risk; 2) to investigate the association of CAARMS factors with functioning; 3) and to test the association of any derived factors with the longitudinal outcome of transition to psychosis.

Methods

The study included 101 patients. First, a principal component analysis (PCA) was conducted using the Varimax rotation method. A minimum initial eigenvalues of greater than or equal to 1.0, analysis of Scree plots, percentage of variance explained by each component, reliability (Cronbach’s alpha) of factors above 0.7 and Parallel Analysis were the criteria used to determine the appropriate number of factors Second, Spearman correlations were run to analyze the relationship between CAARMS factors and sociodemographic and functional variables (i.e. age, schooling, Social and Occupational Functioning Assessment Scale-SOFAS- and Health of the Nation Outcome Scales-HoNOS- scores). Third, we performed a Logistic regression analysis to evaluate the association between baseline CAARMS factors and the risk of transition to psychosis at the 6-month follow-up.

Results

A total of 101 consecutive patiens were recruited. We found that: 1) a 6 factor model solution as the most appropriate, jointly accounting for 65% of the variance; 2) factors 1 (“negative-interpersonal”), 2 (“cognitive-disorganization”), 3 (“positive”), and 4 (“motor-physical changes”) were negatively correlated with SOFAS total score; factors 1, 2, and 3 showed positive correlations with HoNOS total score; factors 2 and 3 present similar patterns of correlations, factor 3 manifesting the strongest association with HoNOS symptoms, HONOS and SOFAS total score. Both factors 5 and 6 show significant associations with HoNOS behavioral impairment; 3) after 6 months 28 participants (30.1%) converted to psychosis. Factors 2 and 3 were positively associated with the risk of transition to psychosis; whereas, the factor 5 (“affective factor”) was negatively associated with the outcome variable.

Conclusions

It is thus crucial to recognize the type and severity of psychopathology in help-seeking individuals in order to intensive clinical monitoring of subclinical psychopathology risk profiles, and design specific care pathways.
Impact of smart technology use on sleep quality in individuals with autism spectrum disorder: a mixed-methods investigation
Authors: Abouzed Mohamed , Salama Basem , Gabr Amgad , Elag Khaled A. , Soliman Mahmoud , Elsaadouni Nisrin , Elzahab Nasr Abou
Published: May 2024

Background

Sleep disturbances are common among individuals with autism spectrum disorder (ASD) and can have a negative impact on their daily functioning and core symptoms. As the use of smart technologies continues to rise, it is crucial to understand how these devices affect the sleep quality of individuals with ASD.

Aim

The objective of this study was to examine the relationship between the use of smart technology and sleep quality in individuals with ASD.

Methods

A mixed-methods approach was employed, combining both quantitative and qualitative data collection techniques. A sample of 83 individuals with ASD, aged between 8 and 25 years, assessed their sleep quality using the Pittsburgh Sleep Quality Index. Additionally, information regarding patterns of smart technology use and relevant covariates was collected. Correlation and regression analyses were conducted to analyze the data. Furthermore, semi-structured interviews were conducted with a subset of participants and their caregivers.

Results

Significant positive correlations were found between poorer sleep quality scores and total screen time (r = 0.42, p < 0.001), pre-bedtime technology use (r = 0.51, p < 0.001), gaming (r = 0.32, p = 0.003), and social media use (r = 0.29, p = 0.008). Pre-bedtime technology use was a significant predictor of poorer sleep quality (β = 0.32, p = 0.006), even after controlling for age, gender, and ASD severity.

Conclusion

The findings of this study emphasize the significant associations between the use of smart technology, particularly before bedtime, and poorer sleep quality in individuals with ASD. These results underscore the importance of developing evidence-based interventions and guidelines to promote healthy sleep habits and mitigate the negative effects of technology exposure in this population.
Association of prenatal and postnatal exposure to air pollution with clinically diagnosed attention deficit hyperactivity disorder: a systematic review
Authors: Zhao Jinzhu , He Tianyi , Wang Feng , Liu Wei
Published: May 2024

Attention deficit hyperactivity disorder (ADHD), a prevalent neurodevelopmental disorder in children, originates from a multifaceted interplay of genetic, neurological, and environmental factors. Recent studies have increasingly concentrated on environmental determinants, notably air pollution, and their impact on the risk of developing ADHD. Additionally, previous research has often conflated clinically diagnosed ADHD cases with instances of mere ADHD-like symptoms, a methodology that can introduce bias and obscure the true relationship between environmental factors and ADHD. To address this oversight, our systematic review meticulously investigates the relationship between both prenatal and postnatal exposures to particular air pollutants and strictly clinically diagnosed ADHD. Our comprehensive review encompassed 801 studies from PubMed, Cochrane Library, Web of Science, and Embase databases, out of which eight met our rigorous inclusion criteria. The Newcastle-Ottawa Scale (NOS) was utilized to gauge quality and bias. Our review found substantiated the connection between prenatal exposure to PM2.5 and NOx and a heightened risk of ADHD, while exposure to PM10 during the prenatal stage was not associated with ADHD. These findings hint at varied health impacts from different particulate matters and the prospect of gender-specific susceptibilities to such exposures. We also identified an association between postnatal exposure to PM2.5, PM10, and NO2 and an increased ADHD risk, underlining the potential neurodevelopmental harms from early exposure to these pollutants. These relationships, seemingly intricate and potentially dose-dependent, underscore the need for more detailed scrutiny. The unique value of our review is in its detailed exploration of the association between specific air pollution exposures and clinically diagnosed ADHD. Our findings offer much-needed clarity in this complex domain and emphasize the importance of future research to standardize exposure and outcome metrics, probe potential mechanisms, and reduce bias and heterogeneity.
Case Report: Halting terminal osseous overgrowth post tibia amputation in children: a report of three cases
Authors: Pon Chee-Chun , Ong Ting-Jie , Abd Rasid Ahmad Fazly , Abd Rashid Abdul Halim , Jamil Kamal
Published: May 2024

Terminal osseous overgrowth is a common complication after trans-diaphyseal amputation in children, leading to pain, soft tissue problems, and recurrent surgical procedures. We report three different cases with post-amputation issues of osseous overgrowth, ulceration, and deformity over the amputation site. The first case involves a 9-year-old boy with a right leg congenital amputation secondary to amniotic band syndrome. The right below-knee stump later experienced recurrent episodes of osseous overgrowth, leading to ulceration. After the prominent tibia was resected and capped with the ipsilateral proximal fibula, a positive outcome was achieved with no more recurrent overgrowth over the right leg stump. The second case involves a 9-year-old girl born with an amniotic constriction band over both legs. Her left leg remained functional after a circumferential Z-plasty, but the right leg was a congenital below-knee amputation. Multiple refashioning surgeries were performed on the right leg due to osseous overgrowth but the patient continued to experience recurrent overgrowth causing pain and difficulty fitting into a prosthesis. We performed osteocartilaginous transfer of the proximal part of the ipsilateral fibula to the right tibial end, successfully preventing the overgrowth of the tibia without any complications. The third case involves an 11-year-old boy with a history of meningococcal septicemia who underwent a right below-knee amputation and left ankle disarticulation due to complications of septic emboli. He experienced a prominent right distal tibia stump, which later developed into valgus deformity as a result of the previous insult to the proximal tibial growth plate. We performed a corrective osteotomy over the proximal right tibia and capped the entire tibia with the ipsilateral fibula as an intramedullary splint for the osteotomy site. Post-operatively, we achieved satisfactory deformity correction and successfully halted the recurrent overgrowth over the right tibia stump. The method of ipsilateral fibula capping is safe and effective in managing the osseous overgrowth complications in trans-diaphyseal amputations among children. Therefore, it is a reasonable option during primary below-knee amputations in children compared to multiple refashioning surgeries.
Seeing the Trees From the Forest: Challenges in Subgroup Analysis-Based Guidelines in Oncology
Authors: Rotem Ofer , Geiger Karyn Revital , Hanovich Ekaterina , Moskovitz Mor , Kurman Noga , Reinhorn Daniel , Peretz Idit , Yerushalmi Rinat , Stemmer Salomon M.
Published: May 2024

As clinical trials in oncology require substantial efforts, maximizing the insights gained from them by conducting subgroup analyses is often attempted. The goal of these analyses is to identify subgroups of patients who are likely to benefit, as well as the subgroups of patients who are unlikely to benefit from the studied intervention. International guidelines occasionally include or exclude novel medications and technologies for specific subpopulations based on such analyses of pivotal trials without requiring confirmatory trials. This Perspective discusses the importance of providing a complete dataset of clinical information when reporting subgroup analyses and explains why such transparency is key for better clinical interpretation of the results and the appropriate application to clinical care, by providing examples of transparent reporting of clinical studies and examples of incomplete reporting of clinical studies.
High-Performance Nanocellulose-Based Ionic Electroactive Soft Actuators
Authors: Wu Yujiao , Cui Qiyuan , Wang Fan
Published: May 2024

High-performance electroactive polymer actuators with large bending, fast response, and high durability have gained attention in the development of micromanipulators and multifunctional bionic soft robots. Herein, we developed high-performance electroactive soft actuators fabricated with ultrathin free-standing microfibrillated cellulose (MFC)-reinforced poly(3,4-ethylenedioxythiophene)/poly(4-styrenesulfonate) (PEDOT/PSS) with multi-walled carbon nanotube (MWCNT)-doped composite electrode films and ion-exchange Nafion membranes by a hot-pressing method. The prepared PEDOT/PSS-MFC-MWCNT electrodes have good film-forming properties with a Young’s modulus of 448 MPa and an electrical conductivity of 75 S/cm. The proposed PEDOT/PSS-MFC-MWCNT/Nafion soft actuators have a sustained peak displacement of 2.1 mm and a long-term cyclic stability of 94% with no degradation over 1 h at 1.0 V, 0.1 Hz. Furthermore, we fabricated soft micro-grippers based on the actuators for mimicking actual finger actions for grasping, pointing, and counting, which introduces new possibilities for the next-generation development of micromanipulators and bionic soft robotics.
Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery
Authors: Wang Meng , Niu Changhe , Wang Zifan , Jiang Yongxin , Jian Jianming , Tang Xiuying
Published: May 2024

To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon Np, control horizon Nc, and preview value Npre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising Npre can improve curve tracking performance. Np was correlated negatively with the curvature change, whereas Nc and Npre were positively connected. Np, Nc, and Npre were correlated positively with the velocity change. Then, the parameters for self-adaptation of Np, Nc, and Npre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path <2.38 cm, and maximum deviation of complex curve path <3.15 cm. The mean absolute value of heading error was ≤0.0096 rad, the standard deviation was ≤0.0091 rad, and the maximum deviation was <0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations.
Influence of Thermal Desorption Technology on Removal Effects and Properties of PAH-Contaminated Soil Based on Engineering Experiments
Authors: Zhang Xin , Li Lei , Shi Xiaokai , Chen Shengquan , Liang Wen , Zhu Yuen , Li Hua
Published: May 2024

Polycyclic aromatic hydrocarbon (PAH) contamination in soil poses a serious threat to ecological safety, human life, and health. Thermal desorption is commonly used to remediate PAH-contaminated soil. Current thermal desorption research primarily focuses on parameters for engineering designs through in-house simulations. In this study, based on a PAH-contaminated site thermal desorption remediation project, the removal efficiency of PAHs in soil (S1, S2) after disposal at different heating temperatures (400, 450, 500, 550 °C) and different residence times (20, 25 min) was investigated, and changes in soil properties before and after disposal were analyzed. The removal rate of 16 PAHs from soil at two concentrations reached 100% after 20 min at 500 °C and 550 °C, respectively. The three-phase structural distances of S1 and S2 soils, respectively, increased by 1.65 and 2.99 times after disposal, sand content increased by 3.20% and 8.27%, water-stable macroaggregates decreased by 8.14% and 2.06%, organic carbon content decreased by 7.27% and 27.05%, heavy fraction organic carbon increased by 33.68% and 5.12%, pH decreased from 10.00 and 10.35 to 8.81 and 8.69, and cation exchange decreased by 13.79% and 26.65%. Soil nutrient content such as TP, AP, TK, and AK increased after thermal desorption, and TN content decreased; approximately 1.0 mg/kg of NO3−-N remained. Our results are expected to support the design of programs for soil reuse after disposal.
Banana Bunch Weight Estimation and Stalk Central Point Localization in Banana Orchards Based on RGB-D Images
Authors: Zhou Lei , Yang Zhou , Deng Fuqin , Zhang Jianmin , Xiao Qiong , Fu Lanhui , Duan Jieli
Published: May 2024

Precise detection and localization are prerequisites for intelligent harvesting, while fruit size and weight estimation are key to intelligent orchard management. In commercial banana orchards, it is necessary to manage the growth and weight of banana bunches so that they can be harvested in time and prepared for transportation according to their different maturity levels. In this study, in order to reduce management costs and labor dependence, and obtain non-destructive weight estimation, we propose a method for localizing and estimating banana bunches using RGB-D images. First, the color image is detected through the YOLO-Banana neural network to obtain two-dimensional information about the banana bunches and stalks. Then, the three-dimensional coordinates of the central point of the banana stalk are calculated according to the depth information, and the banana bunch size is obtained based on the depth information of the central point. Finally, the effective pixel ratio of the banana bunch is presented, and the banana bunch weight estimation model is statistically analyzed. Thus, the weight estimation of the banana bunch is obtained through the bunch size and the effective pixel ratio. The R2 value between the estimated weight and the actual measured value is 0.8947, the RMSE is 1.4102 kg, and the average localization error of the central point of the banana stalk is 22.875 mm. The results show that the proposed method can provide bunch size and weight estimation for the intelligent management of banana orchards, along with localization information for banana-harvesting robots.
Antibiotic-Resistant Bacteria Isolated from Street Foods: A Systematic Review
Authors: Fusaro Carmine , Miranda-Madera Valentina , Serrano-Silva Nancy , Bernal Jaime E. , Ríos-Montes Karina , González-Jiménez Francisco Erik , Ojeda-Juárez Dennys , Sarria-Guzmán Yohanna
Published: May 2024

Street food may be a vehicle of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) to humans. Foods contaminated with ARB entail serious problems or challenges in the fields of medical care, animal husbandry, food industry, and public health worldwide. The objectives of this systematic review were to identify and evaluate scientific reports associated with ARB isolated from various street foods. “Preferred reporting items for systematic reviews and meta-analysis” (PRISMA) guidelines were followed. The bibliographic material covers a period from January 2015 to April 2024. Six electronic scientific databases were searched individually for full-text articles; only those papers that met the inclusion and exclusion criteria were selected. Seventeen papers were included in this systematic review. This study highlighted the wide distribution of ARB resistant to β-lactams and other antibiotics, posing significant health risks to consumers. High resistance levels were observed for antibiotics such as ampicillin, ceftriaxone, and tetracycline, while some antibiotics, such as ceftazidime, clavulanic acid, cefoperazone, cotrimoxazole, doxycycline, doripenem, fosfomycin, vancomycin, and piperacillin-tazobactam, demonstrated 100% susceptibility. The prevalence of ARB in street foods varied between 5.2% and 70.8% among different countries. The multiple resistance of various bacteria, including Escherichia coli, Staphylococcus, Salmonella, and Klebsiella, to multiple classes of antibiotics, as well as environmental factors contributing to the spread of antibiotic resistance (AR), emphasize the urgent need for comprehensive approaches and coordinated efforts to confront antimicrobial resistance (AMR) under the “One Health” paradigm.
Antimicrobial Metabolites of Caucasian Medicinal Plants as Alternatives to Antibiotics
Authors: Fik-Jaskółka Marta , Mittova Valentina , Motsonelidze Catherine , Vakhania Malkhaz , Vicidomini Caterina , Roviello Giovanni N.
Published: May 2024

This review explores the potential of antimicrobial metabolites derived from Caucasian medicinal plants as alternatives to conventional antibiotics. With the rise of antibiotic resistance posing a global health threat, there is a pressing need to investigate alternative sources of antimicrobial agents. Caucasian medicinal plants have traditionally been used for their therapeutic properties, and recent research has highlighted their potential as sources of antimicrobial compounds. Representatives of 15 families of Caucasian medicinal plant extracts (24 species) have been explored for their efficacy against these pathogens. The effect of these plants on Gram-positive and Gram-negative bacteria and fungi is discussed in this paper. By harnessing the bioactive metabolites present in these plants, this study aims to contribute to the development of new antimicrobial treatments that can effectively combat bacterial infections while minimizing the risk of resistance emergence. Herein we discuss the following classes of bioactive compounds exhibiting antimicrobial activity: phenolic compounds, flavonoids, tannins, terpenes, saponins, alkaloids, and sulfur-containing compounds of Allium species. The review discusses the pharmacological properties of selected Caucasian medicinal plants, the extraction and characterization of these antimicrobial metabolites, the mechanisms of action of antibacterial and antifungal plant compounds, and their potential applications in clinical settings. Additionally, challenges and future directions in the research of antimicrobial metabolites from Caucasian medicinal plants are addressed.
Component Effects in Binary Droplet Impact Behaviors on the Heated Plate: Comparison Study of Ethanol/Propanol and Ethanol/Water Droplets and Observation of Novel Bubble Shrinkage Phenomenon
Authors: Yang Xiaoyuan , Huang Bingyao , Zhang Yi , Li Yuyang
Published: May 2024

This work aims to investigate the effect of liquid physical properties on the behavior of binary droplets impact on the heated smooth aluminum alloy plate with a high-speed imaging system. Two groups of mixed solutions with similar boiling point differences are selected as the working liquid, in which the low-boiling-point components are both ethanol and the high-boiling point components are propanol and water, respectively. Compared to the ethanol/propanol binary droplets, the experimental results show that the ethanol/water binary droplets have diverse impact phenomena and significantly broad transition boiling regimes, as well as the reduced droplet residence time and increased Leidenfrost temperature point. With the decreasing ethanol content in ethanol/water binary droplets, these effects become more prominent. For secondary atomization, the ethanol/water binary droplet undergoes parent droplet breakup into fragment droplets with larger diameters (Ds > 0.3 mm). Both binary droplets produce satellite droplets with small diameters (Ds < 0.3 mm) by puffing and ejection. In terms of the ethanol/propanol binary droplet impact, the probability of puffing is higher and the satellite droplet diameters are small. In the ethanol/water binary droplet impact, the probability of ejection is higher and the satellite droplet diameter distribution is wider. When an ethanol/water binary droplet of 25 vol.% ethanol content impacts the heated wall at Ts = 120 °C, a novel large bubble shrinkage phenomenon occurs at the late stage of droplet evaporation. This phenomenon is proposed to be relevant to the increasing surface tension and saturation temperature with decreasing ethanol content, as well as the decreasing ambient temperature above the top surface of the bubble.
Assessing the Sustainability of Alternative Shaft Construction Methods
Authors: Pedro António M. G. , Repsold Lucas , Almeida e Sousa Jorge
Published: May 2024

Reducing carbon dioxide (CO2) emissions is a global priority. The concrete industry has a major role in this reduction since it accounts for about 8% of global CO2 emissions. Despite significant improvements in the sustainability of the production of concrete, one of the best solutions is still to improve the design and construction methods, such that the required quantities of concrete are reduced. Using, as a reference, a real case study, this study compares alternative shaft construction methodologies from engineering and sustainability points of view, highlighting the advantages and drawbacks of each solution. To achieve this purpose, a back analysis is performed to ensure that the numerical model is accurately calibrated and the shaft construction methods can be adequately assessed. The results show that, while the considered methods are applicable and satisfy engineering requirements, the characteristics of the lining of the shaft could have been optimized, resulting in a reduction in CO2 emissions by at least 50% without compromising the safety of the construction.
Risk of Tree Fall on High-Traffic Roads: A Case Study of the S6 in Poland
Authors: Kogut Tomasz , Wancel Dagmara , Stępień Grzegorz , Smuga-Kogut Małgorzata , Szostak Marta , Całka Beata
Published: May 2024

Modern technologies, such as airborne laser scanning (ALS) and advanced data analysis algorithms, allow for the efficient and safe use of resources to protect infrastructure from potential threats. This publication presents a study to identify trees that may fall on highways. The study used free measurement data from airborne laser scanning and wind speed and direction data from the Institute of Meteorology and Water Management in Poland. Two methods were used to determine the crown tops of trees: PyCrown and OPALS. The effect of wind direction on potential hazards was then analyzed. The OPALS method showed the best performance in terms of detecting trees, with an accuracy of 74%. The analysis showed that the most common winds clustered between 260° and 290°. Potential threats, i.e., trees that could fall on the road, were selected. As a result of the analysis, OPALS detected between 140 and 577 trees, depending on the chosen strategy. The presented research shows that combining ALS technology with advanced algorithms and wind data can be an effective tool for identifying potential hazards associated with falling trees on highways.
Traffic Flow Prediction with Random Walks on Graph and Spatiotemporal Bidirectional Attention Transformer
Authors: Yang Shudong , Zhou Yimin , Wu Zhengbin
Published: May 2024

Traffic flow prediction is crucial in intelligent transportation systems. Considering the severe disruptions caused by traffic accidents or congestion, a time series model is developed for traffic flow prediction based on multiple random walks on graphs (MRWG) and the bidirectional spatiotemporal attention mechanism (BSAM), which can adapt to both normal and exceptional situations. The MRWG mechanism is applied to capture spatial features of urban areas during traffic accidents and congestion, especially the spatial dependencies among neighboring regions. Further, a local position attention module is applied to acquire the spatial correlations between different regions to investigate their impact on the global area, while a local temporal attention module is adopted to extract short-term periodic time correlations from traffic flow data. Finally, a spatiotemporal bidirectional attention module is applied to simultaneously extract both the temporal and spatial correlations of the historical traffic flow data in order to generate the output prediction. Experiments have been conducted on NYCTaxi and NYCBike datasets with abnormal events, and the results indicate that the developed model can efficiently predict traffic flow in abnormal events, especially short-term traffic disruptions, outperforming the baseline methods under both abnormal and normal conditions.
Vehicle Route Planning for Relief Item Distribution under Flood Uncertainty
Authors: Toathom Thanan , Champrasert Paskorn
Published: May 2024

Flooding, a pervasive and severe natural disaster, significantly damages environments and infrastructure and endangers human lives. In affected regions, disruptions to transportation networks often lead to critical shortages of essential supplies, such as food and water. The swift and adaptable delivery of relief goods via vehicle is vital to sustain life and facilitate community recovery. This paper introduces a novel model, the Vehicle Routing Problem for Relief Item Distribution under Flood Uncertainty (VRP-RIDFU), which focuses on optimizing the speed of route generation and minimizing waiting times for aid delivery in flood conditions. The Genetic Algorithm (GA) is employed because it effectively handles the uncertainties typical of NP-Hard problems. This model features a dual-population strategy: random and enhanced populations, with the latter specifically designed to manage uncertainties through anticipated route performance evaluations, incorporating factors like waiting times and flood risks. The Population Sizing Module (PSM) is implemented to dynamically adjust the population size based on the dispersion of affected nodes, using standard deviation assessments. Introducing the Complete Subtour Order Crossover (CSOX) method improves solution quality and accelerates convergence. The model’s efficacy is validated through simulated flood scenarios that emulate various degrees of uncertainty in road conditions, affirming its practicality for real-life rescue operations. Focusing on prioritizing waiting times over travel times in routing decisions has proven effective. The model has been tested using standard CVRP problems with 20 distinct sets, each with varying node numbers and patterns, demonstrating superior performance and efficiency in generating vehicle routing plans compared to the shortest routes, which serve as the benchmark for optimal solutions. The results highlight the model’s capability to deliver high-quality solutions more rapidly across all tested scenarios.
A Comparison of the Relationship between Dynamic and Static Rock Mechanical Parameters
Authors: Shen Shijie , Gao Yanfang , Jia Lichun
Published: May 2024

The rock’s mechanical properties play an important role in the whole process of conventional and unconventional oil and gas exploration and progression. At present, there are two approaches to determining the mechanical parameters. One is to measure the rock sample in the laboratory (i.e., static elastic modulus Es). The other is to obtain parameters by geophysical logging data (i.e., dynamic elastic modulus Ed). In general, static parameters can more accurately reflect the mechanical properties of rock under actual geo-stresses. At the same time, their determinations are difficult. Therefore, one of the best methods is to establish the correlation between the dynamic and static parameters. This paper investigates the relation between the dynamic and static parameters using different methods for various rocks in the literature. Based on the relationship of Es=aEd+b, a correction between a and b is proposed using the multinomial form a=0.67+0.101b−0.006b2+0.0001b3. It is found that the Es can be derived from the Ed just when the parameter b is known. In terms of different types of rocks, for igneous and metamorphic rocks, the best correlation between Es and Ed obeys the power-law correlation; for sedimentary rocks, there are linear and logarithmic correlations. Theoretically, the difference between dynamic and static elastic moduli can be attributed to microcracks and pores within rocks. This study can provide guidance for engineers to predict the desired static parameters precisely using logging or seismic data.
Time–Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks
Authors: Wei Ming , Sun Xinlei , Zong Jianye
Published: May 2024

Existing deep learning-based seismic signal denoising methods primarily operate in the time domain. Those methods are ineffective when noise overlaps with the seismic signal in the time domain. Time–frequency domain-based deep learning methods are relatively rare and usually employ single loss function, resulting in suboptimal performance on low SNR signals and potential damage to P wave. This paper proposes a method based on generative adversarial networks (GANs). Compared to convolutional neural networks, the discriminator in GANs helps retain more true signal details by judging denoising performance. Additionally, an attention mechanism is introduced to fully extract signal features, and a perceptual loss is employed to evaluate the difference between the denoised result and the target’s high-level features. Experimental results show that this method can effectively improve SNR and ensure that the denoised result is close to the true signal. Furthermore, by comparing DeepDenoiser and ARDU, it is proven that the proposed method achieves better denoising performance, especially for low SNR signals, while causing less damage to the seismic signals.