bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of climatic variables on maize grain yield and its components by adjusting the sowing date
Authors: Wu Wenming , Yue Wei , Bi Jianjian , Zhang Lin , Xu Dafeng , Peng Chen , Chen Xiang , Wang Shiji
Published: June 2024

Yield and its components are greatly affected by climate change. Adjusting the sowing date is an effective way to alleviate adverse effects and adapt to climate change. Aiming to determine the optimal sowing date of summer maize and clarify the contribution of climatic variables to grain yield and its components, a consecutive 4-year field experiment was conducted from 2016 to 2019 with four sowing dates at 10-day intervals from 5 June to 5 July. Analysis of historical meteorological data showed that more solar radiation (SR) was distributed from early June to mid-August, and the maximum temperature (Tmax) > 32°C appeared from early July to late August, which advanced and lasted longer in 1991–2020 relative to 1981–1990. Additionally, the precipitation was mainly distributed from early June to late July. The climate change in the growing season of summer maize resulted in optimal sowing dates ranging from 5 June to 15 June, with higher yields and yield stability, mainly because of the higher kernel number per ear and 1,000-grain weight. The average contribution of kernel number per ear to grain yield was 58.7%, higher than that of 1,000-grain weight (41.3%). Variance partitioning analysis showed that SR in 15 days pre-silking to 15 days post-silking (SS) and silking to harvest (SH) stages significantly contributed to grain yield by 63.1% and 86.4%. The extreme growing degree days (EDD) > 32°C, SR, precipitation, and diurnal temperature range (DTR) contributed 20.6%, 22.9%, 14.5%, and 42.0% to kernel number per ear in the SS stage, respectively. Therefore, we concluded that the early sowing dates could gain high yield and yield stability due to the higher SR in the growing season. Meanwhile, due to the decreasing trend in SR and increasing Tmax trend in this region, in the future, new maize varieties with high-temperature resistance, high light efficiency, shade tolerance, and medium-season traits need to be bred to adapt to climate change and increased grain yield.
How-tests for consciousness and direct neurophenomenal structuralism
Authors: Fink Sascha Benjamin
Published: June 2024

Despite recent criticism, the search for neural correlates of consciousness (NCCs) is still at the core of a contemporary neuroscience of consciousness. One common aim is to distinguish merely statistical correlates from “NCCs proper”, i.e., NCCs that are uniquely associated with a conscious experience and lend themselves to a metaphysical interpretation. We should then distinguish between NCCs as data and NCCs as hypotheses, where the first is just recorded data while the second goes beyond any set of recorded data. Still, such NCC-hypotheses ought to be testable. Here, I present a framework for so-called “sufficiency tests.” We can distinguish four different classes of such tests, depending on whether they predict creature consciousness (which systems are conscious), state consciousness (when a system is conscious), phenomenal content (what a system is conscious of), or phenomenal character (how a system experiences). For each kind of test, I provide examples from the empirical literature. I also argue that tests for phenomenal character (How-Tests) are preferable because they bracket problematic aspects of the other kinds of tests. However, How-Tests imply a metaphysical tie between the neural and phenomenal domain that is stronger than supervenience, delivers explanations but does not close the explanatory gap, uses first-person methods to test hypotheses, and thereby relies on a form of direct neurophenomenal structuralism.
The relationship between teachers’ zest for work and teaching motivation: the mediating role of achievement goals
Authors: Aytaç Alper , Şahin Çiğdem , Görgülü Deniz , Dilber Yaşar , Direk Ayhan
Published: June 2024

Objective

This study aims to investigate the mediating role of achievement goals in the relationship between teachers’ zest for work and teaching motivation.

Method

The research was designed using the relational survey model. The research sample consisted of 518 teachers working in various cities in Turkey in 2023–2024 academic year fall semester. A convenience sampling method was used in sampling. Three Likert-type scales were used as data collection tools. In the data analysis, firstly, the data suitability to normal distributions was examined. As a result of the analysis, kurtosis and skewness values were examined and it was assumed that the data were normally distributed. SPSS Process extension was used to analyze the data.

Results and discussion

According to the research results, teachers’ zest for work positively and significantly predicted strong and positive achievement goals. In addition, teachers’ achievement goals significantly and positively predicted their teaching motivation. Additionally, teachers’ zest for work positively and significantly predicted their teaching motivation. Moreover, it can be inferred that achievement goals for students have a mediating role in the relationship between teachers’ zest for work and teaching motivation. In this context, it is suggested that policies that increase teachers’ teaching motivation should be prioritized.
Development and validation of the Workplace Relational Needs Satisfaction Scale (W-RNSS)
Authors: Hanc Veronika , Žvelc Gregor , Bajec Boštjan
Published: June 2024

While inadequate relationships in the workplace pose a significant psychosocial risk, quality interpersonal relationships can contribute to positive effects and prevent negative outcomes. Erskine’s model of relational needs, not yet studied in the work environment, can provide a more detailed understanding of the needs employees experience in their workplace relationships. We adapted the general Relational Needs Satisfaction Scale (RNSS) for coworker relationships and examined the factor structure of relational needs in the workplace and their connections to various work aspects. The sample comprised 273 participants, including both employees and students, in a workplace setting with coworkers. The results show that the Workplace Relational Needs Satisfaction Scale (W-RNSS) is a valid and reliable instrument (α = 0.93 for the total scale and 0.77 < α < 0.89 for the subscales) for measuring relational needs in coworker relationships. A bi-factor model was the most suitable for describing the data (χ2/df = 1.94, CFI = 0.95, TLI = 0.94, NNFI = 0.94, RMSEA = 0.06, SRMR = 0.04, AIC = 13289.27, BIC = 13506.29), confirming the previously supported 5-factor structure and the general factor. Satisfaction of relational needs was associated with higher work satisfaction, increased work engagement, greater motivation and lower burnout, underscoring the importance of quality interpersonal relationships among employees. W-RNSS shows potential for researching connections with other work aspects and practical applications in prevention and intervention strategies.
Finding the best predictive model for hypertensive depression in older adults based on machine learning and metabolomics research
Authors: Guo Jiangling , Zhao Jingwang , Han Peipei , Wu Yahui , Zheng Kai , Huang Chuanjun , Wang Yue , Chen Cheng , Guo Qi
Published: June 2024

Objective

Depression is a common comorbidity in hypertensive older adults, yet depression is more difficult to diagnose correctly. Our goal is to find predictive models of depression in hypertensive patients using a combination of various machine learning (ML) methods and metabolomics.

Methods

Methods We recruited 379 elderly people aged ≥65 years from the Chinese community. Plasma samples were collected and assayed by gas chromatography/liquid chromatography-mass spectrometry (GC/LC-MS). Orthogonal partial least squares discriminant analysis (OPLS-DA), volcano diagrams and thermograms were used to distinguish metabolites. The attribute discriminators CfsSubsetEval combined with search method BestFirst in WEKA software was used to find the best predicted metabolite combinations, and then 24 classification methods with 10-fold cross-validation were used for prediction.

Results

34 individuals were considered hypertensive combined with depression according to our criteria, and 34 subjects with hypertension only were matched according to age and sex. 19 metabolites by GC-MS and 65 metabolites by LC-MS contributed significantly to the differentiation between the depressed and non-depressed cohorts, with a VIP value of more than 1 and a P value of less than 0.05. There were multiple metabolic pathway alterations. The metabolite combinations screened with WEKA for optimal diagnostic value included 12 metabolites. The machine learning methods with AUC values greater than 0.9 were bayesNet and random forests, and their other evaluation measures are also better.

Conclusion

Altered metabolites and metabolic pathways are present in older adults with hypertension combined with depression. Methods using metabolomics and machine learning performed quite well in predicting depression in hypertensive older adults, contributing to further clinical research.
Psychological dimensions in alcohol use disorder: comparing active drinkers and abstinent patients
Authors: Zizzi Alessio , Berri Isabel Margherita , Berri Alessandro , Occhipinti Martina , Escelsior Andrea , Guglielmo Riccardo , Pereira Da Silva Beatriz , Amore Mario , Serafini Gianluca
Published: June 2024

Background

Alcohol use disorder (AUD) is a major public health concern due to its various physical, psychological, and social consequences. Despite regulatory differences, abstinence remains the primary treatment objective. Addressing the multifaceted nature of alcohol use disorder requires a comprehensive approach.

Methods

150 AUD patients (66%male) with a mean age of 54.10 ± 11.3 years were recruited for the study. Depression, impulsivity, alexithymia, and hopelessness were assessed to determine if there were significant differences in these dimensions between abstinent (N=72) and active drinkers (N=78).

Results

The study found significant differences in the psychological dimensions scores, active drinkers exhibited higher levels of depression, impulsivity, alexithymia, and hopelessness compared to abstinent patients.

Conclusion

Treatment outcomes for patients with AUD vary between regulatory agencies, but abstinence remains the safest and most preferred objective in managing AUD. Prioritizing abstinence-oriented interventions is crucial for achieving long term recovery and minimizing relapse risk. These results emphasize the intricate relationship between AUD and mental health issues, highlighting the need for comprehensive interventions addressing both alcohol consumption and associated psychological distress. Promoting abstinence (or at least reducing alcohol consumption) not only preserves mental health but also prevents life-threatening consequences such as suicide.
Pilot evaluation of a Psychological First Aid online training for COVID-19 frontline workers in American Indian/Alaska Native communities
Authors: O'Keefe Victoria M. , Grubin Fiona , Vaidya Nainika , Maudrie Tara L. , Conrad Maisie , Neuner Sophie , Jridi Shardai , Cook Mary Ann , Carson Kathryn A. , Barlow Allison , Haroz Emily E.
Published: June 2024

Introduction

The COVID-19 pandemic exacerbated mental health concerns and stress among American Indians and Alaska Natives (AI/ANs) in the United States, as well as among frontline workers responding to the pandemic. Psychological First Aid (PFA) is a promising intervention to support mental wellbeing and coping skills during and after traumatic events, such as the COVID-19 pandemic. Since PFA is often implemented rapidly in the wake of a disaster or traumatic event, evidence evaluating its impact is lacking. This paper reports pilot evaluation results from a culturally adapted PFA training designed to support COVID-19 frontline workers and the AI/AN communities they serve during the pandemic.

Methods

This study was designed and implemented in partnership with a collaborative work group of public health experts and frontline workers in AI/AN communities. We conducted a pre-post, online pilot evaluation of a culturally adapted online PFA training with COVID-19 frontline workers serving AI/AN communities. Participants completed a baseline survey and two follow-up surveys 1 week and 3 months after completing the PFA training. Surveys included demographic questions and measures of anxiety, burnout, stress, positive mental health, communal mastery, coping skills, PFA knowledge, confidence in PFA skills, and satisfaction with the PFA training.

Results

Participants included N = 56 COVID-19 frontline workers in AI/AN communities, 75% were AI/AN, 87% were female, and most (82%) were between the ages of 30–59. Participants reported high satisfaction with the training and knowledge of PFA skills. Pilot results showed significant increases in positive mental health and social wellbeing and reductions in burnout from baseline to 3 months after completing the PFA training among frontline workers. There were no changes in communal mastery, coping skills, stress, or anxiety symptoms during the study period.

Discussion

To our knowledge, this is the first pilot evaluation of a PFA training designed and culturally adapted with and for AI/AN communities. Given that many AI/AN communities were disproportionately impacted by COVID-19 and prior mental health inequities, addressing acute and chronic stress is of crucial importance. Addressing traumatic stress through culturally adapted interventions, including Indigenous PFA, is crucial to advancing holistic wellbeing for AI/AN communities.
Implementation of basic life support education for the lay public in China: barriers, enablers, and possible solutions
Authors: Dong Xuejie , Zhang Lin , Wang Zongbin , Zheng Zhi-jie
Published: June 2024

Background

Education for the lay public in basic life support (BLS) is critical for increasing bystander cardiopulmonary resuscitation (CPR) rates and improving survival from out-of-hospital cardiac arrest (OHCA). Despite years of implementation, the BLS training rate in China has remained modest. The aim of this study was to investigate the factors influencing the implementation of BLS training programs in emergency medical service (EMS) centers in China and to identify specific barriers and enablers.

Methods

Qualitative interviews were conducted with key informants from 40 EMS centers in Chinese cities. The participants included 11 directors/deputy directors, 24 training department leaders, and 5 senior trainers. The interview guide was based on the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. Thematic content analysis was used to identify themes and patterns across the interviews.

Results

We identified 16 factors influencing the implementation of BLS training programs encompassing the outer content, inner context, innovation and bridging factors. Some factors acted as either barriers or enablers at different EPIS stages. The main implementation barriers included limited external leadership, insufficient government investment, low public awareness, a shortage of trainers, an absence of incentives, an absence of authoritative courses and guidelines, a lack of qualification to issue certificates, limited academic involvement, and insufficient publicity. The main enablers were found to be supportive government leaders, strong public demand, adequate resources, program champions, available high-quality courses of high fitness within the local context, the involvement of diverse institutions, and effective publicity and promotion.

Conclusion

Our findings emphasize the diversity of stakeholders, the complexity of implementation, and the need for localization and co-construction when conducting BLS training for lay public in city EMS centers. Improvements can be made at the national level, city level, and EMS institutional level to boost priority and awareness, promote legislation and policies, raise sustainable resources, and enhance the technology of BLS courses.
The determinants of COVID-19 case reporting across Africa
Authors: Han Qing , Rutayisire Ghislain , Mbogning Fonkou Maxime Descartes , Avusuglo Wisdom Stallone , Ahmadi Ali , Asgary Ali , Orbinski James , Wu Jianhong , Kong Jude Dzevela
Published: June 2024

Background

According to study on the under-estimation of COVID-19 cases in African countries, the average daily case reporting rate was only 5.37% in the initial phase of the outbreak when there was little or no control measures. In this work, we aimed to identify the determinants of the case reporting and classify the African countries using the case reporting rates and the significant determinants.

Methods

We used the COVID-19 daily case reporting rate estimated in the previous paper for 54 African countries as the response variable and 34 variables from demographics, socioeconomic, religion, education, and public health categories as the predictors. We adopted a generalized additive model with cubic spline for continuous predictors and linear relationship for categorical predictors to identify the significant covariates. In addition, we performed Hierarchical Clustering on Principal Components (HCPC) analysis on the reporting rates and significant continuous covariates of all countries.

Results

21 covariates were identified as significantly associated with COVID-19 case detection: total population, urban population, median age, life expectancy, GDP, democracy index, corruption, voice accountability, social media, internet filtering, air transport, human development index, literacy, Islam population, number of physicians, number of nurses, global health security, malaria incidence, diabetes incidence, lower respiratory and cardiovascular diseases prevalence. HCPC resulted in three major clusters for the 54 African countries: northern, southern and central essentially, with the northern having the best early case detection, followed by the southern and the central.

Conclusion

Overall, northern and southern Africa had better early COVID-19 case identification compared to the central. There are a number of demographics, socioeconomic, public health factors that exhibited significant association with the early case detection.
The association between SII and aging: evidence from NHANES 1999–2018
Authors: Wang Nanbu , Ren Lian , Li Ziyuan , Hu Yunhao , Zhou Jingpei , Sun Quan , Pei Bin , Li Xinyu , Peng Wanqing , Yu Jinyan , Zhao Renhui , Huang Ziting , Chen Zhenhu , Huang Guoxin
Published: June 2024

Background

The study aimed to examine the association between the systemic immune-inflammation index (SII), a contemporary metric of systemic inflammatory response, and biological aging, which are closely interconnected processes.

Methods

This cross-sectional study utilized 10 cycles of data from the NHANES database spanning from 1990 to 2018. The study examined the relationship between the SII index, calculated as P * N/L, where P represents preoperative peripheral platelet count, N represents neutrophil count, and L represents lymphocyte count, and biological aging. Biological aging was assessed through various methods, such as phenotypic age, phenotypic age acceleration (PhenoAgeAccel), biological age, and biological age acceleration (BioAgeAccel). Correlations were analyzed using weighted linear regression and subgroup analysis.

Results

Among the 7,491 participants analyzed, the average age was 45.26 ± 0.34 years, with 52.16% being female. The average phenotypic and biological ages were 40.06 ± 0.36 and 45.89 ± 0.32 years, respectively. Following adjustment for potential confounders, elevated SII scores were linked to increased phenotypic age, biological age, Phenotypic age acceleration, and Biological age acceleration. Positive correlations were observed between health behavior and health factor scores and biological aging, with stronger associations seen for health factors. In health factor-specific analyses, the β coefficient was notably higher for high BMI. The robust positive associations between SII scores and both phenotypic age and biological age in the stratified analyses were consistently observed across all strata.

Conclusion

The evidence from the NHANES data indicate that SII may serve as a valuable marker for assessing different facets of aging and health outcomes, such as mortality and the aging process. Additional research is warranted to comprehensively elucidate the implications of SII in the aging process and its utility as a clinical instrument for evaluating and addressing age-related ailments.
Identification of formation mechanism and key elements of quality geriatric care behavior of nursing assistants in nursing homes: a grounded theory study
Authors: Liao Lulu , Bai Xue , He Xiaoxiao , Tan Lei , Yang Linghua , Long Huan , Huang Shenglan , Li Xia , Han Ying , Peng Xiao , Chen Huijing , Yang Xiufen , Li Hui , Wang Shuang , Liu Yilan
Published: June 2024

Objective(s)

This study aimed to identify the key elements and develop a formation mechanism model of quality geriatric care behavior for nursing assistants.

Methods

This qualitative research employed the strategy of grounded theory proposed by Strauss and Corbin. Furthermore, the data was collected by participatory observation and semi-structured interviews. A total of 12 nursing managers, 63 nursing assistants, and 36 older people from 9 nursing homes in 6 cities were interviewed, whereas for the observatory survey, participants were recruited from 2 nursing homes.

Results

The comparative and analysis process revealed 5 key elements of quality geriatric care behavior, including holistic care, personalized care, respect, positive interaction, and empowerment. Based on the Capability-Opportunity-Motivation-Behavior (COM-B) model, key elements and the 3 stages of quality geriatric care behavior (negative behavior cognition stage, practice exchange run-in stage, and positive behavior reinforcement stage), the theoretical framework of the formation mechanism was established.

Conclusion

The results indicated that nursing assistants’ capabilities, motivation, and organizational and environmental support are vital for quality care behaviors. The theoretical framework established in this study provides theoretical support and practical reference to policymakers, institutional administrators, and healthcare professionals for improving nursing assistant’s care behaviors.
Addressing researcher fraud: retrospective, real-time, and preventive strategies–including legal points and data management that prevents fraud
Authors: Kennedy James E.
Published: June 2024

Researcher fraud is often easy and enticing in academic research, with little risk of detection. Cases of extensive fraud continue to occur. The amount of fraud that goes undetected is unknown and may be substantial. Three strategies for addressing researcher fraud are (a) retrospective investigations after allegations of fraud have been made, (b) sting operations that provide conclusive evidence of fraud as it occurs, and (c) data management practices that prevent the occurrence of fraud. Institutional and regulatory efforts to address researcher fraud have focused almost exclusively on the retrospective strategy. The retrospective approach is subject to controversy due to the limitations of post-hoc evidence in science, the difficulty in establishing who actually committed the fraud in some cases, the application of a legal standard of evidence that is much lower than the usual standards of evidence in science, and the lack of legal expertise by scientists investigating fraud. The retrospective strategy may be reliably effective primarily in cases of extensive, careless fraud. Sting operations can overcome these limitations and controversies, but are not feasible in many situations. Data management practices that are effective at preventing researcher fraud and unintentional errors are well-established in clinical trials regulated by government agencies, but appear to be largely unknown or unimplemented in most academic research. Established data management practices include: archiving secure copies of the raw data, audit trails, restricted access to the data and data collection processes, software validation, quality control checks, blinding, preregistration of data processing and analysis programs, and research audits that directly address fraud. Current discussions about data management in academic research focus on sharing data with little attention to practices that prevent intentional and unintentional errors. A designation or badge such as error-controlled data management could be established to indicate research that was conducted with data management practices that effectively address intentional and unintentional errors.
Assessing the contribution of land and water management approaches to sustainable land management and achieving land degradation neutrality
Authors: Hartmann Lisa , Walz Yvonne , Hansohm Jonas , Domingos Vellozo Leticia , Walinder Elizabeth , Andreeva Olga , Harari Nicole , Hendrickson John , Kinyua Ivy , Parrotta John , Rath Daniel , Sylvén Magnus , Tumuhe Charles L. , de Vente Joris , Orr Barron Joseph
Published: June 2024

The framework of land degradation neutrality (LDN) and the concept of sustainable land management (SLM) are ways to instigate action required to address land degradation. Although land and water management approaches supporting SLM and the achievement of LDN exist, the transition to sustainable agricultural systems is hindered by various factors and the achievement of LDN is lagging behind. More information on such approaches is needed to sensitize decision-makers for fostering their implementation. This study responds to this need by examining the alignment of the following land and water management approaches with SLM and LDN: agroecology, climate-smart agriculture, conservation agriculture, forest landscape restoration, integrated agriculture, regenerative agriculture, and rewilding. The alignment assessment used a formative methodological approach combining literature review and extensive expert consultations, and is structured along the SLM and LDN pillars of ecosystem health, food security, and human-wellbeing, each comprised by several criteria, as well as selected cross-cutting socioeconomic criteria that span all pillars. The results indicate that each of the approaches contributes to SLM and the achievement of LDN in different ways and to varying degrees, with none of the approaches embracing principles or practices that directly conflict with the criteria of SLM and LDN. A higher degree of alignment was identified for the ecosystem health and food security pillars, while most gaps in alignment concern criteria of the human wellbeing pillar along with certain cross-cutting criteria. The results of the assessment led to the identification of entry points for addressing gaps in alignment via supplementary activities that directly target the gaps during project planning and implementation, as well as through adhering to principles and established guidelines. Importantly, conclusions about the degree of alignment or about gaps in alignment of an approach with SLM and LDN criteria are conceptually indicative, but may change in actual practice depending on where and how projects are implemented. Notwithstanding, clarifying the approaches' contribution to SLM and the achievement of LDN can help overcome the lack of formal intergovernmental recognition of the approaches, prevent misinterpretation, and ensure their strategic inclusion in broader efforts to remedy land degradation.
Reverse genetics construction and pathogenicity of a novel recombinant NADC30-like PRRSV isolated in China
Authors: Guo Jinyao , Li Chenxi , Lu Huipeng , Wang Bin , Zhang Linjie , Ding Jingjing , Jiao Xue , Li Qingyu , Zhu Shanyuan , Wang Anping , Li Yanhua
Published: June 2024

China has the largest pig herd in the world which accounts for more than 50% of the global pig population. Over the past three decades, the porcine reproductive and respiratory syndrome virus (PRRSV) has caused significant economic loss to the Chinese swine industry. Currently, the prevalent PRRSV strains in the field are extremely complicated, and the NADC30-like strains, NADC34-like strains, and novel recombinant viruses have become a great concern to PRRS control in China. In this study, a novel NADC30-like PRRSV, named GS2022, was isolated from the lung of a dead pig collected from a farm that experienced a PRRS outbreak. The complete genome of GS2022 shares the highest identity with the NADC30 strain and contains a discontinuous deletion of 131 aa in nsp2. Novel deletion and insertion have been identified in ORF7 and 3’UTR. Recombination analysis revealed that the GS2022 is a potential recombinant of NADC30-like and JXA1-like strains. Both inter-lineage and intra-lineage recombination events were predicted to be involved in the generation of the GS2022. An infectious cDNA clone of GS2022 was assembled to generate the isogenic GS2022 (rGS2022). The growth kinetics of rGS2022 were almost identical to those of GS2022. The pathogenicity of the GS2022 and rGS2022 was evaluated using a nursery piglet model. In the infection groups, the piglets exhibited mild clinical symptoms, including short periods of fever and respiratory diseases. Both gross lesions and histopathological lesions were observed in the lungs and lymph nodes of the infected piglets. Therefore, we reported a novel recombinant NADC30-like PRRSV strain with moderate pathogenicity in piglets. These results provide new information on the genomic characteristics and pathogenicity of the NADC30-like PRRSV in China.
LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications
Authors: Aldamani Raghad , Abuhani Diaa Addeen , Shanableh Tamer
Published: June 2024

This study presents a comprehensive analysis of utilizing TensorFlow Lite on mobile phones for the on-edge medical diagnosis of lung diseases. This paper focuses on the technical deployment of various deep learning architectures to classify nine respiratory system diseases using X-ray imagery. We propose a simple deep learning architecture that experiments with six different convolutional neural networks. Various quantization techniques are employed to convert the classification models into TensorFlow Lite, including post-classification quantization with floating point 16 bit representation, integer quantization with representative data, and quantization-aware training. This results in a total of 18 models suitable for on-edge deployment for the classification of lung diseases. We then examine the generated models in terms of model size reduction, accuracy, and inference time. Our findings indicate that the quantization-aware training approach demonstrates superior optimization results, achieving an average model size reduction of 75.59%. Among many CNNs, MobileNetV2 exhibited the highest performance-to-size ratio, with an average accuracy loss of 4.1% across all models using the quantization-aware training approach. In terms of inference time, TensorFlow Lite with integer quantization emerged as the most efficient technique, with an average improvement of 1.4 s over other conversion approaches. Our best model, which used EfficientNetB2, achieved an F1-Score of approximately 98.58%, surpassing state-of-the-art performance on the X-ray lung diseases dataset in terms of accuracy, specificity, and sensitivity. The model experienced an F1 loss of around 1% using quantization-aware optimization. The study culminated in the development of a consumer-ready app, with TensorFlow Lite models tailored to mobile devices.
Qualitative Perturbation Analysis and Machine Learning: Elucidating Bacterial Optimization of Tryptophan Production
Authors: Ramos-Valdovinos Miguel Angel , Salas-Navarrete Prisciluis Caheri , Amores Gerardo R. , Hernández-Orihuela Ana Lilia , Martínez-Antonio Agustino
Published: June 2024

L-tryptophan is an essential amino acid widely used in the pharmaceutical and feed industries. Enhancing its production in microorganisms necessitates activating and inactivating specific genes to direct more resources toward its synthesis. In this study, we developed a classification model based on Qualitative Perturbation Analysis and Machine Learning (QPAML). The model uses pFBA to obtain optimal reactions for tryptophan production and FSEOF to introduce perturbations on fluxes of the optima reactions while registering all changes over the iML1515a Genome-Scale Metabolic Network model. The altered reaction fluxes and their relationship with tryptophan and biomass production are translated to qualitative variables classified with GBDT. In the end, groups of enzymatic reactions are predicted to be deleted, overexpressed, or attenuated for tryptophan and 30 other metabolites in E. coli with a 92.34% F1-Score. The QPAML model can integrate diverse data types, promising improved predictions and the discovery of complex patterns in microbial metabolic engineering. It has broad potential applications and offers valuable insights for optimizing microbial production in biotechnology.
Optimization Design of Permanent Magnet Synchronous Motor Based on Multi-Objective Artificial Hummingbird Algorithm
Authors: Zhang Shaoru , Yan Hui , Yang Likun , Zhao Hua , Du Xiuju , Zhang Jielu
Published: June 2024

The interior permanent magnet synchronous motor (IPMSM) is known for its high output torque, strong overload capacity, and high power density, making it a popular choice in the electric vehicle industry. This paper proposes an improved multi-objective artificial hummingbird algorithm that combines chaotic mapping, adaptive weights, and dynamic crowding entropy. An optimization strategy that combines the Taguchi method with the Improved Multi-Objective Artificial Hummingbird Algorithm (IMOAHA), is proposed to minimize torque ripple and back electromotive force in the interior permanent magnet synchronous motor while simultaneously increasing the average torque of the motor. Taking the 8-pole 48-slot interior permanent magnet synchronous motor as an example, the optimization objectives include back electromotive force, average torque, and torque ripple. The rotor-related structural parameters are used as optimization variables. First, the Taguchi method is employed to identify parameters that significantly influence the optimization objectives. Subsequently, response surface fitting is used to establish the relationship between the optimization objectives and parameters. Finally, the multi-objective artificial hummingbird algorithm is utilized for optimization. By comparing the finite element analysis of the motor models before and after optimization, it is evident that the improved multi-objective artificial hummingbird algorithm can effectively enhance the performance of the interior permanent magnet synchronous motor.
Numerical Study on Rotor–Building Coupled Flow Field and Its Influence on Rotor Aerodynamic Performance under an Atmospheric Boundary Layer
Authors: Liu Yang , Shi Yongjie , Aziz Aqib , Xu Guohua
Published: June 2024

In urban settings, buildings create complex turbulent conditions, affecting helicopter flight performance during missions and increasing safety risks during takeoff and landing. A numerical study on rotor–building coupled flow field is carried out to address rotor aerodynamic performance under building interferences in natural atmospheric conditions. A high-fidelity atmospheric boundary layer (ABL) model described by an exponential law is established herein. The solution of the coupled flow field is based on the Reynolds-averaged Navier–Stokes (RANS) equations, with the rotor’s rotation achieved through the overset grid method. Based on the dominant wind features, the building flow field is distributed into four regions, where the updraft along the headwind side impacts the rotor, bringing about a 76% increase in pitching moment. On the lateral side of the building, distorted rotor wake squeezed upward into the rotor disk, leading to severe blade–vortex interaction (BVI). During low-altitude hovering over rooftops, the mixing of building shed vortices with forward flow wakes causes the formation of a circulation region on the rotor’s windward side, resulting in a thrust loss of approximately 7.8%. Meanwhile, the flow environment on the leeward side of the buildings is more stable. Therefore, it is recommended that helicopters adopt a headwind approach during rooftop operations. However, an 11.4% loss in the average hover figure of merit is observed due to consistent thrust losses caused by the recirculation region.
Non-Cooperative Spacecraft Pose Estimation Based on Feature Point Distribution Selection Learning
Authors: Yuan Haoran , Chen Hanyu , Wu Junfeng , Kang Guohua
Published: June 2024

To address the limitations of inadequate real-time performance and robustness encountered in estimating the pose of non-cooperative spacecraft during on-orbit missions, a novel method of feature point distribution selection learning is proposed. This approach utilizes a non-coplanar key point selection network with uncertainty prediction, pioneering in its capability to accurately estimate the pose of non-cooperative spacecraft, thereby representing a significant advancement in the field. Initially, the feasibility of designing a non-coplanar key point selection network was analyzed based on the influence of sensor layout on the pose measurement. Subsequently, the key point selection network was designed and trained, leveraging images extracted from the spacecraft detection network. The network detected 11 pre-selected key points with distinctive features and was able to accurately predict their uncertainties and relative positional relationships. Upon selection of the key points exhibiting low uncertainty and non-coplanar relative positions, we utilized the EPnP algorithm to achieve accurate pose estimation of the target spacecraft. Our experimental evaluation on the SPEED dataset, which comes from the International Satellite Attitude Estimation Competition, validates the effectiveness of our key point selection network, significantly enhancing estimation accuracy and timeliness compared to other monocular spacecraft attitude estimation methods. This advancement provides robust technological support for spacecraft guidance, control, and proximity operations in orbital service missions.
Development of Carbon Composite Blades within the Context of the Experimental Validation of a CFD-Based Design Tool for Contra-Rotating, Electric Fan Engines
Authors: Hawner Sebastian , Rduch Lukas , Baumhöfener Henry , Hupfer Andreas
Published: June 2024

Electric propulsion systems have emerged as a disruptive technological approach towards achieving sustainable and climate-neutral aviation. To expand the operational envelope of such propulsion units in terms of altitude and velocity, an enclosing duct and counter-rotating rotors to enhance efficiency can be utilized. In this study, an iterative CFD-based design tool developed for these novel propulsion systems is utilized to design a reference engine, having a classic rotor–stator configuration. Being the key component of this propulsor, a manufacturing process for composite blades is presented. This effort aims to make state-of-the-art technology accessible to smaller research projects, promoting the widespread adoption of electric propulsion technology in the aviation sector. By experimental investigations of the blade elongation both in tensile tests and engine operation, measuring the tip clearance with a high-speed camera, this process could be validated to facilitate the transferability of research. Finally, the performance of the manufactured engine is measured by a five-hole miniature probe, not only in design point but also in off-design operation. The results indicate that a substantial reduction in discrepancies between initial specifications, subsequent CFD simulations, and experimental investigations compared to conventional design tools relying on empirical formulations can be achieved due to the CFD-based approach. This allows the CFD-based tool to be validated for designing scalable contra-rotating fan engines.
Adaptive Scheduling Method for Passenger Service Resources in a Terminal
Authors: Mou Qifeng , Liang Qianyu , Tian Jie , Jing Xin
Published: June 2024

To alleviate the tense situation of limited passenger service resources in the terminal and to achieve the matching of resource scheduling with the flight support process, the process–resource interdependent network is constructed according to its mapping relationship and the time-varying characteristics of the empirical network and network evolution conditions are analyzed. Then, node capacity, node load, and the cascading failure process are investigated, the impact of average service rate and service quality standard on queue length is considered, the node capacity model is constructed under the condition of resource capacity constraints, and the load-redistribution resource adaptive scheduling method based on cascading failure is proposed. Finally, the method’s effectiveness is verified by empirical analysis, the service efficiency is assessed using the total average service time and variance, and the network robustness is assessed using the proportion of maximum connected subgraph. The results indicate that the resource adaptive scheduling method is effective in improving service efficiency, and the average value of its measurement is smaller than that of the resource average allocation method by 0.069; in terms of the robustness improvement of the interdependent network, the phenomenon of re-failure after the load redistribution is significantly reduced.
Analysis of Film Unloading Mechanism and Parameter Optimization of Air Suction-Type Cotton Plough Residual Film Recovery Machine Based on CFD—DEM Coupling
Authors: Fang Weiquan , Wang Xinzhong , Zhu Changshun , Han Dianlei , Zang Nan , Chen Xuegeng
Published: June 2024

The optimization of film-unloading and film–soil separation components can effectively improve the residual film unloading rate and reduce impurity content. So, the DEM models of soil and residual film were established and the suspension and flow characteristics under fluid action were analyzed based on the CFD—DEM coupling simulation in this article. The matching parameters of the film-unloading and film-lifting device were optimized with the Box–Behnken test. When the wind velocity was between 1.65 and 10.54 m·s−1, the film–soil separation effect was the best, with a film–impurity separation rate of 96.6%. The optimized parameter combination of the film-unloading device and film-lifting device is A = 9°, B = 40 mm, and C = 40 mm (A, B, and C represent the angle between the teeth and the normal of the air inlet, the minimum distance between the teeth and the air inlet, and the width of the air inlet, respectively). With the optimized parameter, the best film unloading effect is achieved, the minimum wind velocity of film unloading is 2.6 m·s−1. This article provides theoretical and simulation methods for assessing the flow characteristics of flexible particles and parameter optimization of air suction devices, which is conducive to the high-purity recovery of residual film.
The Influence of New Quality Productive Forces on High-Quality Agricultural Development in China: Mechanisms and Empirical Testing
Authors: Lin Li , Gu Tianyu , Shi Yi
Published: June 2024

Advancing the construction and application of new quality productive forces is an essential prerequisite for achieving high-quality agricultural development and expediting the establishment of agricultural powerhouses. This study aims to elucidate the internal mechanisms through which new quality productivity contributes to high-quality agricultural development and to explore practical pathways for enhancing agricultural quality through its promotion. Utilizing panel data spanning 2012 to 2021 from 30 provinces and municipalities in mainland China, the entropy method is employed to gauge levels of new quality productivity and high-quality agricultural development. Additionally, employing research methodologies including SYS-GMM and threshold effect models, this study empirically investigates how the advancement of new quality productivity influences high-quality agricultural development. Our research reveals the following key findings: (1) The development of new quality productive forces significantly enhances high-quality agricultural development, exhibiting a heterogeneous distribution pattern favoring the “eastern region > western region > central region” and “northern region > southern region”. (2) New quality productive forces can bolster the level of high-quality agricultural development by fostering innovation, coordination, openness, and shared development within its subsystems. However, they may impede progress by inhibiting improvements in green development within the subsystems. (3) The results of the threshold effect test demonstrate that the promotion effect of the development of new quality productive forces on high-quality agricultural development escalates with the level of high-quality agricultural development. Specifically, as the level of high-quality agricultural development exceeds the first threshold value of 0.1502, the promotion effect becomes significant; crossing the second threshold value of 0.2010 further amplifies this effect. This paper’s primary marginal contribution involves empirically analyzing the potential nonlinear effects of advancing new quality productivity in enhancing the level of high-quality agricultural development. This enriches empirical research on how new quality productivity fosters the development of high-quality agriculture.
Biofumigation Treatment Using Tagetes patula, Sinapis alba and Raphanus sativus Changes the Biological Properties of Replanted Soil in a Fruit Tree Nursery
Authors: Wieczorek Robert , Zydlik Zofia , Zydlik Piotr
Published: June 2024

Apple replant disease (ARD) may cause significant losses both in commercial orchards and in fruit tree nurseries. The negative effects of ARD may be limited by using biofumigation. The aim of the study was to assess the influence of this treatment on the biological properties of replanted soil in a tree nursery. In two-year experiment, apple trees of the ‘Golden Delicious’ cultivar were used. The trees were planted into soil from two sites. The soil from one site had not been used in a nursery before (crop rotation soil). The other soil had been used for the production of apple trees (replanted soil). Three species of plants were used in the replanted soil as a forecrop: French marigold (Tagetes patula), white mustard (Sinapis alba), and oilseed radish (Raphanus sativus var. oleifera). The following parameters were assessed in the experiment: the enzyme and respiratory activity of the soil, the total count of bacteria, fungi, oomycetes and actinobacteria in the soil, as well as the count and species composition of soil nematodes. The vegetative growth parameters of the apple trees were also assessed. The biological properties of the replanted soil were worse than those of the crop rotation soil. In the replanted soil, the organic matter content, enzyme and respiratory activity as well as the count of soil microorganisms were lower. The biofumigants, used as a forecrop on the replanted soil, significantly increased its enzyme activity and respiratory activity. Dehydrogenase activity increased more than twofold. Growth parameters of the trees were significantly improved. The height of the trees increased by more than 50%, and the leaf area, weight and total length of side shoots were higher as well. The density of nematodes in the replanted soil after biofumigation was significantly reduced, with a larger reduction in the marigold fumigated soil. Eight of the eleven nematode species were completely reduced in the first year after biofumigation treatment.