Miracle-dz/UniFAR-FTUs huggingface dataset:
Soil properties and microbial communities of spring maize filed in response to tillage with straw incorporation and nitrogen fertilization in northeast China

Soil enzymes and microorganisms are both important to maintaining good soil quality and are also sensitive to changes in agricultural management. The individual effects of tillage, straw incorporation and nitrogen (N) fertilization on soil enzymes and microflora have been widely acknowledged, but their interactive effect remains largely unknown. In a 5–year in–situ field study, effects of rotary (RTS) and plow tillage (PTS) practices with straw incorporation combined with three N fertilization levels (0 kg N ha–1, CK; 187 kg N ha–1, MN; 337 kg N ha–1, HN) on soil enzyme activities and microbial communities were assessed. Our results showed that the activities of β–glucosidase (βG), N–acetylglucosaminidase (NAG) and acid phosphatase (APH) were improved in RTS+MN. The bacterial and fungal abundances in RTS+MN and RTS+HN were 1.27–27.51 times higher than those in other treatment groups. However, the bacterial and fungal alpha diversities were enhanced in PTS+MN and PTS+CK compared with other treatments, respectively. Proteobacteria and Basidiomycota were the predominant phylum for the respective bacterial and fungal communities. Moreover, significant interactive effects were found in the fungal community composition, but only minor impacts were observed on the bacterial community composition. Soil water content and penetration resistance contributed more to the soil enzyme activity and microbial community than other soil properties investigated, whereas there was a significant positive correlation between βG and APH activities and microbial abundance. These findings can provide new insights into tillage with straw incorporation and N fertilization on maize cultivation in northeast China.
Geographical reconfiguration in global value chains: Search within limited space?

Negative performance gaps entice firms to undertake geographical reconfiguration of their global value chains (GVC) as a substitute for, or complement to, change of governance modes, decomposition of offshored activities, or shift of local service providers. In this study, we build on performance feedback theory and the concept of problemistic search to examine to which extent firms move offshored service activities to new countries when performance gaps are negative. We also examine if these relocations take place within a search space limited by the managers’ cognitive span. We formulate a set of hypotheses revolving around this idea of search within a limited space. Our hypotheses are supported when tested on a sample of global sourcing projects undertaken by 230 firms between 1995 and 2012.
Object Description Using Visual and Tactile Data

With the development of vision and haptic sensor technologies, robots have become increasingly capable of perceiving their external environment. Although machine vision and haptics have surpassed humans in some aspects of perception, it is difficult for robots to describe objects from multiple viewpoints using a combination of visual and haptic modalities. In this study, we use convolutional neural networks to separately extract visual and haptic features and then fuse these two types of features. Then, multitask learning is combined with multilabel classification to form a multitask-multilabel classification method. The developed method is used to identify the color, shape, material attributes, and class of an object from the visual-haptic fused feature vector. To verify the effectiveness of the proposed object description method, experiments are conducted on the PHAC-2 dataset and the collected VHAC dataset. The experimental results show that the proposed method produces the most accurate object descriptions with the smallest number of parameters.
A Glimpse of Molecular Biomarkers in Huntington’s Disease

Huntington’s disease (HD) is a devastating neurodegenerative disorder that is caused by an abnormal expansion of CAG repeats in the Huntingtin (HTT) gene. Although the main symptomatology is explained by alterations at the level of the central nervous system, predominantly affecting the basal ganglia, a peripheral component of the disease is being increasingly acknowledged. Therefore, the manifestation of the disease is complex and variable among CAG expansion carriers, introducing uncertainty in the appearance of specific signs, age of onset and severity of disease. The monogenic nature of the disorder allows a precise diagnosis, but the use of biomarkers with prognostic value is still needed to achieve clinical management of the patients in an individual manner. In addition, we need tools to evaluate the patient’s response to potential therapeutic approaches. In this review, we provide a succinct summary of the most interesting molecular biomarkers that have been assessed in patients, mostly obtained from body fluids such as cerebrospinal fluid, peripheral blood and saliva.
‘The Kids are Alert’: Generation Y Responses to Employer Use and Monitoring of Social Networking Sites

Employer monitoring of employees’ and job applicants’ social networking site (SNS) data is widespread and growing, but remains ethically, legally and efficaciously controversial. Examining this emergent source of tension in the employment relationship, this paper explores how Generation Y employees experience and perceive employer use and monitoring of SNSs, and whether employer-related concerns influenced their online behaviour. A survey of 385 employed students revealed widespread SNS engagement amongst respondents, with many experiencing some form of employer SNS use. Employer SNS use was, however, generally perceived negatively. Negativity took the form of procedural justice violations based on issues such as invasion of privacy. Nevertheless, many students displayed alertness through actively managing online profiles which, in turn, marginally yet significantly increased their justice perceptions. The study has ethical and practical implications for employer monitoring and use of SNSs, as well as contributing to our understanding of young people's online behaviour.
How field experience shapes pre-service primary teachers’ technology integration knowledge and practice

ABSTRACT The expectation for teachers to integrate technology within their classroom practice is growing. However, few studies examine the influences on pre-service teachers’ technology integration knowledge and practice, and the role of field experience in shaping these. Narratives from semi-structured pre-COVID interviews with 35 pre-service primary school teachers in one teacher education programme in Ireland, some of whom used a digital camera, were analysed using an inductive thematic approach. Opportunities for pre-service teachers to develop their knowledge and practice were shaped by their interaction with the primary school context, the people within it and the digital resources provided, the Zone of Proximal Development (ZPD). While the initial teacher education programme provided opportunities and digital resources to support the primary teachers’ learning, the Zone of Promoted Action, these were ultimately shaped by the ZPD. The results have implications for professional development programmes striving to support teachers in developing innovative practice in a post-COVID era.
Inter-Turn Fault Detection of Induction Motors Using End-Shield Leakage Fluxes

In this article, a non-invasive online technique to detect inter-turn stator winding fault of induction motors using end-shield leakage fluxes is proposed. The fault indicator is based on the fundamental component of the summation of the leakage fluxes which are spatially separated by an angle of 360°/number of the poles. Theoretical analysis using the analytical and finite element models revealed that the proposed fault indicator is independent of number of poles and location of the fault in any of the coils of the stator windings. Both theoretical analysis and experimental validation on a 10 hp induction motor showed that the proposed anomaly indicator is directly proportional to the fault severity level. A comparative study with various non-invasive, terminal measurements-based online techniques reveals that the proposed methodology is the most sensitive technique with an additional advantage of more than 92% reduction in the sensor cost for a 4-pole, 10 hp induction motor. Real-time evaluation of the proposed anomaly indicator with the prototype Motor Winding Monitoring Device (MWMD) shows that even under intermittent conditions, as small as 1-turn fault (0.5%) can be detected within 150 ms without any false alarms.
Salivary Melatonin Changes in Oncological Patients: A Systematic Review

Melatonin is known as a regulator of circadian sleep and waking rhythm. This hormone secreted by the pineal gland also has protective, oncostatic, and antioxidant properties. This systematic review was designed to answer the question “Is there a relationship between salivary melatonin changes and oncological diseases? ”. Following the inclusion and exclusion criteria, ten studies were included, according to PRISMA statement guidelines. In all included studies, the diagnostic material was unstimulated whole saliva, in which the melatonin changes were determined by different laboratory methods. Most studies concerned changes in melatonin levels in patients with brain tumours due to a direct effect on the circadian rhythm centres. Other studies focused on disorders of melatonin secretion and its inclusion as a diagnostic marker in patients with prostate cancer and oral squamous cell carcinoma. The association between melatonin changes and sleep quality and chronotype in patients with newly diagnosed lung cancer and lymphoma survivors was also investigated. In conclusion, our systematic review may suggest trends for melatonin secretion alterations in oncological patients. However, due to the significant heterogeneity of the included reports, it is not possible to clearly determine a link between changes in salivary melatonin levels and the oncological diagnosis.
Testing the psychometric quality of screening tools for the well-being of children and caregivers in alternative care-settings: a multi-informant study with families in Tanzania

ABSTRACT Given the lack of validated screening tools for the well-being of children and caregivers in family-based care in low-income settings, the present study aimed to test the psychometric quality of two newly developed tools: Scorecard A as a screener for children’s emotional and behavioral problems and Scorecard B as a screener for caregivers’ well-being and resources. The study was conducted within the framework of an ongoing caregiver training program in Dar es Salaam, Tanzania, and included a sample of 124 caregivers and 99 children (57 girls, M age = 9.30, SD = 4.65, range 3–17) divided in three equally sized age groups of preschool (3 to 5 years), primary school (6 to 12 years) and secondary school age (13 to 17 years). Caregivers reported on children’s emotional and behavioral problems using Scorecard A and the Strengths and Difficulties Questionnaire (SDQ) administered as a structured interview. A sub-sample (n = 78) of caregivers participated in another structured interview on various aspects of their well-being and caregiving resources, based on which interviewers rated Scorecard B. Additionally, caregivers completed Scorecard B and measures of their well-being and resources administered as self-report questionnaires. All assessments were conducted twice with an interval of 1 week. Scorecard A showed good internal consistency and test-retest-reliability across age groups and significant associations with the SDQ total score and subscales indicate convergent validity, particularly among preschool-aged children. The internal consistency of Scorecard B was acceptable in the questionnaire and good in the interviewer rating, while re-test-reliability in both modes and inter-rater reliability between interviewer and caregiver rating were moderate. Associations with established measures of well-being and resources provided evidence for convergent validity of Scorecard B. The Scorecards hold promise as brief screening tools to identify children and caregivers with difficulties and evaluate intervention programs in low-resource contexts.
Interpretation of Inter-Turn Fault in Transformer Winding Using Turns Ratio Test and Frequency Response Analysis

The transformer is critical equipment in electrical power systems as it is continuously exposed to various failures. Inter-turns fault in the transformer reduces the turns of the winding, thus it can be detected using a transformer-turns-ratio (TTR) test. On the other hand, the Frequency Response Analysis (FRA) method is used for assessing the mechanical condition of the transformer winding and core. In this paper, the TTR, and FRA methods are proposed to detect the inter-turns fault in the transformer winding. The winding inter-turn fault was investigated using FRA inductive inter-winding measurement and compared with the TTR test. Findings from the statistical analysis showed that FRA inductive inter-winding can detect the inter-turn fault. Finally, acceptance limits for the absolute sum of logarithmic error and standard deviation were proposed. The limits were established based on TTR’s acceptance limit from the IEEE standard. This finding proves that the FRA test can be used to complement the result in the conventional TTR test.
Automatic Detection of Cracks on Concrete Surfaces in the Presence of Shadows

Deep learning-based methods, especially convolutional neural networks, have been developed to automatically process the images of concrete surfaces for crack identification tasks. Although deep learning-based methods claim very high accuracy, they often ignore the complexity of the image collection process. Real-world images are often impacted by complex illumination conditions, shadows, the randomness of crack shapes and sizes, blemishes, and concrete spall. Published literature and available shadow databases are oriented towards images taken in laboratory conditions. In this paper, we explore the complexity of image classification for concrete crack detection in the presence of demanding illumination conditions. Challenges associated with the application of deep learning-based methods for detecting concrete cracks in the presence of shadows are elaborated on in this paper. Novel shadow augmentation techniques are developed to increase the accuracy of automatic detection of concrete cracks.
The role of excessive social media content generation, attention seeking, and individual differences on the fear of missing out: a multiple mediation model

ABSTRACT The rise of social media platforms has changed human communication once and forever and consequently, has triggered an array of emotions such as fear of missing out (FoMO). In two different studies, this paper investigated individual differences, psychological and social media motivational variables as predictors of FoMO. The paper also sought to test whether FoMO will have a negative impact on academic performance above and beyond social media engagement and social media addiction. Using a structural equation modelling (SEM) along with multiple mediation analysis, the results indicated that perceived excessive social media content generation, attention seeking, and personality variables significantly predicted FoMO controlling for individual differences and social media activity such as Instagram followers, Twitter followers, as well as WhatsApp engagement level. Furthermore, the biological sex of the individual was found to moderate the relationship between attention seeking and FoMO. Findings also indicated that FoMO had a positive impact on both social media engagement and social media addiction. Parallel mediation analysis revealed that FoMO had a negative effect on academic performance above and beyond social media addiction.
Deficits in Emotion Recognition and Theory of Mind in Parkinson’s Disease Patients With and Without Cognitive Impairments

Background Emotion recognition and social deficits have been previously reported in Parkinson’s disease (PD). However, the extent of these impairments is still unclear and social cognition is excluded from the cognitive domains considered in the current criteria for PD mild cognitive impairment (MCI). This study aims to analyze emotion recognition, affective and cognitive theory of mind in early PD patients classified according to Level II MCI criteria, and to evaluate the prevalence of socio-cognitive deficits in this sample. Methods We enrolled 45 participants with PD, classified as cognitively unimpaired (CU; n = 32) or MCI (n = 13) based on a standard neuropsychological assessment. Social cognitive skills were evaluated through validated tests for emotion recognition (i.e., Ekman 60-faces test, Ek60 Test) and mental states attribution (Story-based Empathy Task, SET) and compared to a group of 45 healthy controls (HC). Between-group differences in social tasks were performed, as well as correlation analyses to assess the relationship between social, cognitive, and clinical variables. Finally, the number of patients with social cognitive impairments in both MCI and CU subgroups was computed based on Italian normative data. Results Statistical comparison revealed significant differences among groups in the Ek60 test, with MCI obtaining significantly lower scores than HC and CU, especially for negative emotions. Significant differences were detected also in the SET, with lower performance in emotion and intention attribution for both PD groups compared to HC. A significant correlation emerged between the Ek60 test and emotion attribution. Nine patients showed poor performance at social tasks, five of them being classified as PD-CU. Discussion Parkinson’s disease cognitive profile was characterized by emotion recognition and attribution deficits. These results, as well as the detection of CU patients with isolated socio-cognitive impairments, underline the importance of assessing social cognition in PD as a possible early marker of cognitive decline.
Binary outcomes, OLS, 2SLS and IV probit

Abstract For a binary outcome Y, generated by a simple threshold crossing model with a single exogenous normally distributed explanatory variable X, the OLS estimator of the coefficient on X in a linear probability model is a consistent estimator of the average partial effect of X. Even in this very simple setting, we show that when allowing for X to be endogenously determined, the 2SLS estimator, using a normally distributed instrumental variable Z, does not identify the same causal parameter. It instead estimates the average partial effect of Z, scaled by the coefficient on Z in the linear first-stage model for X, denoted γ 1, or equivalently, it estimates the average partial effect of the population predicted value of X, These causal parameters can differ substantially as we show for the normal Probit model, which implies that care has to be taken when interpreting 2SLS estimation results in a linear probability model. Under joint normality of the error terms, IV Probit maximum likelihood estimation does identify the average partial effect of X. The two-step control function procedure of Rivers and Vuong can also estimate this causal parameter consistently, but a double averaging is needed, one over the distribution of the first-stage error V and one over the distribution of X. If instead a single averaging is performed over the joint distribution of X and V, then the same causal parameter is estimated as the one estimated by the 2SLS estimator in the linear probability model. The 2SLS estimator is a consistent estimator when the average partial effect is equal to 0, and the standard Wald test for this hypothesis has correct size under strong instrument asymptotics. We show that, in general, the standard weak instrument first-stage F-test interpretations do not apply in this setting.
Fabrication of Electrospun Juglans regia (Juglone) Loaded Poly(lactic acid) Scaffolds as a Potential Wound Dressing Material

Juglone (5-hydroxy-1,4-naphthoquinone) (J) is a naphthoquinone structured allelochemical that is mostly found in the roots, leaves, nut-hulls, bark, and wood of walnut (Juglans regia). In this study, the biocompatibility, mechanical, thermal, chemical, morphological, and antimicrobial properties of the poly(lactic acid) (PLA) (w/v)/J (10, 20, 30 mg) electrospun scaffolds were investigated. Based on the results of the study, it was shown that juglone addition increased the antimicrobial properties of the scaffolds against the Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), compared to the neat PLA film after 24 h of contact time. According to the tensile test results, the addition of J made the scaffolds more flexible but decreased the mechanical strength. The cytotoxicity properties of the J-added scaffolds demonstrated a toxic behavior on the first day of incubation. However, with an increase in the J ratio, the fibroblast cell metabolic activity increased for all incubation periods.
Applications of the remote sensing technology to detect and monitor the rust disease in the wheat – a literature review

Abstract The wheat production loss induced by rust pathogens is huge per annum globally. Those pathogens cause substantial fungal diseases of the wheat. Therefore, it is significant to be curbed on a global scale. Wheat rust early detection would improve wheat yield and its quality traits. In this paper, a review was accomplished to demonstrate the necessity for reliable health condition monitoring of the wheat at ground-based, airborne and spaceborne scales applying remotely sensed sensors and techniques. Several deep learning algorithms integrating with spectral data could promisingly detect wheat leaf rust infection at ground-based and field scales. Multispectral and hyperspectral sensors also indicated sound capabilities in the discrimination of wheat rust infection using machine learning algorithms such as SVM. Furthermore, a weather-based model comprising the combination of meteorological data with machine learning techniques represented a great potential for wheat rust early detection.
Social Isolation and Loneliness among Near-Centenarians and Centenarians: Results from the Fordham Centenarian Study

Social isolation and loneliness have been recognized as problems older people face due to their adverse effects on health and mortality, but very few researchers have analyzed their co-occurrence, which might be particularly prevalent and critical among the very old. In this study, we investigated the prevalence of combinations of social isolation and loneliness among near-centenarians and centenarians. We used data collected from 94 individuals aged 95–107 from the Fordham Centenarian Study. We built a four-group typology and explored associations with individual characteristics in various domains (demographic, socioeconomics, social, health, care, and psychological) with multinomial logistic regression models. Considering their combinations, the most prevalent groups were “isolated and lonely” and “neither isolated nor lonely” (29.8% and 28.7%, respectively). The “lonely but not isolated” (20.2%) and “isolated but not lonely” (21.3%) groups were also notably large. The likelihood of belonging to each group varied according to various individual characteristics, such as education, health, and personality. Social isolation and loneliness are distinct phenomena among centenarians. The consideration of their varied combination can help better assess life conditions at very old ages. Taking into account the differences between groups can facilitate the design of tailored interventions to improve the lives of near-centenarians and centenarians.