Miracle-dz/UniFAR-FTUs huggingface dataset:
An evaluation of supervisees’ perceptions of the benefits and limitations of pastoral/reflective supervision among Christian clergy in the UK

ABSTRACT Clergy from several Christian denominations in the UK were invited to participate in an online survey which explored their experience of pastoral/reflective supervision. The research question was, “How has the practice of pastoral supervision within Christian denominations in the UK been beneficial, and what have been its limitations? ” The aims of the research were: to explore what has been helpful to supervisees in the provision of pastoral supervision; and to determine what has not been helpful to supervisees in the provision of pastoral supervision. 173 clergy respondents took part. The data show that pastoral/reflective supervision is mostly beneficial to respondents, and that pastoral/reflective supervision enables clergy to self-perceive feelings of: support; lessening isolation; increased insight into personal process; affirmation of their ministry; and to have a better sense of wellbeing. Some hindrances to effective supervision are identified, and some recommendations for training and denominational support are made.
Unit-root Tests Based on Forward and Reverse Dickey–Fuller Regressions

In this article, we present the command adfmaxur, which computes the Leybourne (1995, Oxford Bulletin of Economics and Statistics 57: 559–571) unit-root statistic for different numbers of observations and the number of lags of the dependent variable in the test regressions. The latter can be either specified by the user or endogenously determined. We illustrate the use of adfmaxur with an empirical example.
Understanding health misinformation sharing among the middle-aged or above in China: roles of social media health information seeking, misperceptions and information processing predispositions

PurposeThe ballooning health misinformation on social media raises grave concerns. Drawing upon the S-O-R (Stimulus-Organism-Response) model and the information processing literature, this study aims to explore (1) how social media health information seeking (S) affects health misinformation sharing intention (R) through the channel of health misperceptions (O) and (2) whether the mediation process would be contingent upon different information processing predispositions.Design/methodology/approachData were collected from a survey comprising 388 respondents from the Chinese middle-aged or above group, one of China's most susceptible populations to health misinformation. Standard multiple linear regression models and the PROCESS Macro were adopted to examine the direct effect and the moderated mediation model.FindingsResults bolstered the S-O-R-based mechanism, in which health misperceptions mediated social media health information seeking's effect on health misinformation sharing intention. As an indicator of analytical information processing, need for cognition (NFC) failed to moderate the mediation process. Contrarily, faith in intuition (FI), an indicator reflecting intuitive information processing, served as a significant moderator. The positive association between social media health information seeking and misperceptions was stronger among respondents with low FI.Originality/valueThis study sheds light on health misinformation sharing research by bridging health information seeking, information internalization and information sharing. Moreover, the authors extended the S-O-R model by integrating information processing predispositions, which differs this study from previous literature and advances the extant understanding of how information processing styles work in the face of online health misinformation. The particular age group and the Chinese context further inform context-specific implications regarding online health misinformation regulation.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0157.
An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery

Efficient detection and evaluation of soybean seedling emergence is an important measure for making field management decisions. However, there are many indicators related to emergence, and using multiple models to detect them separately makes data processing too slow to aid timely field management. In this study, we aimed to integrate several deep learning and image processing methods to build a model to evaluate multiple soybean seedling emergence information. An unmanned aerial vehicle (UAV) was used to acquire soybean seedling RGB images at emergence (VE), cotyledon (VC), and first node (V1) stages. The number of soybean seedlings that emerged was obtained by the seedling emergence detection module, and image datasets were constructed using the seedling automatic cutting module. The improved AlexNet was used as the backbone network of the growth stage discrimination module. The above modules were combined to calculate the emergence proportion in each stage and determine soybean seedlings emergence uniformity. The results show that the seedling emergence detection module was able to identify the number of soybean seedlings with an average accuracy of 99.92%, a R2 of 0.9784, a RMSE of 6.07, and a MAE of 5.60. The improved AlexNet was more lightweight, training time was reduced, the average accuracy was 99.07%, and the average loss was 0.0355. The model was validated in the field, and the error between predicted and real emergence proportions was up to 0.0775 and down to 0.0060. It provides an effective ensemble learning model for the detection and evaluation of soybean seedling emergence, which can provide a theoretical basis for making decisions on soybean field management and precision operations and has the potential to evaluate other crops emergence information.
Estimation for flexible Weibull extension under progressive Type-II censoring

In this paper, we discussed classical and Bayes estimation procedures for estimating the unknown parameters as well as the reliability and hazard functions of the flexible Weibull distribution when observed data are collected under progressively Type-II censoring scheme. The performances of the maximum likelihood and Bayes estimators are compared in terms of their mean squared errors through the simulation study. For the computation of Bayes estimates, we proposed the use of Lindley’s approximation and Markov Chain Monte Carlo (MCMC) techniques since the posteriors of the parameters are not analytically tractable. Further, we also derived the one and two sample posterior predictive densities of future samples and obtained the predictive bounds for future observations using MCMC techniques. To illustrate the discussed procedures, a set of real data is analysed.
Structures, Electronic, and Magnetic Properties of CoKn (n = 2–12) Clusters: A Particle Swarm Optimization Prediction Jointed with First-Principles Investigation

Transition-metal-doped clusters have long been attracting great attention due to their unique geometries and interesting physical and/or chemical properties. In this paper, the geometries of the lowest- and lower-energy CoKn (n = 2–12) clusters have been screened out using particle swarm optimization and first principles relaxation. The results show that except for CoK2 the other CoKn (n = 3–12) clusters are all three-dimensional structures, and CoK7 is the transition structure from which the lowest energy structures are cobalt atom-centered cage-like structures. The stability, the electronic structures, and the magnetic properties of CoKn clusters (n = 2–12) clusters are further investigated using the first principles method. The results show that the medium-sized clusters whose geometries are cage-like structures are more stable than smaller-sized clusters. The electronic configuration of CoKn clusters could be described as 1S1P1D according to the spherical jellium model. The main components of petal-shaped D molecular orbitals are Co-d and K-s states or Co-d and Co-s states, and the main components of sphere-like S molecular orbitals or spindle-like P molecular orbitals are K-s states or Co-s states. Co atoms give the main contribution to the total magnetic moments, and K atoms can either enhance or attenuate the total magnetic moments. CoKn (n = 5–8) clusters have relatively large magnetic moments, which has a relation to the strong Co-K bond and the large amount of charge transfer. CoK4 could be a magnetic superatom with a large magnetic moment of 5 μB.
Phase accuracy evaluation for phase-shifting fringe projection profilometry based on uniform-phase coded image

Phase-shifting fringe projection profilometry (PSFPP) is a three-dimensional (3D) measurement technique widely adopted in industry measurement. It recovers the 3D profile of measured objects with the aid of the fringe phase. The phase accuracy is among the dominant factors that determine the 3D measurement accuracy. Evaluation of the phase accuracy helps refine adjustable measurement parameters, contributes to evaluating the 3D measurement accuracy, and facilitates improvement of the measurement accuracy. Although PSFPP has been deeply researched, an effective, easy-to-use phase accuracy evaluation method remains to be explored. In this paper, methods based on the uniform-phase coded image (UCI) are presented to accomplish phase accuracy evaluation for PSFPP. These methods work on the principle that the phase value of a UCI can be manually set to be any value, and once the phase value of a UCI pixel is the same as that of a pixel of a corresponding sinusoidal fringe pattern, their phase accuracy values are approximate. The proposed methods provide feasible approaches to evaluating the phase accuracy for PSFPP. Furthermore, they can be used to experimentally research the property of the random and gamma phase errors in PSFPP without the aid of a mathematical model to express random phase error or a large-step phase-shifting algorithm. In this paper, some novel and interesting phenomena are experimentally uncovered with the aid of the proposed methods.
Immunomodulatory Effects of Endoscopic Ultrasound-Guided Thermal Ablation in Patients with Pancreatic Ductal Adenocarcinoma

Simple Summary Thermal ablation under endoscopic ultrasound (EUS)-guidance has been investigated in pancreatic ductal adenocarcinoma (PDAC) based on its potential to boost local and systemic anti-tumor immune response. In a recent phase II randomized controlled trial, ablation of borderline resectable (BR) and locally advanced (LA) PDAC using the HybridTherm Probe (HTP) under EUS-guidance in combination with chemotherapy was shown to ameliorate disease progression at 6 months compared to chemotherapy alone. In this work, we aimed to explore the effects of EUS-ablation with HTP on the systemic immune response in patients with BR and LA PDAC. In contrast to chemotherapy, EUS-HTP selectively affected immunological predictors of poor outcome such as serum levels of APRIL/TNFSF13 and inflammatory monocytes, reinforcing its potential use in selected PDAC patients. Abstract Immunological consequences of endoscopic ultrasound (EUS)-local thermal ablation (LTA) for pancreatic ductal adenocarcinoma (PDAC) have not been extensively assessed. We aimed to explore EUS-LTA effects on the systemic immune response in PDAC. Peripheral blood was collected from 10 treatment-naïve patients with borderline resectable and locally advanced PDAC, randomly allocated to Nab-paclitaxel plus Gemcitabine chemotherapy (CT-arm, n = 5) or EUS-LTA with HybridTherm Probe plus CT (HTP + CT-arm, n = 5). Twenty healthy donors were included as controls. Flow-cytometry and multiplex assays were used to profile immune cell subsets and measure serum cytokines/chemokines, respectively. At baseline, PDAC patients showed increased circulating monocytes and lower circulating lymphocytes and CD19+ B cells counts compared to healthy controls. After 4 months, CT induced decrease of B regulatory cells, CD4+ cytotoxic T cells and IL-1β. The addition of EUS-HTP to CT selectively decreased the serum levels of APRIL/TNFSF13 as well as T regulatory cells, total, classic and inflammatory monocytes. Serum levels of APRIL/TNFSF13 and total, classic and inflammatory monocytes counts at baseline were associated with worse overall survival. EUS-HTP has the potential to selectively impact on immune cells and cytokines associated with poor outcomes in PDAC.
The transcriptionally active bacterial communities of grapevine rhizosphere in dependence on rootstock and scion variety

The rhizosphere is where crucial processes for the productivity of viticultural systems occur. The composition of the bacterial communities associated with the rhizosphere of grapevines is known to depend on plant genotype. However, the genotype of grafted grapevines differs between scion and rootstock; the role of each genotype is unclear. To untangle the effect of scion and rootstock, the rRNA (V4–V5 region of 16S rRNA) extracted from the rhizosphere of the grape varieties Riesling and Mueller-Thurgau ungrafted vs grafted on different rootstocks was sequenced. The bioinformatic analysis with tools designed to be robust for compositional data showed that the investigated rootstocks or scions or combinations, respectively, recruited bacterial communities with distinguishable traits. Statistical differences were revealed between ungrafted Riesling vs Mueller-Thurgau, between grafted Riesling vs ungrafted Riesling, and between ungrafted Mueller-Thurgau vs grafted Mueller-Thurgau. Thus, confirming the role of scion and rootstock genotype as a driver of the structure and composition of bacterial communities in the rhizosphere of grapevines.
Semantic Segmentation of China's Coastal Wetlands Based on Sentinel-2 and Segformer

Concerning the ever-changing wetland environment, the efficient extraction of wetland information holds great significance for the research and management of wetland ecosystems. China’s vast coastal wetlands possess rich and diverse geographical features. This study employs the SegFormer model and Sentinel-2 data to conduct a wetland classification study for coastal wetlands in Yancheng, Jiangsu, China. After preprocessing the Sentinel data, nine classification objects (construction land, Spartina alterniflora (S. alterniflora), Suaeda salsa (S. salsa), Phragmites australis (P. australis), farmland, river system, aquaculture and tidal falt) were identified based on the previous literature and remote sensing images. Moreover, mAcc, mIoU, aAcc, Precision, Recall and F-1 score were chosen as evaluation indicators. This study explores the potential and effectiveness of multiple methods, including data image processing, machine learning and deep learning. The results indicate that SegFormer is the best model for wetland classification, efficiently and accurately extracting small-scale features. With mIoU (0.81), mAcc (0.87), aAcc (0.94), mPrecision (0.901), mRecall (0.876) and mFscore (0.887) higher than other models. In the face of unbalanced wetland categories, combining CrossEntropyLoss and FocalLoss in the loss function can improve several indicators of difficult cases to be segmented, enhancing the classification accuracy and generalization ability of the model. Finally, the category scale pie chart of Yancheng Binhai wetlands was plotted. In conclusion, this study achieves an effective segmentation of Yancheng coastal wetlands based on the semantic segmentation method of deep learning, providing technical support and reference value for subsequent research on wetland values.
Investigation of electron irradiation-induced magnetism in layered MoS2 single crystals

By using higher acceleration energies than the displacement energy of Mo atoms, the electron irradiation on the layered MoS2 single crystals is found to be an effective and simple method to induce the diamagnetic to ferromagnetic phase transition persisting up to room temperature. The easy axis can be controllable by regulating the electron dose and the acceleration energy. The ferromagnetic states are largely attributed to the strain around the vacancies.
Application of The Role Playing Method in Learning Drama Texts to Improve Writing Skills for Class VIII Junior High School Students

The ability to write drama texts at the junior high school level is still relatively low. This study aims to improve the ability to write drama texts by using the role playing method. The research subjects were 35 students of class VIII at SMPN 2 Bandung in the academic year 2020/2021.This research was conducted using a qualitative descriptive research method. In collecting data, the researcher used test, observation and interview instruments. Before and after the learning treatment, students were given a pretest and posttest. Based on the results of the research conducted, the scores for writing drama texts were obtained with an average pretest of 55.4 and posttest of 81.7. The results of the SPSS analysis showed a significance of 0.000<0.05 so that there was a difference between the learning outcomes before and after the treatment. The results obtained indicate that the application of the role playing method in learning to write drama texts can be increased by a difference of 26.3 in grade VIII junior high school students.
Time-zoomable FRET spectroscopy with a 512 x16 SPAD line sensor

We demonstrate a new 512x16 single photon avalanche diode (SPAD) based line sensor with per-pixel TCSPC histogramming for time-resolved, time-zoomable, FRET spectroscopy. The line sensor can operate in single photon counting (SPC) mode as well as time-correlated single photon counting (TCSPC) and per-pixel histogramming modes. TCSPC has been the preferred method for fluorescence lifetime measurements due to its collection of full decays as a histogram of arrival times. However, TCSPC is slow due to only capturing one photon per exposure and large timestamp data transfer requirements for offline histogramming. On-chip histogramming improves the data rate by allowing multiple SPAD pulses (up to one pulse per laser period) to be processed in each exposure cycle, along with secondly reducing the I/O bottleneck as only the final histogram is transferred. This can enable 50x higher acquisition rates (up to 10 billion counts per second), along with time-zoomable histogramming operation from 1.6ns to 205ns with 50ps resolution. A broad spectral range can be interrogated with the sensor (450-900nm). Overall, these sensors provide a unique combination of light sensing capabilities for use in high speed, sensitive, optical instrumentation in the time/wavelength domain. We test the sensor performance by observation of fluorescence resonance energy transfer (FRET) between FAM and TAMRA and between EGFP and RFP FRET standards.
Anti-fatigue effect of tormentic acid through alleviating oxidative stress and energy metabolism-modulating property in C2C12 cells and animal models

BACKGROUND/OBJECTIVES Oxidative stress is caused by reactive oxygen species and free radicals that accelerate inflammatory responses and exacerbate fatigue. Tormentic acid (TA) has antioxidant and anti-inflammatory properties. Thus, the aim of present study is to determine the fatigue-regulatory effects of TA in H2O2-stimulated myoblast cell line, C2C12 cells and treadmill stress test (TST) and forced swimming test (FST) animal models. MATERIALS/METHODS In the in vitro study, C2C12 cells were pretreated with TA before stimulation with H2O2. Then, malondialdehyde (MDA), lactate dehydrogenase (LDH), creatine kinase (CK) activity, tumor necrosis factor (TNF)-α, interleukin (IL)-6, superoxide dismutase (SOD), catalase (CAT), glycogen, and cell viability were analyzed. In the in vivo study, the ICR male mice were administered TA or distilled water orally daily for 28 days. FST and TST were then performed on the last day. In addition, biochemical analysis of the serum, muscle, and liver was performed. RESULTS TA dose-dependently alleviated the levels of MDA, LDH, CK activity, TNF-α, and IL-6 in H2O2-stimulated C2C12 cells without affecting the cytotoxicity. TA increased the SOD and CAT activities and the glycogen levels in H2O2-stimulated C2C12 cells. In TST and FST animal models, TA decreased the FST immobility time significantly while increasing the TST exhaustion time without weight fluctuations. The in vivo studies showed that the levels of SOD, CAT, citrate synthase, glycogen, and free fatty acid were increased by TA administration, whereas TA significantly reduced the levels of glucose, MDA, LDH, lactate, CK, inflammatory cytokines, alanine transaminase, aspartate transaminase, blood urea nitrogen, and cortisol compared to the control group. CONCLUSIONS TA improves fatigue by modulating oxidative stress and energy metabolism in C2C12 cells and animal models. Therefore, we suggest that TA can be a powerful substance in healthy functional foods and therapeutics to improve fatigue.
The denoising semi-coupled dictionary learning for retina image super-resolution

Retina images are mainly obtained by Spectral Domain-Optical Coherence Tomography (SD-OCT), however, most of the acquired volume data are low-resolution(LR) images with noise, making it hard to quantify diseased tissue based on low quality retinal images. In this paper, we propose a denoising Semi-Coupled Dictionary Learning(SCDL) model to reconstruct the noise image while guaranteeing certain noise robustness. First, we use non-local similarities of retina images to construct constraint term, which is added to the objective function of the proposed model. Then, in order to guarantee the fidelity of reconstructed image, the initialized interpolation section should be replaced by the corresponding LR image after SR reconstruction. However, the noise in LR image will affects the reconstructed image quality. So we perform bilateral filtering on the LR image before replacement. Last, two sets of experiments on retinal noise images validate that our proposed method outperforms other state-of-the-art methods.
DOA Estimation Based on Coherent Integration-Sparse Bayesian Learning With Time-Variant Gain-Phase Errors

Direction of arrival (DOA) estimation is an important topic of array signal processing and channels without gain-phase errors or with time-invariant gain-phase errors are assumed in most of the DOA estimation algorithms. However, with the measured data of a reconnaissance receiver system, we verify that the gain-phase error between channels is time-variation. Based on this fact, a more practical signal model with time-variant gain-phase errors is constructed. To obtain accurate DOA estimation, an algorithm termed as coherent integration-sparse Bayesian learning with time-variant gain-phase errors is (CI-SBLVGPE) devised which has two superiorities, i.e., 1) the CI operation integrates the signal energy into a single snapshot, which decreases the gain-phase errors, makes the compensation possible, and guarantees a low computational complexity; 2) the priori information of the gain-phase errors, source DOA, and noise is used to reduce the computational complexity and improve the robustness. With mathematical analyses, numerical simulations, and real data processing, we validate the constructed model and superiorities of the proposed method.