bluuebunny/crossref_metadata_2025_split huggingface dataset:
Editorial
Authors: Dai David Wei , Grieve Averil , Yahalom Sharon 
Published: December 2022

Interactional competence (IC) is a concept gaining growing currency in language learning, teaching and assessment. First proposed by Kramsch (1986), IC focuses on developing second language (L2) speakers’ abilities to use language for functional purposes, ranging from “survival as a tourist or a student to negotiating treaties” (p. 366). The conceptualization of language competence as an ability for use differentiates IC from traditional understanding of proficiency, which consists of componential, de-contextualized ability indicators such as lexical range, grammatical soundness, and pronunciation. The ethos of IC emphasizes that language teaching education needs to focus on cultivating L2 speakers’ abilities to use their linguistic resources (e.g., vocabulary and grammar) to achieve meaningful social actions in real-world interactional contexts. This special issue comprises three research articles and three book reviews, each of which exemplifies the ways in which IC can be applied to face-to-face and online learning opportunities for L2 speakers.
Development of India Meteorological Department - High Resolution Rapid Refresh (IMD-HRRR) Modeling System for Very Short Range Weather Forecasting
Authors: Srivast Akhil , Kumar Prashant , Panda Sambit Kumar , Das Ananda Kr. , Pattanaik D. R. 
Published: December 2022

Abstract

The aim of this study is the operational dynamical nowcasting application over different parts of India from Indian Meteorological Department (IMD) using Doppler Weather Radar (DWR) network of multiple radars and numerical weather prediction (NWP) model. The High Resolution Rapid Refresh (HRRR) approach has been adopted to achieve this objective in which the DWR data are hourly assimilated at convective-scale in the Weather Research and Forecasting (WRF) model. The designated NWP setup implemented for very short-range to nowcasting of weather is defined as the IMD-HRRR modelling system. Various quality controls are employed before assimilating DWR data in the IMD-HRRR system. Three different domains are specified over India that cover the entire Indian landmass, and next 12-hours predictions are provided from hourly cyclic assimilation experiments. The results of all domains suggested that the IMD-HRRR predictions are not degraded with forecast lengths (upto 12 hours) when compared against observations e.g. Synop, Metar, Buoy, total precipitable water (TPW) from GPS stations. Minimum errors are achieved when model predictions are compared against Buoy and TPW observations. The correlation values are higher than 0.9 for all domains. Furthermore, the IMD-HRRR model forecasts are also compared with observed DWR radial winds to demonstrate auxiliary applications of the DWR data for model verifications at high spatio-temporal resolution.
Adaptive Dimensional Search Based Orthogonal Experimentation SSA (ADOX-SSA) for training RBF Neural Network and optimal Feature Selection
Authors: Mahapatra Ajit Kumar , Panda Nibedan , Pattanayak Binod Kumar 
Published: December 2022

Abstract
Salp Swarm Algorithm (SSA) is a new stochastic approach for solving optimization issues based on the ideas of swarm intelligence. The ease of implementation and lower number of parameters to fine-tune are responsible for SSA's success and universal acceptance among researchers. The typical SSA method suffers from local optima entrapment and a poor convergence rate while dealing with more complex situations because of a lack of population density and inadequate local exploitation. To overcome such challenges, this study suggests a hybrid kind of SSA called Adaptive-Dimensional-Search based Orthogonal Experimentation SSA (ADOX-SSA). Furthermore, the inclusion of an ADOX operator increases population diversity, intensifies local exploitation, and strengthens the SSA standards. Consequently, the balance between the processes of exploration and exploitation is attuned, resulting in a faster rate of convergence than the normal SSA. To validate the ADOX-SSA technique's competency, 14 basic functions and 30 advanced standard functions were chosen following IEEE-CEC-2014. The findings of the proposed strategy have also been compared to those of recent metaheuristic approaches. Two nonparametric tests were employed to demonstrate statistical significance as Friedman and Holms approach. Additionally, the suggested ADOX-SSA approach is castoff for training Radial Basis Function Neural Network (RBFNN) by selecting datasets from the UCI depository. Finally, the same suggested approach is utilized to select the best features from benchmark datasets while maintaining accuracy and minimizing neural network complexity.
Green Innovation, Globalization, Financial development and CO2 Emissions: The role of Governance as a moderator in South Asian Countries
Authors: Naz Ayesha , Aslam Misbah 
Published: December 2022

Abstract
The current study is designed to analyze the relationship between, environmental innovations, globalization, financial development, and CO2 emissions in the South Asian region over the period of 1996 to 2019. In this regard, the role of governance is also incorporated as a moderator along with Environmental Kuznets Curve (EKC) hypothesis. The sample size includes Bangladesh, India, Pakistan, Nepal, and Sri Lanka. The results of robust least square show the validity of EKC in the sample countries. Environmental innovations show desirable results on CO2 emissions, while globalization, financial development, and governance are increasing environmental degradation. The role of governance as a moderator is only effective and favorable with environmental innovation. However, in the case of globalization and financial development, governance is appeared to be ineffective in lessening the rate of emissions rather it contributes to emissions. It is clearly showing the missing link in formulating coherent policy to achieve sustainability targets. Therefore, it is desirable to improve the role of governance with respect to environmental policies not only to handle directly environmental issues but also indirectly while promoting the process of globalization and financial development.
Comparative Effects of Curcumin versus Nano-Curcumin on histological, immunohistochemical expression, histomorphometric, and biochemical changes to pancreatic beta-cells and lipid profile of Streptozocin induced diabetes
Authors: Metawea mohamed r. , Abdelrazek Heba M A , El-Hak Heba Nageh Gad , Moghazee Mona m. , Marie Ohoud 
Published: December 2022

Abstract
Diabetes mellitus is a worldwide problem characterized by hyperglycemia as well as the damage of the microscopic structure of the beta-cells of Langerhans pancreatic islets. In the present study, the histological, immunohistochemical, morphometric, and biochemical alterations to pancreatic beta-cells in streptozocin (STZ) induced diabetes were assessed in rats treated with Curcumin (CU) or Nano-Curcumin (nCU). Twenty-four adults male Wistar albino rats were distributed into four groups: the nondiabetic control group, the diabetic untreated group, and two diabetic groups treated with CU or nCU, respectively. Blood glucose, serum insulin levels and lipid profile were measured. The pancreatic tissues were collected and processed into paraffin sections for histological and immunohistochemical examination, oxidative stress markers and real time PCR expression for pancreatic and duodenal homeobox 1 (PDX1). The insulin expression in beta-cells was assessed using immunohistochemistry. Morphometrically, the percentage area of anti-insulin antibody reaction and the percentage area of islets cells were determined. STZ-induced deteriorating alteration in beta-cells led to declines in the number of functioning beta-cells and insulin immunoreactivity. In STZ-treated rats, CU and nCU significantly reduced blood glucose concentration while increasing blood insulin level. It also caused a significant increase in the number of immunoreactive beta-cells to the insulin expression and significant reduction of the immunoreactive beta-cells to the caspase-3 expression. In conclusion, CU and nCU could have a therapeutic role in the biochemical and microscopic changes in pancreatic beta-cells in diabetes induced rats through STZ administration with more bio-efficacy of nCUR.
Assessing the effectiveness and implementation of a universal classroom-based set of educator practices to improve preschool children's outcomes: Protocol for a cluster randomized controlled type 2 hybrid trial in Singapore
Authors: Tan Evelyn Siyun , McLeod Bryce D. , Mildon Robyn A. , Shlonsky Aron , Seah Cheryl K.F. , McCrickerd Keri , Goh Esther , Kembhavi Gayatri 
Published: December 2022

Abstract
Background
Providing high-quality early childhood care and education is widely understood as key to maximizing children's potential to succeed later in life, as it equips young children with the essential skills and competencies needed for their development. Despite the government's efforts to support the early childhood sector, educators in Singapore continue to report difficulties in implementing practices in classrooms that promote children's social, emotional, and cognitive development. To enhance educators' skills in these domains, we developed the Enhancing and Supporting Early development to better children's Lives (EASEL) Approach, a set of universal, educator-delivered practices for use with 3-6-year-old children to improve social, emotional, behavioral, and executive functioning (SEB+EF) outcomes.
Methods
This study will evaluate the implementation and effectiveness of the EASEL Approach in improving early childhood educators' teaching practices and, in turn, children's SEB+EF outcomes. We will conduct a cluster randomized controlled trial that utilizes a type 2 hybrid implementation-effectiveness design in 12 childcare centers. We will use the EPIS (Explore, Prepare, Implement, Sustain) Framework to support the implementation of the EASEL Approach. Implementation strategies include training, educator self-assessments, practice-based coaching, and data monitoring. Primary outcomes include educators' teaching practices and their adoption of the EASEL Approach in everyday practice. Secondary outcomes include the acceptability and feasibility of the EASEL Approach and children's SEB+EF outcomes. Quantitative and qualitative data will be collected at baseline, six months, and after implementation.
Conclusion
Findings from this study will provide important evidence on the implementation of the EASEL Approach in early childhood classrooms in Singapore and the effectiveness of the EASEL Approach in improving educators' teaching practices and children's SEB+EF outcomes.
Trial registration
This study was prospectively registered on ClinicalTrials.gov, Identifier: NCT05445947 on 6th July 2022.
Interference coordination algorithm based on disturbance code
Authors: chen xiaoqing , feng ziliang 
Published: December 2022

Abstract
With the development of wireless network and the improvement of spectrum space reuse, the same-frequency interference becomes the bottleneck restricting the performance of wireless communication system, which will inevitably reduce the network performance and user service quality. Same-frequency network is an unavoidable problem in wireless communication. How to more effectively restrain or coordinate the interference of the same-frequency critical interval has become a long-term problem. Although the traditional SFR successfully coordinates the interference between the neighbors, it also fragments the use of wireless resources, increases the complexity of resource scheduling, and affects the throughput of the system. Therefore, this paper proposes a purpose of focusing on the edge users of adjacent cells; to ensure that the disturbances of the edge users of adjacent cells are orthogonal to each other and do not affect each other to resolve their own signals. In order to reduce the interference of edge users to the adjacent communication, improve the throughput and wireless resource utilization of the entire wireless communication system.
UV 20W-class single-mode nanosecond pulse delivery using a vacuum-free/ambient air inhibited-coupling hollow-core fiber
Authors: LEROI FLORIAN , GÉRÔME FRÉDÉRIC , DIDIERJEAN JULIEN , SABY JULIEN , BENABID FETAH , BOULLET JOHAN 
Published: December 2022

Abstract

Silica fibers have been successful used to deliver high-power high-energy laser beam in the near-infrared and visible range but suffer from high absorption and color formation in the UV spectrum which disqualify then for such wavelength. Recently, the advances of inhibited-coupling hollow-core photonic crystal fibers made them as an unique tool to transport UV radiation thanks to an ultralow overlap with the silica cladding and record losses down to 10 dB/km. By further optimizing such fiber, we report in this letter a record single-mode delivery of 23.3 W (155 μJ) with 92% transmission from a 343 nm, 10 ns, 150 kHz laser source, corresponding to an improvement of the current state-of-the-art by two orders of magnitude. © 2022 OpticaPublishing Group
What is Appropriate? On the Assessment of Human-Robot Proxemics for Casual Encounters in Closed Environments
Authors: Neef Nicolas Eric , Zabel Sarah , Lauckner Mathis , Otto Siegmar 
Published: December 2022

Abstract

Autonomous robots of different kinds become more and more salient in our everyday life and because the spatial proximity of robots has an effect on the well-being of humans, researchers try to find and give appropriate guidelines regarding spatial proximity. In doing so, these studies rely on a few different concepts (e.g., safety or comfort) and on a rather large variety of different measurement approaches thereof. From psychology we know that the conceptual framing, as well as the way we measure (number of items, labels of scales, and wording) can affect the outcome of such studies. We present the first study that addresses and evaluates the consequences of these different conceptual and methodological approaches. We found that there is significant interaction between the different measurement approaches and the determined appropriate distance. This interaction was significant for both lateral passing distances and frontal approaches. This can have real-life effects, since the appropriate distances determined in studies such as the present may directly influence actual programming algorithms. Furthermore, by using three different types of robots, we were able to show that the humanization of mechanoids can lead to the acceptance of smaller distances by humans.
A prediction and fuzzy-based approach for VM migration for optimal trade off energy-performance in cloud network
Authors: Zolfaghari Rahmat 
Published: December 2022

Abstract
Energy efficiency, low utilization of Physical Machines (PM) and service access level are challenges of cloud networks, while efficient migration of Virtual Machines (VMs) is a solution for them. Uncontrolled migration of the VMs can causes migration overhead, that most important of them comprising: violation in Service Level Agreement (SLA) and high energy consumption. Generally, performance requirements in the form of SLAs are formalized. The main goal of the dynamic migration of VMs is a trade-off in energy and performance. Detecting when cloud's PMs is being overloaded or under-loaded are two substantial sub-problems of dynamic VM migration, which directly effects the performance and energy efficiency. Thus, migration technique is a practical approach for balancing the electricity consumption and Quality of Service (QoS) requirements in the cloud networks. In this paper, with definition an architectural framework in the system modelling, in first to detect overloaded and under-loaded PMs a Predict Detection method (PD method), and then for selection of VMs on overloaded PMs a Fuzzy Selection method (FS method), in overall, algorithm (PDFS) is proposed. The algorithm PDFS simulation results demonstrate that the proposed algorithm significantly outperforms benchmark algorithms in terms of energy, migration overhead, service access and SLA violation.
F10-AS1/hsa-miR-146b-5p/RAD54L axis regulates the progression of gastric cancer by mediating homologous recombination pathway
Authors: Zhou Ying , Geng Yan , Guo Xianling 
Published: December 2022

Abstract
Radiation resistance and the etiology of gastric cancer (GC) is significantly influenced by the DNA damage response, particularly DNA double-strand breaks (DSBs). A repair method for dealing with DNA damage is homologous recombination repair (HRR). The stability of the genome may be directly harmed by the homologous recombination deficiency (HRD) or blockage of the HRR pathway, inducing a slight increase in chromosome aberration and eventually leading to malignant tumors, including gastric adenocarcinoma. Therefore, to clarify the mechanism of DNA damage repair and create GC therapeutic targets, it is crucial to investigate the feedback mechanism of HRR to DSB in GC. Through bioinformatics analysis, the differentially expressed genes related to GC were found, the core genes were screened by the WGCNA method, the ceRNA network F10-AS1/hsa-miR-146b-5p / RAD54L was predicted and constructed by mirDIP and LncBase database, and the inhibitory effect of knockdown F10-AS1 on RAD54L was verified by in vivo and in vitro experiments. Results exhibited that knockdown of F10-AS1 raised the inhibitory effect of F10-AS1 on hsa-miR-146b-5p, promoted the combination of miR-146b-5p and RAD54L, downregulated the expression of RAD54L, and induced GAC cell death in vitro and slowed tumor growth in vivo. In the HR route of DNA repair, the F10-AS1/hsa-miR-146b-5p/RAD54 axis is crucial. Knockdown of F10-AS1 means that it inhibits homologous recombination in cancer cells and the maintenance efficiency of the DNA repair mechanism, induces the accumulation of spontaneous DSB, blocks DNA repair in cancer cells, achieves the best effect of anticancer drugs, and inhibits tumor development.
Two Improving Approaches for Faulty Interaction Localization using Logistic Regression Analysis
Authors: Nishiura Kinari , Choi Eun-Hye , Choi Eunjong , Mizuno Osamu 
Published: December 2022

Abstract

Faulty Interaction Localization (FIL) is a process to identify which combination of input parameter values induced test failures in combinatorial testing. An accurate and fast FIL provides helpful information to fix defects causing the test failure. One type of conventional FIL approach, which analyzes test results of whole test cases and estimates the suspiciousness of each combination, has two main concerns; (1) the accuracy is not enough, (2) the huge time cost is sometimes needed. In this paper, we propose two novel approaches to improve those concerns. FROGa attempts to estimate suspiciousness more accurately using logistic regression analysis. FROGb attempts to estimate failure-inducing combinations at high speed by estimating the subsets of them using logistic regression analysis and exploring just their supersets. Through evaluation experiments using a large number of artificial test results based on several real software systems, we observed that FROGa has very high accuracy, and FROGb can drastically reduce time cost for targets that have been difficult to complete by the conventional method.
Bio-medical Image Restoration Using Hybrid Hyperspectral Images Deep Learning With MMA
Authors: Latchoumi T. P. , Senthilselvi A. , chelliah Balika J 
Published: December 2022

Abstract
Photothermal Therapy (PT) is an efficient scanning technique in biological research. However, rapid PT scanning and noise attenuation constitute a critical challenge in a clinical trial. To solve this problem, techniques based on Compression Sensors (CS) have recently been developed that use lower PT calculation costs and excellent accuracy. The majority of image results of prominence techniques depends on rare and inconsistent circumstances. It also takes considerable time to ensure that the photothermal information gathered empirically meets CS requirements. To address these constraints, a Hyperspectral Images Deep Learning (HSIDL-PT) and Maximize-Minimize Algorithm (MMA) method proposed in this paper to efficiently reduce under-represented artifacts in Neural Network (NN). The proposed method is to retrieve high photothermal images which used 5% of the total measuring information. Furthermore, the quantitative evaluation shows that the s HSIDL-PT technique improves performance by 30 percent when compared to the existingZ technique. with mean Structure Similarity Index (SSIM) = 0.974, Peak Signal-to-Noise Ratio (PSNR) = 29.88 dB and Standard Deviation of ± 0.007. Researchers propose MMA with Deep Learning, which would be the outcome of employing the limit for non-convex optimization, ethical and responsible that used the unifying MMA viewpoint; studies demonstrate that the proposed method outperforms the only one depending on linear maximizing.
The effect of motor interference therapy on traumatic memories: A randomized, double blind, controlled study
Authors: Lorena Reyes-Santos , Alonso Morales-Rivero , Erik Bisanz , Jeffery Bisanz , Tim Phizackerley , Daniel Crail-Melendez 
Published: December 2022

Abstract
Introduction: Traumatic memories are a core symptom of PTSD and stress-related disorders, as well as a transdiagnostic symptom found in many different mental disorders. There are effective psychological treatments for PTSD symptoms, but access to these specialized treatments can be difficult and expensive. One potential for treatment is the use of visuospatial tasks to interrupt memory reconsolidation processes. The aim of this pilot study was to determine the usefulness of Motor Interference Therapy (MIT), which consists of a visuospatial task verbally directed through an audio, for the treatment of traumatic memories.
Methods: We conducted a randomized, double blind, controlled study. 28 participants with at least one traumatic memory causing distress were randomized to receive either MIT or an abbreviated version of Jacobson´s Progressive Muscle Relaxation Technique (PMR). Both interventions were administered twice for a total duration of 30 minutes. The assessment scales (PTSD Symptom Severity Scale-Revised, visual-analog scale (EQ-VAS) from EuroQol 5D, and a visual analogue scale of traumatic memory distress) were administered by a blinded researcher to the treatment group in three times: before the intervention, one week after the intervention and one month later. Only the visual-analog scale that rated the level of stress provoked by the traumatic memory was also applied immediately after the intervention. For each dependent variable a Group (PMR, MIT) x Session analysis of variance was conducted with repeated measures on the second variable. Critical Group x Session interactions were analyzed further with pairwise comparisons. Analyses of covariance were conducted to evaluate posttest scores adjusted for any pretest differences.
Results: Mean scores improved from pretest to posttests for both interventions on all seven measures, and these improvements were statistically significant in all seven cases for MIT and in five of seven cases for PMR. Significant statistical differences were observed between groups on the visual analog scale for traumatic memories: MR scores declined from pretest to the immediate posttest (p = .002) but showed no further decline. MIT scores also declined from pretest to immediate posttest (p < .001), but they continued to improve over the subsequent week (p = .002) and were sustained one month following treatment. Mean MIT scores were lower than mean PMR scores at one week and one month (ps < .002). no adverse events were reported in either group.
Conclusion: MIT is an easy to apply technique that requires few resources and little training. The results strongly suggests that MIT could be a useful tool in the treatment of traumatic memories and yields proof-of-principle support for conducting future research with a large cohort, properly powered to stablish efficacy.
ClinicalTrials.gov Identifier: NCT03627078