Four-step Approach Research Articles

Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market

This paper proposes a long short-term memory (LSTM) neural network model to predict daily stock price movements based on asset pricing factors (i.e., the five factors proposed by Fama and French, and the short-term momentum factor). Based on three independent experiments, we systematically evaluate the explanatory power and the predictive power of the LSTM model by employing 3316 A-share listed companies in the Shanghai and Shenzhen stock exchanges from the in-sample period January 1, 2008 to December 31, 2019. Furthermore, we propose a four-step approach to dynamically update the underlying stocks in different portfolios based on the empirical findings. All portfolios are simulated using out-of-sample data (i.e., from January 1, 2020, to May 31, 2021) to avoid look-ahead bias. The trading results suggest that our dynamic investment strategies are superior to the benchmark index and are able to generate significant returns with relatively low risks.

PM 3/66 (3) Guidelines for the management of plant health risks of biowaste of plant origin

<scp>PM</scp> 3/66 (3) Guidelines for the management of plant health risks of biowaste of plant origin

An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring

Continuous crop monitoring often requires a time-series set of satellite images. Since satellite images have a trade-off in spatial and temporal resolution, spatiotemporal image fusion (STIF) has been applied to construct time-series images at a consistent scale. With the increased availability of high spatial resolution images, it is necessary to develop a new STIF model that can effectively reflect the properties of high spatial resolution satellite images for small-scale crop field monitoring. This paper proposes an advanced STIF model using a single image pair, called high spatial resolution image fusion using object-based weighting (HIFOW), for blending high spatial resolution satellite images. The four-step weighted-function approach of HIFOW includes (1) temporal relationship modeling, (2) object extraction using image segmentation, (3) weighting based on object information, and (4) residual correction to quantify temporal variability between the base and prediction dates and also represent both spectral patterns at the prediction date and spatial details of fine-scale images. The specific procedures tailored for blending fine-scale images are the extraction of object-based change and structural information and their application to weight determination. The potential of HIFOW was evaluated from the experiments on agricultural sites using Sentinel-2 and RapidEye images. HIFOW was compared with three existing STIF models, including the spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC. Experimental results revealed that the HIFOW prediction could restore detailed spatial patterns within crop fields and clear crop boundaries with less spectral distortion, which was not represented in the prediction results of the other three models. Consequently, HIFOW achieved the best prediction performance in terms of accuracy and structural similarity for all the spectral bands. Other than the reflectance prediction, HIFOW also yielded superior prediction performance for blending normalized difference vegetation index images. These findings indicate that HIFOW could be a potential solution for constructing high spatial resolution time-series images in small-scale croplands.

Lane-level trajectory reconstruction based on data-fusion

While lane-changing movements are performed on the entire motorway network for various reasons, such as overtaking, their intensity is substantially greater near complex segments, such as weaving areas. Aside from mandatory lane changes, some drivers also conduct lateral maneuvers for cooperation or anticipation near network nodes. Unlike longitudinal driver behavior (car-following models), lateral driver behavior (lane-changing movements) has received fewer research efforts. The scarcity of suitable data resources to analyze these behaviors and movements might be a crucial cause for this research gap. This paper presents a four-step approach for reconstructing and correcting lateral bias in trajectories collected by a commercial traffic information application running on everyday smartphones. The resulting lateral position is accurate enough to allow for identification of the driving lane, and thus, the lane changes. The algorithm's core is built on a data fusion method using trajectory and loop detector data. The evaluation and validation of the proposed algorithm using drones and closed-circuit television (CCTV) data demonstrate that the core of the algorithm can correctly match more than 94% of trajectory and loop detector data. Between each pair of successive detector stations, the lateral position error has been significantly corrected and reduced to less than half the width of a standard lane of motorway networks. As a result, more than 90% of processed trajectory sample points are in the correct lane. The algorithm requires just two calibration parameters, so it is relatively simple to apply to other test networks.

Novel Route to Produce Hydrocarbons from Woody Biomass Using Molten Salts.

The thermochemical decomposition of woody biomass has been widely identified as a promising route to produce renewable biofuels. More recently, the use of molten salts in combination with pyrolysis has gathered increased interest. The molten salts may act as a solvent, a heat transfer medium, and possibly also a catalyst. In this study, we report experimental studies on a process to convert woody biomass to a liquid hydrocarbon product with a very low oxygen content using molten salt pyrolysis (350–450 °C and atmospheric pressure) followed by subsequent catalytic conversions of the liquids obtained by pyrolysis. Pyrolysis of woody biomass in molten salt (ZnCl2/NaCl/KCl with a molar composition of 60:20:20) resulted in a liquid yield of 46 wt % at a temperature of 450 °C and a molten salt/biomass ratio of 10:1 (mass). The liquids are highly enriched in furfural (13 wt %) and acetic acid (14 wt %). To reduce complexity and experimental issues related to the production of sufficient amounts of pyrolysis oils for further catalytic upgrading, model studies were performed to convert both compounds to hydrocarbons using a three-step catalytic approach, viz., (i) ketonization of acetic acid to acetone, (ii) cross-aldol condensation between acetone and furfural to C8–C13 products, followed by (iii) a two-stage catalytic hydrotreatment of the latter to liquid hydrocarbons. Ketonization of acetic acid to acetone was studied in a continuous setup over a ceria–zirconia-based catalyst at 250 °C. The catalyst showed no signs of deactivation over a period of 230 h while also achieving high selectivity toward acetone. Furfural was shown to have a negative effect on the catalyst performance, and as such, a separation step is required after pyrolysis to obtain an acetic-acid-enriched fraction. The cross-aldol condensation reaction between acetone and furfural was studied in a batch using a commercial Mg/Al hydrotalcite as the catalyst. Furfural was quantitatively converted with over 90% molar selectivity toward condensed products with a carbon number between C8 and C13. The two-stage hydrotreatment of the condensed product consisted of a stabilization step using a Ni-based Picula catalyst and a further deep hydrotreatment over a NiMo catalyst, in both batch setups. The final product with a residual 1.5 wt % O is rich in (cyclo)alkanes and aromatic hydrocarbons. The overall carbon yield for the four-step approach, from pinewood biomass to middle distillates, is 21%, assuming that separation of furfural and acetic acid after the pyrolysis step can be performed without losses.

A data-driven degradation prognostics approach for rolling element bearings

Degradation prognostic plays a crucial role in increasing the efficiency of health management for rolling element bearings (REBs). In this paper, a novel four-step data-driven degradation prognostics approach is proposed for REBs. In the first step, a series of degradation features are extracted by analyzing the vibration signals of REBs in time domain, frequency domain and time-frequency domain. In the second step, three indicators are utilized to select the sensitive features. In the third step, different health state labels are automatically assigned for health state estimation, where the influence of uncertain initial condition is eliminated. In the last step, a multivariate health state estimation model and a multivariate multistep degradation trend prediction model are combined to estimate the residence time in different health status and remaining useful life (RUL) of REBs. Verification results using the XJTU-SY datasets validate the effectiveness of the proposed method and show a more accurate prognostics results compared with the existing major approaches.

The concept of integration in mixed methods research: a step-by-step guide using an example study in physiotherapy

ABSTRACT Background Integration is a unique attribute of Mixed Methods Research (MMR). However, some MMR studies, published in the field of physiotherapy and other allied health professions, have illustrated a lack of understanding of the concept of integration. Aims The purpose of this paper is to provide guidance for integrating elements of mixed methods research in order to effectively support evidence-based practice in health. Methods The concept of integration of findings is explained with reference to the author’s recent PhD study, which used a mixed methods exploratory sequential design. This article describes how the author used a narrative joint display to integrate findings from both the qualitative and survey phases of the study. Then, a step-by-step approach is introduced to guide the interpretation and integration of the findings. Results This four-step approach demonstrates integration of the two different datasets: Creating a joint display, linking activity, establishing relationships, and interpreting and reporting. Tables and Figures are used to support detailed description and illustration of the integration process. Discussion A joint display provides a visual representation of how the qualitative and quantitative findings in a MMR study can be integrated. In this way, interpretation of the data drawn from this process extend beyond the individual findings of each study component to facilitate a greater understanding of complex health care issues.

Teaching fiberoptic-assisted tracheoscopy in very low birth weight infants: A randomized controlled simulator study.

ObjectiveWe developed a fiberoptic-assisted tracheoscopy (FAST) method to avoid direct laryngoscopy during surfactant replacement therapy and compared two training approaches on a very low birth weight (VLBW) infant simulator.DesignThis prospective randomized controlled study was conducted at the Department of Neonatology and Pediatric Intensive Care Medicine of the University Medical Center Hamburg-Eppendorf, Germany.ParticipantsWe recruited physicians, trainees, students, and nurses without prior experience in endoscopic techniques.InterventionsParticipants were assigned randomly to a group that received instructions according to Peyton’s Four-Step Approach and a control group that received standard bedside teaching only.Main outcome measuresPrimary endpoints were the total and the component times required to place the bronchoscope and the method success.ResultsWe recruited 186 participants. Compared with the control group, the Peyton group had a lower mean (±standard deviation) FAST completion time (33.2 ± 27.5 s vs. 79.5 ± 47.9 s, p < 0.001; d = 1.12) and a higher FAST success rate (95% vs. 84%, p = 0.036, V = 0.18).ConclusionAfter standardized training, the vast majority of novices completed FAST successfully. Peyton’s four-step approach resulted in faster and more successful performance than standardized training.

MetaOntology: Toward developing an ontology for the metaverse.

Metaverse is now perceived as a celebrated future version of the internet. In this new anticipated virtual universe, interconnected digital platforms leveraged by augmented, extended, and virtual realities will elevate users' immersive experiences through multidimensional interactions. In particular, users will be offered a broad spectrum of digital activities within a newly immersive setting mediated by technology. This study aims to design a domain ontology (MetaOntology) for the metaverse to provide an explicit specification of relevant state-of-the-art technologies and infrastructure. A four-step methodological approach is followed to construct the designated ontology. Due to the immaturity of the metaverse, MetaOntology is not intended to furnish a complete outlook on the domain, rather it aims to establish a cornerstone so as to facilitate future efforts in building extant versions of this ontology considering the evolvement of relevant technologies.

An exploration of high performance computing cloud contract for SMEs: a systematic literature review

PurposeHigh performance computing (HPC) is used to solve complex calculations that personal computing devices are unable to handle. HPC offers the potential for small- and medium-size enterprises (SMEs) to engage in product innovation, service improvement and the optimization of resource allocation (Borstnar and Ilijas, 2019). However, the expensive infrastructure, maintenance costs and resource knowledge gaps that accompany the use of HPC can make it inaccessible to SMEs. By moving HPC to the cloud, SMEs can gain access to the infrastructure without the requirement of owning or maintaining it, but they will need to accept the terms and conditions of the cloud contract. This paper aims to improve how SMEs access HPC through the cloud by providing insights into the terms and conditions of HPC cloud contracts.Design/methodology/approachThis paper adopts a systematic literature review by implementing a four-step approach. A comprehensive search was undertaken and results synthesized to enable this paper’s objectives to be met.FindingsThis paper proposes that SMEs could gain competitive advantage(s) by understanding their own needs and improving their contract negotiation abilities, service management skills and risk management abilities before accepting the terms and conditions of the cloud contract. Furthermore, a checklist, service-level agreement, easily ignored elements and risk areas are presented as guidance for SMEs when reviewing their HPC cloud contract(s).Originality/valueWhile HPC cloud contracts are a niche research topic, it is one of the key factors influencing the ability of SMEs to access HPC through the cloud. It is, however, by no means a level playfield with SMEs at a distinct disadvantage because of not influencing the writing up of the HPC cloud contract. The added value of the paper is that it contributes to our overall understanding of the terms and conditions of HPC cloud contracts.

Synthesis of a library of 2-aryl-2H-tetrazole-5-carboxamides for photoaffinity labeling of aminergic G-protein coupled receptors

A four-step approach to 2-aryl-2H-tetrazole-5-carboxamides bearing GPCR-focused N-substituted piperazine residues involves ‘SAFE’ diazotransfer onto 1-(piperazin-1-yl)butane-1,3-diones followed by [3+2] cycloaddition of arenediazonium cations at the intermediate ααα-diazo acetamides. The compounds prepared are intended for photoaffinity labeling of aminergic G-protein coupled receptors.

Efficacy and influencing factors of the four-step approach combining the situational simulation teaching method in the clinical practice of standardized training for residents.

Background and AimsClinical skills practice is an essential component in standardized residency training. However, traditionally skill training methods are dogmatic and not all residents are exposed to such prescribed situations during their residency. The aims of this study were to evaluate the effectiveness and influence factors of a four‐step approach combining situational simulation teaching methods in clinical practice for residents.MethodsEnrolled all second‐year residents from the internal medicine base between May 2017 and May 2018 (n = 94), randomly divided into two groups. Forty‐eight residents were selected as experimental group, while the others 46 as the control group. Adopted traditional clinical practice method in the teaching and assessment of the control group, while used four‐step approach combining situational simulation teaching method in experimental group. We compared the theoretical and skill assessment scores in preclass and postclass. Conducted a satisfaction survey after class and analyzed the influencing factors of the teaching effect evaluation.ResultsThere were no significant differences in the theoretical and skill assessment scores between experimental group and control group at the beginning. After the class, both the average skill assessment and Direct Observation of Procedural Skills scores of the experimental group were higher than those of the control. Satisfaction survey findings identified that the experimental group expressed higher satisfaction. Logistic regression showed that educational background, “situational simulation mode helps to improve clinical skills training,” “helps to maintain attention during learning,” and “helps improve the ability to exercise analysis and solve problems” were the influencing factors of learners' satisfaction.ConclusionThe application of four‐step approach combining situational simulation teaching methods in the clinical practice of residents can significantly improve skills, thinking ability, decision‐making ability, and teaching satisfaction. Therefore, four‐step approach combining situational simulation teaching methods is worth promoting in teaching clinical skills for internal medicine residency training.

Barriers and facilitators to implementing a nursing clinical decision support system in a tertiary hospital setting: A qualitative study using the FITT framework

ObjectiveClinical decision support systems (CDSSs) have been increasingly introduced to health care settings; however, their adoption is far from ideal. Guided by the FITT framework, this study aims to explore barriers and facilitators to the implementation of a CDSS from the perspective of nurses. MethodsA qualitative study with 200 h of participatory observation and 21 semi structured interviews was conducted from February to August 2021 in four medical-surgical wards in a 2000-bed tertiary hospital in Shanghai, China. The field notes were typed and the audio-recorded interviews were transcribed to texts verbatim and were coded with a four-step approach. We used the FITT framework to interpret our findings based on the technology, individual and task attributes and the fit between them. ResultsA total of twelve categories were identified, which were integrated into two themes: barriers and facilitators to system implementation. All categories but one can be mapped to the three attributes of the FITT framework: technology, individual and task. We assumed that management has a vital role to play in the following areas: addressing user resistance, improving system usability, setting standards on practice and, finally, building connectivity between nurses and the technical staff to improve the fit between the technology, individual and task attribute and thus promote system implementation. ConclusionBarriers and facilitators to CDSS implementation include system-related, user-related and organizational factors which can largely be fit into the FITT framework. There is potential to extend the FITT framework to represent management intervention on inter-disciplinary collaboration. Future empirical studies on facilitating strategies from the management to improve user experience and willingness of CDSS adoption are needed.

A Holistic Overview of Studies to Improve Group-Based Assessments in Higher Education: A Systematic Literature Review

There is a soaring demand for work-ready graduates who can quickly adapt to an ever-challenging work environment. Group-based assessments have been widely recommended as a means to develop the skills required for the world of work. However, group-based assessments are perceived as challenging for both students and educators. This systematic literature review (SLR), based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA), focuses on analyzing and synthesizing the existing literature on group-based assessments. A four-step approach was undertaken in order to conduct this research. The SLR identified 71 relevant articles, analyzed using thematic analysis with the aid of NVivo software. An open coding approach was adopted to generate codes. The validity of the SLR process and the reliability of the research tool were maintained through the use of trustworthiness. The findings identified dominant themes such as self- and peer evaluations, training students for group work assessments, group formation, group size, and the role of academics and technology in facilitating group processes. The outcomes of this review contribute significantly to the design and administration of group-based assessments in higher education by providing academics with practical guidelines to effectively facilitate group-based assessments which fit the purpose.

Applying ecosystem services principles to the derivation of freshwater environmental quality standards

Freshwater ecosystems provide many benefits to people (ecosystem services), but their biodiversity and functioning is threatened by anthropogenic stressors, including chemical pollution. Environmental quality standards (EQSs) for chemicals, are designed to protect species, but their derivation takes no account of ecosystem processes or species interactions and hence their links to biodiversity, ecosystem functioning and ecosystem services are uncertain. Here we explore a novel approach for the derivation of chemical EQSs to protect ecosystem service providing units (i.e., assemblages of species with ecological traits that underpin an ecosystem service) and ultimately protect ecosystem service delivery in different freshwater bodies and river basins. This approach, which was illustrated for two Water Framework Directive priority chemicals (a pyrethroid insecticide and polybrominated diphenyl ethers), is the first application of an ecosystem services framework to derive EQS values. The four-step approach enabled the derivation of ecosystem service-specific and river basin-specific standards that can inform spatially-defined and targeted management of chemical impacts on the aquatic (freshwater) environment. The derivation of ecosystem service specific EQS values also helps in communicating and highlighting the incremental benefits of improving water quality. A Tier I assessment focusing on protecting ecosystem service providing units was successfully undertaken based on available ecotoxicological effects data for each chemical. However, Tier II and Tier III assessments require further scientific research and tool development to quantify chemical impacts on ecosystem services delivery based on service providing taxa and their functional traits.

Development of an Integrated Soft E-Nose for Food Quality Assessment

Food is one of the basic essential elements for human beings. Its quality is directly related to our physical, emotional, and social well-being. An electronic nose is a non-destructive instrument and is an effective solution for quick and easy determination of food quality. In this paper, an integrated soft E-nose methodology focusing on performance enhancement and complexity reduction is proposed and implemented for the effective classification of beef quality and prediction of microbial population in beef. The soft E-nose methodology is tested on 18 available datasets. Ours is a four-step approach that includes validated data collection, signal processing with a moving average filter and principal component analysis for feature reduction, efficient pattern recognition using machine learning and deep learning techniques like support vector machine, k-nearest neighbors, artificial neural network, extreme learning machine, and deep neural network for classification and regression, and an output stage for classification and prediction of the microbial population of beef. This methodology could provide accuracy greater than 98% using k-nearest neighbors, artificial neural network methods, and a correlation coefficient of greater than 0.98 using support vector machine and artificial neural network methods. The proposed methodology is validated using 10-fold cross-validation.

What is truly representative: the development of a Restaurant Authenticity Scale (RAS)

ABSTRACT The purpose of this study was to develop a restaurant authenticity scale (RAS), containing multiple restaurant attributes and authenticity conceptualizations. A four-step approach was used to develop this scale. Ultimately, the RAS contained 20 items and three dimensions: authenticity of ambiance, authenticity of cuisine, and authenticity of people. Findings from the current study show that the authenticity of cuisine dimension explained the greatest amount of variance of restaurant authenticity. With regard to theory, the current study has determined which items, authenticity conceptualizations, and dimensions should be included in the RAS. The current study offers guidance to restaurant practitioners by calling attention to key restaurant attributes which are critical to guests’ perceptions of restaurant authenticity.

Priority Indicators for Adolescent Health Measurement – Recommendations From the Global Action for Measurement of Adolescent Health (GAMA) Advisory Group

PurposeThis article describes the selection of priority indicators for adolescent (10–19 years) health measurement proposed by the Global Action for Measurement of Adolescent health advisory group and partners, building on previous work identifying 33 core measurement areas and mapping 413 indicators across these areas. MethodsThe indicator selection process considered inputs from a broad range of stakeholders through a structured four-step approach: (1) definition of selection criteria and indicator scoring; (2) development of a draft list of indicators with metadata; (3) collection of public feedback through a survey; and (4) review of the feedback and finalization of the indicator list. As a part of the process, measurement gaps were also identified. ResultsFifty-two priority indicators were identified, including 36 core indicators considered to be most important for measuring the health of all adolescents, one alternative indicator for settings where measuring the core indicator is not feasible, and 15 additional indicators for settings where further detail on a topic would add value. Of these indicators, 17 (33%) measure health behaviors and risks, 16 (31%) health outcomes and conditions, eight (15%) health determinants, five (10%) systems performance and interventions, four (8%) policies, programmes, laws, and two (4%) subjective well-being. DiscussionA consensus list of priority indicators with metadata covering the most important health issues for adolescents was developed with structured inputs from a broad range of stakeholders. This list will now be pilot tested to assess the feasibility of indicator data collection to inform global, regional, national, and sub-national monitoring.

Optimising water resources management by Using Water Evaluation and Planning (WEAP) in the West of Iraq

Iraq has been suffering from decreasing Euphrates discharge due to the construction of dams within upstream countries and the use of surface irrigation systems. The country is facing a problem with meeting the increasing demand for water as a result of population growth and development in the industrial and agricultural sectors. Therefore, a simulation modelling was applied for western Iraq (Ramadi city as a case study) using the Water Evaluation and Planning System (WEAP) for the period 2018–2035. This research follows a four-step approach that involves: (i) evaluating the available water of the Euphrates River under declined water imports caused by the construction of dams in Turkey and Syria, (ii) assessing present and future water demands of the domestic, industrial, and agricultural sectors, (iii) improving water productivity (WP) by means of saving more water, (iv) estimating the economic returns under improved water use. The results showed that Iraq would face a serious problem in the coming years, represented by the limited storage of Haditha Dam, which is considered the strategic water storage site for the central and southern regions of Iraq. The study indicated the necessity of finding alternative sources of water supply by adopting new water management strategies to reduce the water deficit.

Chatbot for construction firms using scalable blockchain network

Information and Communication Technologies (ICT), including multimedia tools, email services, voice-based tools, and handheld computing tools, have been extensively used for automating and digitalizing different construction processes and activities. However, these technologies are subjected to single-point attacks or failures, manipulation, and lack of privacy and traceability. This study introduces a novel information exchange and management system for construction firms based on blockchain technology and chatbots. The system leverages the characteristics of blockchain technology in terms of peer-to-peer operation mode, data integrity, structuring, and privacy, and the chatbots' merits regarding ease of use and degree of automation. The system is developed for tracking work progress in construction projects as a generic use-case using a four-step approach. First, a private blockchain network is configured for data distribution and storage. Second, a smart contract is coded for regulating data writing/reading operations. Third, a chatbot is developed for data collection and retrieval through textual conversations. Fourth, serverless cloud function and cloudant database are configured to allow the linkage between the blockchain network and the chatbot. A prototype of the system is built and applied to a case study of non-residential construction project to test and verify its capabilities. Further, the system's performance is assessed in terms of the writing and reading latencies and the storage size. The system features can be extended by embedding mathematical algorithms to simultaneously analyze data and employing Inter-Planetary File System (IPFS) to maintain visuals and large-size data.