Search

Copyright of Journal of Ege University Nursing Faculty (JEUNF) is the property of Journal of Ege University Nursing Faculty (JEUNF) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Collaborative filtering is a powerful marketing tool capable of improving the customer experience and increasing sales. Segmentation of the customer base by collaborative filtering assumes that those interested in specific products will also be interested in similar products. The basic methods of collaborative filtering are analyzed in the present work. The analysis reveals what factors affect the selection of a collaborative filtering method in segmentation of the retail customer base. [ABSTRACT FROM AUTHOR]

AbstractObjectiveMethodResultsDiscussion\nHIGHLIGHTSThe present study seeks to add to the existing literature by determining if having a plan for suicide, is associated with an individual’s self-reported likelihood of attempting suicide in the future.Data came from a sample of 97 United States Army personnel with past week ideation or lifetime attempt history. Assessments were collected at baseline, 1-month, 3-month, and 6-months.Self-reported likelihood of attempting suicide in the future was not associated with the presence of a plan for suicide overall or a plan with a specific method (i.e., firearm, cutting/scratching, and medication).Although a plan for suicide is commonly thought to indicate elevated risk our findings suggest that presence or absence of suicide plans is not associated with more self-reported likelihood of a future suicide attempt.The self-reported likelihood of attempting suicide in the future was not associated with the presence of a plan for suicide among service members.Findings suggest that presence or absence of suicide plans is not associated with self-reported likelihood of suicidal behavior among service members.Clinicians and researchers working with a military population may benefit from a broader approach to risk assessment and safety planning that does not rely too heavily on an individual’s self-reported plans for suicide.The self-reported likelihood of attempting suicide in the future was not associated with the presence of a plan for suicide among service members.Findings suggest that presence or absence of suicide plans is not associated with self-reported likelihood of suicidal behavior among service members.Clinicians and researchers working with a military population may benefit from a broader approach to risk assessment and safety planning that does not rely too heavily on an individual’s self-reported plans for suicide. [ABSTRACT FROM AUTHOR]

The article presents the results of a pilot programme to include psychological tests in the qualification rules for candidates for military divers and scuba divers. 207 candidates for training at the Polish Army Divers and Scuba Divers Training Centre were tested. They were subjected to mental fitness tests, personality tests, psychomotor fitness tests and general fitness tests. Numerous psychological contraindications to undertaking training and diving service were identified; the most common issue identified being a lack of robustness of the diver's mental state for dealing with the situations likely to be encountered in the water. The conclusions suggest that, as in other countries, psychological examinations should be included in the qualification of candidates for diving. The results of the tests, despite their non-mandatory nature, were partly taken into account in the opinion of the Military Maritime and Medical Commission. [ABSTRACT FROM AUTHOR]

This paper presents the results of a pilot programme to include psychological tests in the recruitment rules for candidates for military divers and scuba divers. A total of 207 candidates to undertake training at the Polish Army Divers and Scuba Divers Training Centre were examined. They were subjected to tests of mental fitness, tests examining personality traits, tests of psychomotor fitness and tests of general fitness. Numerous psychological contraindications to undertake diving training and service were found; the most common was a low degree of mental fitness. In conclusion, it was suggested that, following the example of other countries, psychological testing should be included in the selection of candidates for divers. The results of the tests, although not obligatory, were partly taken into account in the opinion issued by the Military Medical and Maritime Commission. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Addressing socio-environmental challenges requires a multifaceted approach and a comprehensive understanding, which is precisely what transdisciplinary research can provide. The transdisciplinary approach, however, brings new challenges to early career researchers, who have to acquire the necessary methods and skills while conducting their ongoing research. Along with increasing access to resources and training, guidance from supervisors and informal exchange with other transdisciplinary early career researchers play key roles in effectively supporting them. [ABSTRACT FROM AUTHOR]

Abstract Pitfall trapping is one of the standard methods used for the capture of ground‐active arthropod groups. Despite being frequently used, the standardization of this method is problematic due to the large range of combinations of the individual parameters of pitfall traps with varying efficacy under different environmental conditions. We evaluated the effects of the trap diameter, the fixing fluid, and their combination on the capture efficacy for harvestmen (Opiliones) and millipedes (Diplopoda). We used pitfall traps with three different diameters: 3 cm, 5 cm, and 12 cm, filled with three types of fixing fluids (saturated fluid of NaCl, 10:1 mixture of 70% ethanol and glycerol and 4% formaldehyde). Altogether, 90 traps representing nine combinations of trap diameters and fixing fluid were placed on a mown meadow in spring and autumn intervals for a total of 45 days. We sampled 1,488 individuals representing 11 harvestmen species and 881 individuals representing 11 millipede species. Large (d = 12 cm) and medium (5 cm) traps captured significantly more millipede species and individuals than the small‐sized traps (3 cm). The same effect was observed for harvestmen species richness, whereas the medium traps (d = 5 cm) captured the highest mean activity of harvestmen. By analyzing the differences in the body sizes of the studied arthropods in relation to the trap diameter and fluid, we found that larger traps, as well as traps filled with NaCl solution, captured larger harvestmen more frequently than the other trap types. Our results revealed that the combination of larger traps (d = 5 and 12 cm) and formaldehyde was most effective in the capture of both studied groups. However, the disadvantage of formaldehyde is its toxicity.

Automating traceability-based decision-making can shorten the reaction time to supply chain disruptions. This paper develops a framework for choosing automated decision-making (ADM) methods based on traceability data. It contains a toolbox comprising methods suitable for ADM, respective selection criteria and a new process to select a suitable ADM method based on companies' requirements. This process is based on an evaluation matrix matching methods and criteria. As a result, the ADM framework suggests the most suitable method to automate a specifically chosen decision. The developed framework is validated in the supply chain of a globally operating truck manufacturer. [ABSTRACT FROM AUTHOR]

Objective: This paper presents the Binary-Based Model (BBM), a new approach to Human Factors (HF) method selection. The BBM helps practitioners select the most appropriate HF methodology in relation to the complexity within the target system. Background: There are over 200 HF methods available to the practitioner and little guidance to help choose between them. Method: The BBM defines a HF "problem space" comprising three complexity attributes. HF problems can be rated against these attributes and located in the "problem space." In addition, a similar HF "approach space" in which 66 predictive methods are rated according to their ability to confront those attributes is defined. These spaces are combined into a "utility space" in which problems and methods coexist. In the utility space, the match between HF problems and methods can be formally assessed. Results: The method space is split into octants to establish broad groupings of methods distributed throughout the space. About 77% of the methods reside in Octant 1 which corresponds to problems with low levels of complexity. This demonstrates that most HF methods are suited to problems in low-complexity systems. Conclusion: The location of 77% of the rated methods in Octant 1 indicates that HF practitioners are underserved with methods for analysis of HF problems exhibiting high complexity. Application: The BBM can be used by multidisciplinary teams to select the most appropriate HF methodology for the problem under analysis. All the materials and analysis are placed in the public domain for modification and consensus building by the wider HF community. [ABSTRACT FROM AUTHOR]

This present study sought to examine how demographics and the availability of means for suicide converge to impact method selection between firearms and hanging, which are the two most lethal and commonly used methods for suicide in the United States. Data were collected as part of a larger online study that gathered information about suicide decedents and the circumstances around their death from family members and friends; the most common sources being parents, friends, and other family members. This study used data from those who died by suicide by firearm or hanging (n = 149); and was restricted to those who identified as male and white. The study examined if age, education, marital status, military affiliation, and firearm ownership impacted method selection. Results indicated that age and owning a firearm were associated with using a firearm in a suicide death. Additionally, those who used a firearm were significantly younger and more likely to own a firearm than those who used another method; and those who died by hanging were significantly younger than those who used another method. Findings from this study can be leveraged to improve the effectiveness of means safety efforts and create more personalised public health messages on firearm safe storage. [ABSTRACT FROM AUTHOR]

Data analysts when forecasting large number of time series, they regularly employ one of the following methodological approaches: either select a single forecasting method for the entire dataset (aggregate selection), or use the best forecasting method for each time series (individual selection). There is evidence in the predictive analytics literature that the former is more robust than the latter, as in individual selection you tend to overfit models to the data. A third approach is to first identify homogeneous clusters within the dataset, and then select a single forecasting method for each cluster (cluster selection). To that end, we examine three machine learning clustering methods: k-medoids, k-NN and random forests. The evaluation is performed in the 645 yearly series of the M3 competition. The empirical evidence suggests: (a) random forests provide the best clusters for the sequential forecasting task, and (b) cluster selection has the potential to outperform aggregate selection. We compare aggregate selection versus cluster selection for forecasting a dataset The evaluation is performed in the 645 yearly series of the M3 competition We first use one of three clustering terchiques: k-medoids, k-NN and random forests We then forecast every cluster with the best possible forecasting method Random forests provide the best clusters for the sequential forecasting task Cluster selection has the potential to outperform aggregate selection. [ABSTRACT FROM AUTHOR]

Context awareness is essential for successful business process management (BPM). So far, research has covered relevant BPM context factors and context-aware process design, but little is known about how to assess and select BPM methods in a context-aware manner. As BPM methods are involved in all stages of the BPM lifecycle, it is key to apply appropriate methods to efficiently use organizational resources. Following the design science paradigm, the study at hand addresses this gap by developing and evaluating the Context-Aware BPM Method Assessment and Selection (CAMAS) Method. This method assists method engineers in assessing in which contexts their BPM methods can be applied and method users in selecting appropriate BPM methods for given contexts. The findings of this study call for more context awareness in BPM method design and for a stronger focus on explorative BPM. They also provide insights into the status quo of existing BPM methods. [ABSTRACT FROM AUTHOR]

Pitfall trapping is one of the standard methods used for the capture of ground-active arthropod groups. Despite being frequently used, the standardization of this method is problematic due to the large range of combinations of the individual parameters of pitfall traps with varying efficacy under different environmental conditions. We evaluated the effects of the trap diameter, the fixing fluid, and their combination on the capture efficacy for harvestmen (Opiliones) and millipedes (Diplopoda). We used pitfall traps with three different diameters: 3 cm, 5 cm, and 12 cm, filled with three types of fixing fluids (saturated fluid of NaCl, 10:1 mixture of 70% ethanol and glycerol and 4% formaldehyde). Altogether, 90 traps representing nine combinations of trap diameters and fixing fluid were placed on a mown meadow in spring and autumn intervals for a total of 45 days. We sampled 1,488 individuals representing 11 harvestmen species and 881 individuals representing 11 millipede species. Large (d = 12 cm) and medium (5 cm) traps captured significantly more millipede species and individuals than the small-sized traps (3 cm). The same effect was observed for harvestmen species richness, whereas the medium traps (d = 5 cm) captured the highest mean activity of harvestmen. By analyzing the differences in the body sizes of the studied arthropods in relation to the trap diameter and fluid, we found that larger traps, as well as traps filled with NaCl solution, captured larger harvestmen more frequently than the other trap types. Our results revealed that the combination of larger traps (d = 5 and 12 cm) and formaldehyde was most effective in the capture of both studied groups. However, the disadvantage of formaldehyde is its toxicity. [ABSTRACT FROM AUTHOR]

Interdisciplinary product development teams are responsible for the solution of complex problems and the creation of innovative ideas. Innovation Coaching within the approach of ASD-Agile Systems Design fosters agile product development processes by realizing agility in the work of development teams. With the aim to implement ASD-Innovation Coaching in practice, a support is needed to enable coaches to select and apply coaching methods based on the team´s situation and need. Within a systematic literature review, 217 proven coaching methods were identified and evaluated regarding their applicability and success in product development projects. Based on specific quality criteria a total of 51 methods are selected and presented in a systematic and intuitive process model. [ABSTRACT FROM AUTHOR]

An array of research methods has been employed for social-qualitative inquiries. However, the selection of specific research methods has rarely been given adequate attention. We mapped out the variety of research methods used in social-qualitative inquiries used in the study of forest policy. Our “problem-method fit” map is based on the usage quantity of a method employed in specific forest policy research themes and contextual analyses. Our map provides a suitable basis for rapid appraisal before deciding appropriate research methods for future studies. While the map provides only an indication of the appropriate methods, it may be supplemented and adapted case-by-case according to the specific needs of the research theme. • We mapped the commonly used research methods in forest policy analysis • The map is “problem-method fit” for specific policy themes and contextual analyses • It can be used as for rapid appraisal when choosing appropriate research methods

The article presents the results of the treatment of 103 patients with intraarticular fractures of the distal humerus over the period from 2002 to 2020, who were under treatment at National Medical Research Center for Traumatology and Orthopedics named after N.N. Priorov and in the State Clinical Hospital named after Eramishantsev. All patients underwent surgical treatment of intraarticular fractures in various ways, depending on the nature and location of the damage. Osteosynthesis with excision of the ulnar process was used in 38 (36%) patients. Fixation of fractures without cutting off the ulnar process through the tendon of the triceps muscle of the shoulder was made in 43 (41%) patients, in 22 cases (23%) they performed osteosynthesis with Hebert screws. The paper presents in detail surgical approaches for various cases of fractures. Based on a retrospective analysis of the results, an algorithm for choosing the method of osteosynthesis for various fracture localization has been developed. The results were evaluated using the Mayo Elbow Performance Score. The developed algorithm for choosing surgical access, depending on the location of the fracture, selects the optimal access to obtain good functional results. A significant (p <0.05-0.001) increase in the number of positive functional results during surgical treatment by 47.6%, with a simultaneous decrease in the duration of rehabilitation treatment by 33.8% was noted as a result of the proposed tactics of treatment of intra- and periarticular fractures of the distal shoulder bones. [ABSTRACT FROM AUTHOR]

Even though many studies have shown the usefulness of clustering for the exploration of spatio-temporal patterns, until now there is no systematic description of clustering methods for geo-referenced time series (GTS) classified as one-way clustering, co-clustering and tri-clustering methods. Moreover, the selection of a suitable clustering method for a given dataset and task remains to be a challenge. Therefore, we present an overview of existing clustering methods for GTS, using the aforementioned classification, and compare different methods to provide suggestions for the selection of appropriate methods. For this purpose, we define a taxonomy of clustering-related geographical questions and compare the clustering methods by using representative algorithms and a case study dataset. Our results indicate that tri-clustering methods are more powerful in exploring complex patterns at the cost of additional computational effort, whereas one-way clustering and co-clustering methods yield less complex patterns and require less running time. However, the selection of the most suitable method should depend on the data type, research questions, computational complexity, and the availability of the methods. Finally, the described classification can include novel clustering methods, thereby enabling the exploration of more complex spatio-temporal patterns. [ABSTRACT FROM AUTHOR]

Background and aims: Obesity is main risk factor for diverse physiological and socio-psychological conditions and premature mortality. Weight loss has been an important strategy for decreasing obesity related mortality. Bariatric surgery is getting wider acceptance as popular treatment option for obesity due to limitations of other treatment methods. Various pre-bariatric aspects can contradict the surgical intervention and result in postoperative complications if not properly assessed. This review assesses these aspects for identifying the points that affect the desired clinical outcomes and safety of bariatric surgery. Materials and Methods: Springer database, PubMed, Google Scholar and Scifinder were searched for literature. Results: Studies reveal that the obese individuals should be properly investigated for various psychiatric issues before subjecting them to bariatric surgery. Studies further suggest specific psychological interventions for obese individuals with severe psychiatric issues prior to bariatric surgery. The obese individuals should also be properly assessed and educated for their eating behavior, lifestyle changes and available social support so safety and efficacy of bariatric interventions remains well intact. Selection of bariatric procedure should be made on evaluation of factors such as disease conditions, current treatment and postoperative complications. Conclusion: Improved outcomes and safety of bariatric surgery can be concluded from assessment of pre-bariatric psychiatric aspects, available social support and rational selection of surgical method. [ABSTRACT FROM AUTHOR]

Intermittent demand shows irregular pattern that differentiates it from all other demand types. It is hard to forecasting intermittent demand due to irregular occurrences and demand size variability. Due to this reason, researchers developed ad hoc intermittent demand forecasting methods. Since intermittent demand has peculiar characteristics, it is grouped into categories for better management. In this paper, specialized methods with a focus of method selection for each intermittent demand category are considered. This work simplifies the intermittent demand forecasting and provides guidance to market players by leading the way to method selection based on demand categorization. By doing so, the paper will serve as a useful tool for practitioners to manage intermittent demand more easily. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Seven families of inversion methods for agricultural applications were identified. • Rationales behind applying inverse modeling in agriculture were summarized. • An operational procedure of inversion method selection was proposed. • The procedure considers practitioners' available resources and goals. Inverse modeling (IM) is a valuable tool in agriculture for estimating model parameters that aid in decision-making. It is particularly useful when parameters cannot be directly measured or easily estimated due to logistical constraints in agricultural settings. Unlike other estimation methods, IM combines a mechanistic model with observations of its outputs to derive the parameters of interest, allowing for the integration of various sources of knowledge. The availability of numerous data sources, such as remote sensing and crowdsourcing, with high spatial and temporal resolution, has expanded the potential of IM in agriculture. Practitioners can now incorporate the spatial and temporal footprint of observational data into parameter estimation. However, common IM techniques currently applied in agriculture often struggle to account for effectively spatial and temporal variability. Relevant IM methods that address these challenges are usually isolated within specific developer and user communities and are not well known within the agricultural community. There is a lack of comprehensive reviews focusing on IM methods suitable for handling spatial and temporal data in agriculture. In parallel, the process of conducting IM in agriculture remains under-formalized. Typically, specific IM methods are chosen for specific combinations of models and types of observational data, but the rationale behind their selection is rarely explained in publications. The relationship between IM methods, models, and observational data is unclear, making it overwhelming for new practitioners to choose an appropriate method. This complex problem, along with the diversity of IM methods, has yet to be adequately addressed while taking into account the specificities of agricultural applications. To address these challenges, this review aims to provide a structured classification of IM methods based on the practical needs of new practitioners in agriculture. It examines a wide range of inversion methods applied in agriculture-related domains and covers four key topics: i) the essential elements and general process of IM, ii) the main families of IM methods in agriculture and their characteristics, iii) the circumstances in which practitioners prefer using IM over other approaches, and their motivations, and iv) practical guidance on choosing a method family based on operational criteria. The review aims to help readers develop a clear understanding of the practice of inverse modeling, gain insights into the diversity of IM methods, and make informed choices when selecting a method family for their agricultural applications. [ABSTRACT FROM AUTHOR]

Abstract Background Unplanned pregnancy remains a common problem in many resource-limited settings, mostly due to limited access to modern family planning (FP) services. In particular, use of the more effective long-acting reversible contraceptive (LARC) methods (i.e., intrauterine devices and hormonal implants) remains low compared to the short-acting methods (i.e., condoms, hormonal pills, injectable hormones, and spermicides). Among reproductive-age women attending FP and antenatal care clinics in Uganda, we assessed perceptions and practices regarding the use of modern contraceptive methods. We specifically aimed to evaluate factors influencing method selection. Methods We performed a mixed-methods cross-sectional study, in which we administered structured interviews to 180 clients, and conducted 4 focus group discussions (FGDs) with 36 clients and 8 in-depth personal qualitative interviews with health service providers. We summarized quantitative data and performed latent content analysis on transcripts from the FGDs and qualitative interviews. Results The prevalence of ever use for LARC methods was 23%. Method characteristics (e.g., client control) appeared to drive method selection more often than structural factors (such as method availability) or individual client characteristics (such as knowledge and perceptions). The most common reasons for choosing LARC methods were: longer protection; better child-spacing; and effectiveness. The most common reasons for not choosing LARC methods included requiring a client-controlled method and desiring to conceive in the near future. The most common reasons for choosing short-acting methods were ease of access; lower cost; privacy; perceived fewer side effects; and freedom to stop using a method without involving the health provider. The personal characteristics of clients, which appeared to be important were client knowledge and number of children. The structural factor which appeared to be important was method availability. Conclusions Our results suggest that interventions to improve uptake of LARC among reproductive age women in this setting should consider: incorporating desired method-characteristics into LARC methods; targeted promotion and supply of LARC; and increased counselling, sensitization, and education.

In practice, product developers often do not select the most suitable design process or method for their task; instead, they use the most common or known ones. The reason for this inappropriate selection is the missing knowledge on the relevant influence factors and boundary conditions. These influence factors characterise the context of a development project. Hence, these factors can significantly influence the initial selection and the adaptation of design processes and methods. Therefore, this contribution provides product developers a usable context-model that supports them with the context-dependent selection and adaption of design processes and methods within their development project. [ABSTRACT FROM AUTHOR]

Multiple hypotheses tests decide on more than one hypothesis based upon the same data set. Despite the significant relevance for business research, we find that multiple testing methods are not systematically applied in our research area. As surprising this finding is, as crucial it is to the significance of research findings. False positive findings can inflate with the number of employed single tests. The focus on medicine and psychology related topics of current multiple testing literature might deter business researchers from employing the tests. Hence, we provide guidance to researchers by exposing the importance of multiple testing. Based on an application oriented systematization, Monte Carlo simulations, and a decision theoretic evaluation scheme, we highlight method recommendations for different conservatism definitions regarding errors. As implicit results we find, that the dependency structure in the data does not influence the method outcomes significantly. In addition, intuitive single step margin based approaches perform similar than sophisticated tests for typical multiple testing problems in a conservative setting. More liberal methods show a stable relation for all simulation designs. [ABSTRACT FROM AUTHOR]

Highlights • Innovative scoring approach to evaluate LC–MS methods for screening metabolites. • Each metabolite can be assigned to its best LC-Method. • Allows to choose a set of methods when analytical throughput is a constraint. • Enables univocal annotation of metabolites prior to multivariate analysis. Abstract Since the ultimate goal of untargeted metabolomics is the analysis of the broadest possible range of metabolites, some new metrics have to be used by researchers to evaluate and select different analytical strategies when multi-platform analyses are considered. In this context, we aimed at developing a scoring approach allowing to compare the performance of different LC–MS conditions for metabolomics studies. By taking into account both chromatographic and MS attributes of the analytes' peaks (i.e. retention, signal-to-noise ratio, peak intensity and shape), the newly proposed score reflects the potential of a set of LC–MS operating conditions to provide useful analytical information for a given compound. A chemical library containing 597 metabolites was used as a benchmark to apply this approach on two RPLC and three HILIC methods hyphenated to high resolution mass spectrometry (HRMS) in positive and negative ionization modes. The scores not only allowed to evaluate each analytical platform, but also to optimize the number of analytical methods needed for the analysis of metabolomics samples. As a result, the most informative combination of three LC methods and ionization modes was found, leading to a coverage of nearly 95% of the detected compounds. It was therefore demonstrated that the overall performance reached with three selected methods was almost equivalent to the performance reached when five LC–MS conditions were used. [ABSTRACT FROM AUTHOR]

Abstract Data Mining (DM) is a fundamental component of the Data Science process. Over recent years a huge library of DM algorithms has been developed to tackle a variety of problems in fields such as medical imaging and traffic analysis. Many DM techniques are far more flexible than more classical numerial simulation or statistical modelling approaches. These could be usefully applied to data-rich environmental problems. Certain techniques such as artificial neural networks, clustering, case-based reasoning or Bayesian networks have been applied in environmental modelling, while other methods, like support vector machines among others, have yet to be taken up on a wide scale. There is greater scope for many lesser known techniques to be applied in environmental research, with the potential to contribute to addressing some of the current open environmental challenges. However, selecting the best DM technique for a given environmental problem is not a simple decision, and there is a lack of guidelines and criteria that helps the data scientist and environmental scientists to ensure effective knowledge extraction from data. This paper provides a broad introduction to the use of DM in Data Science processes for environmental researchers. Data Science contains three main steps (pre-processing, data mining and post-processing). This paper provides a conceptualization of Environmental Systems and a conceptualization of DM methods, which are in the core step of the Data Science process. These two elements define a conceptual framework that is on the basis of a new methodology proposed for relating the characteristics of a given environmental problem with a family of Data Mining methods. The paper provides a general overview and guidelines of DM techniques to a non-expert user, who can decide with this support which is the more suitable technique to solve their problem at hand. The decision is related to the bidimensional relationship between the type of environmental system and the type of DM method. An illustrative two way table containing references for each pair Environmental System-Data Mining method is presented and discussed. Some examples of how the proposed methodology is used to support DM method selection are also presented, and challenges and future trends are identified. Highlights • A methodology to support Data Mining (DM) method choice in environmental sciences. • DMMCM is a conceptual map providing an overview of common Data Mining methods. • DMMT provides templates of main DM families, to simplify use by practicioners. • Extensive real environmental applications and 5 detailed case studies are shown. • Existing and future challenges in DM for Data Science are postulated. [ABSTRACT FROM AUTHOR]

SPMT ( Self-Propelled Modular Transporter) was employed to transport bulk precasted members, such as cylinders and cover slabs for marine works during the construction of No.2 berth, No.3 berth, and No.4 berth in Yingling Operation Area of Jingu port area in Qinzhou Port. Compared with the traditional air transportation, this method has advantages in saving the construction period, reducing the project cost, and protecting the construction safety, provides an effective guarantee for the smooth implementation of the project. This paper can provide references for transportation of other mass concrete members. [ABSTRACT FROM AUTHOR]

Sustainability assessments provide scientific support in decision procedures towards sustainable solutions. However, in order to contribute in identifying and choosing sustainable solutions, the sustainability assessment has to fit the decision context. Two complicating factors exist. First, different stakeholders tend to have different views on what a sustainability assessment should encompass. Second, a plethora of sustainability assessment methods exist, due to the multi-dimensional characteristic of the concept. Different methods provide other representations of sustainability. Based on a literature review, we present a protocol to facilitate method selection together with stakeholders. The protocol guides the exploration of i) the decision context, ii) the different views of stakeholders and iii) the selection of pertinent assessment methods. In addition, we present an online tool for method selection. This tool identifies assessment methods that meet the specifications obtained with the protocol, and currently contains characteristics of 30 sustainability assessment methods. The utility of the protocol and the tool are tested in a case study on the recovery of resources from domestic waste water. In several iterations, a combination of methods was selected, followed by execution of the selected sustainability assessment methods. The assessment results can be used in the first phase of the decision procedure that leads to a strategic choice for sustainable resource recovery from waste water in the Netherlands. [ABSTRACT FROM AUTHOR]