Adequate Set Research Articles

Optimal siting and sizing of battery energy storage systems in unbalanced distribution systems: A multi objective problem under uncertainty

In this paper the siting and sizing problem of battery energy storage systems in unbalanced active distribution systems is formulated as a mixed-integer, non-linear, constrained multi objective (MO) optimization problem under uncertainties. The problem is cumbersome from the computational point of view due to the presence of intertemporal constraints, a great number of state variables and the presence of uncertainties in the problem input data. A new approach based on the trade-off/risk analysis is proposed to obtain with acceptable computational efforts a solution that may not be the optimal solution but represents a reasonable and robust compromise. We use the trade-off/risk analysis, because it was specifically developed for power system planning problems in which we deal with a wide range of options, with possible conflicting objectives, and with uncertainty and risk. The proposed approach includes new procedures to select an adequate set of planning alternatives to be considered in the trade-off/risk analysis framework and to assist the planning engineer when difficulties arise in setting probabilities of the input data. Numerical applications to an IEEE unbalanced test system demonstrate the effectiveness of the proposed procedure and indicate the best alternatives of storage systems in the range from 450 kW to 600 kW globally installed in a reduced set of nodes.

Indonesian Fake News Classification Using Transfer Learning in CNN and LSTM

Fake news spreads quickly and is challenging to stop due to the ease of accessing and sharing information online. Deep learning techniques are a method that can be used to identify fake news quickly and accurately. The types of neural networks commonly utilized in deep learning architectures include Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), which can perform well when managing the task of classifying fake news, according to several pertinent studies. Regarding handling instances of Indonesian fake news classification, this study compares how well the CNN and LSTM models perform. However, given that Indonesian is a low-resource language with scant documentation, it is challenging to build an adequate data set. At the same time, the CNN and LSTM classification models require significant training data. We proposed a transfer learning method by combining two classification models with a pre-trained IndoBERT language model. 1340 news text data were used, including 643 actual news texts from CNN Indonesia, Liputan6, and Detik and 697 fake news texts from TurnBackHoax. As a result, the performance of the combination of the LSTM classification model with IndoBERT outperformed that of the CNN classification model with IndoBERT, which only produced an accuracy of 92.91%, down by 6%, and was able to produce an accuracy of up to 97.76%, an increase of 4.8% from before. Furthermore, the results show that the LSTM classification model outperforms the CNN classification model in capturing the representation created by IndoBERT. Additionally, these insights may serve as a basis for future research on identifying fake news in Indonesia, helping to improve methods for combatting misinformation in Indonesia.

Modelling mobility as a service: A literature review

MaaS (Mobility as a Service) is often presented as a means to solve some of the most pressing mobility issues such as car dependency and increasing vehicular greenhouse gas emissions. In this context, adequate models and simulations are key to estimating the impact of potential MaaS implementations and support decision-making to reap the benefits MaaS could bring. This article aims at reviewing MaaS representation in existing academic literature pertaining to transportation modelling, in order to assess its progress and identify trajectories for future improvements. We do so by considering mobility as an economic system in which MaaS interacts with both travel demand and supply. After identifying an adequate set of indicators to analyze MaaS models, we review 31 contributions and find out that most studies consist of demand-centered usage models while the integration of the system's supply side's operational and profitability factors is still lacking, especially in-MaaS financial flows.

Good-enough Care? How Patients' Perceptions of Counselors' Professional Skills Relate to Everyday Life in Forensic Long-stay Units.

The overall goal of long-term forensic care is to strive toward acceptable levels of adaptation and quality of life (QoL) of the forensic patient in the institutional context. While the bulk of the literature has focused on the deleterious consequences of personality pathology in this regard, research investigating the contribution of the quality of the therapeutic relationship has remained rather scant. Assuming that the perceived competence of the direct counselor, as perceived by patients, forms an important aspect in this regard, the central aim of this study was to investigate the relationship between patients' perceptions of their therapist's professional skills, their self-reported maladaptive behavior on the ward, and their experienced QoL. To this end, we recruited patients (N = 60) in long-stay forensic units and investigated their perceptions of 10 specific skills displayed by their therapist, along a "too little-too much" rating scale. The results revealed that patients who had the overall impression that their counselor was equipped with an adequate set of professional skills showed less maladaptive behavior and perceived a higher QoL on the ward. Conversely, at a more specific competence level, only a positive relationship between a counselor's predictability and self-reported QoL was found. Taken together, these results highlight that an overall professional skill evaluation matters in the context of forensic patients' adaptation and QoL in their long-stay units, with the counselor's predictability serving as a crucial aspect in obtaining the most favorable outcomes.

Higher prevalence of sacbrood virus in Apis mellifera (Hymenoptera: Apidae) colonies after pollinating highbush blueberries.

Highbush blueberry pollination depends on managed honey bees (Apis mellifera) L. for adequate fruit sets; however, beekeepers have raised concerns about the poor health of colonies after pollinating this crop. Postulated causes include agrochemical exposure, nutritional deficits, and interactions with parasites and pathogens, particularly Melisococcus plutonius [(ex. White) Bailey and Collins, Lactobacillales: Enterococcaceae], the causal agent of European foulbrood disease, but other pathogens could be involved. To broadly investigate common honey bee pathogens in relation to blueberry pollination, we sampled adult honey bees from colonies at time points corresponding to before (t1), during (t2), at the end (t3), and after (t4) highbush blueberry pollination in British Columbia, Canada, across 2 years (2020 and 2021). Nine viruses, as well as M. plutonius, Vairimorpha ceranae, and V. apis [Tokarev et al., Microsporidia: Nosematidae; formerly Nosema ceranae (Fries et al.) and N. apis (Zander)], were detected by PCR and compared among colonies located near and far from blueberry fields. We found a significant interactive effect of time and blueberry proximity on the multivariate pathogen community, mainly due to differences at t4 (corresponding to ~6 wk after the beginning of the pollination period). Post hoc comparisons of pathogens in near and far groups at t4 showed that detections of sacbrood virus (SBV), which was significantly higher in the near group, not M. plutonius, was the primary driver. Further research is needed to determine if the association of SBV with highbush blueberry pollination is contributing to the health decline that beekeepers observe after pollinating this crop.

NCI MATCH (EAY131) arm Z1D: Retrospective evaluation of attributions of adverse events experienced on nivolumab in patients with MSI-high tumors.

e14711 Background: Machine learning algorithms for prediction of Adverse Events (AEs) due to immune checkpoint inhibitors (ICIs) rely on clinically annotated data to serve as gold standards. Clinical trials are the best sources of such data, but audits to assess validity are rare, compromising future machine learning efforts. We reviewed AEs reported on the National Cancer Institute Molecular Analysis for Therapy Choice (NCI-MATCH) EAY-131 Arm Z1D. Patients matched to Arm Z1D due to MSI-high status were treated with nivolumab. Routine and serious AEs were reported to NCI. Attribution data from AEs on 29 patients with tumor genomics suitable for machine learning approaches were obtained and reviewed retrospectively. Methods: Documented AEs were sorted by grade via the Common Terminology for Adverse Events (CTCAE) system and categorized as related or unrelated to ICIs. Primary source data reported to NCI were reviewed. If any ambiguity was found, trial sites were queried to provide additional information and documentation. Decisions were made by an experienced NCI medical officer with the benefit of additional data and site responses. All serious AEs and a subset of ambiguous routine AEs were assessed to determine if attribution conformed to expected attribution per the nivolumab package insert. Uncertain attribution required additional queries, including email or phone correspondence. Grade 1 or 2 events were only evaluated to determine if any attribution to ICIs was possible. If a higher grade event was attributed to ICIs, there was no further query for lower grade events on the same patient. Results: A total of 639 Grade 1, 152 Grade 2, 75 Grade 3, 10 Grade 4, and 5 Grade 5 events were reported among the 29 patients. One Grade 5 event, 5 Grade 4 events, 13 Grade 3 Events, 9 Grade 2 events, and 6 Grade 1 events were considered less likely to be attributed to ICIs compared with the initial sponsor or site investigator assessment of possibly, probably, or definitely related. Two Grade 4 events, 1 Grade 2 event, and 8 Grade 1 events were considered to be possibly, probably, or definitely due to ICIs compared with the initial sponsor or site assessment of less likely or unrelated. Site responsiveness to retrospective queries from the sponsor varied. Additional lab values, primary source documentation, and communication with site investigators were necessary to make modifications. A total of 11 AEs required additional queries to site investigators. Conclusions: Retrospective reviews of real-time site and sponsor assessments of attribution exhibit significant modification of initial attribution to ICIs in this review of 29 patients on Arm Z1D of the NCI MATCH trial. Machine learning approaches for prediction of AEs on patients treated with ICIs may require careful assessments of primary source data to develop adequate training sets, given the paucity of gold standard data available.

Impact of pollination schedules on the fruit characteristics of kiwifruit

Timings of pollination are crucial for adequate fruit set, development, quality and yield of kiwifruit. A study was undertaken for three years (2020-22) to find out the effective pollination period (EPP) of commercial kiwifruit cultivars in Sikkim conditions. Ten-year-old vines of the female cultivar Monty and the male cultivar Allison were taken for the study. It was found that 8 am is the most important time to perform pollination for obtaining bigger-sized fruits. Fruit set was found to be 100% when pollination was performed at 9 am, 10 am and 3 pm. The yield was found to be the highest at 9 am (35.20 kg/plant) pollination time; TSS recorded 1.38-fold variations with the maximum at 8 am (19.1°B). Total acidity was recorded as the minimum at 9 am (0.64%) pollination. Further, ascorbic acid was recorded at to be the maximum at 9 am (118.9 mg/100g) pollination. Primary and secondary nutrients were also found to be higher in pollinated flowers from 8.00 am to 12.00 pm. Thus, efforts for producing good quality and marketable size fruits in kiwifruit the vines should the flowers should be within the 8.00 am to 12.00 pm pollination timings.

Technical note: Bimodal parameterizations of in situ ice cloud particle size distributions

Abstract. The cloud particle size distribution (PSD) is a key parameter for the retrieval of microphysical and optical properties from remote-sensing instruments, which in turn are necessary for determining the radiative effect of clouds. Current representations of PSDs for ice clouds rely on parameterizations that were largely based on aircraft in situ measurements where the distribution of small ice crystals were uncertain. This makes current parameterizations deficient to simulate remote-sensing observations sensitive to small ice, such as from lidar and thermal infrared instruments. In this study we fit the in situ PSDs of ice crystals from the JULIA (JÜLich In situ Aircraft data set) database, which consists of 11 campaigns covering the tropics, midlatitudes and the Arctic, consistently processed and considered more robust in their measurements of small ice. For the fitting, we implement an established approach to PSD parameterizations, which consists of finding an adequate set of parameters for a modified gamma function after normalization of both PSD axes. These parameters are constrained to match in situ measurements when predicting microphysical properties from the PSDs, via a cost function minimization method. We selected the ice water content and the ice crystal number concentration, which are currently key parameters for modern satellite retrievals and model microphysics schemes. We found that a bimodal parameterization yields better results than a monomodal one. The bimodal parameterization has a lower spread for almost all ice crystal sizes over the entire range of analyzed temperatures and fits better the observations, especially for particles between 20 and about 110 µm at temperatures between −60 and −20 ∘C. For this temperature range, the root mean square error for the retrieved Nice is reduced from 0.36 to 0.20. This demonstrates a clear advantage to considering the bimodality of PSDs, e.g., for satellite retrievals.

Legal Aspects of ESG Development in the Field of Poland’s Raw Material Policy Using the Example of the Geological and Mining Sector

The challenges connected with energy transformation, and in particular the raw materials policy, require due care for the functioning of the sector in question, in compliance with the applicable laws. The creation of an adequate set of legal instruments, including the ESG reporting obligation, will foster the responsible development of the raw materials security sector, which will also contribute to a sustainable regional development model. The article analyzes the raw materials policy within the sustainable regional development system and legal regulations governing ESG reporting.

A roadmap to define and select aquatic biological traits at different scales of analysis

Trait-based approaches are a powerful tool, as they not only improve understanding of ecological complexity and functioning but also allow comparison across different ecosystems and biogeographical regions. They may be used to unveil ecosystem processes and assess community structures, but their great potential becomes limited when dealing with scattered trait data and historically unstandardised trait nomenclature. The lack of standardisation allows authors to use the terminology of their preference, which inevitably leads to ambiguous misunderstandings and limits comparison between different studies. There have been some attempts to organise the trait vocabulary, but even these are mostly created from the perspective of a single ecosystem, which limits their applicability. In this work, we conducted a systematic literature review that identified and compiled 1127 traits across 37 datasets of fishes, invertebrates and zooplankton from freshwater, marine and transitional ecosystems. This dataset was then used to build on the Marine Species Traits Wiki and to propose a new, unified approach to a trait vocabulary based directly on readily available trait data. We propose a single standardised designation for all the different traits identified and provide a list of all the different synonyms commonly used for these traits. A roadmap to help the trait selection process is also provided, offering a guide through four main steps and important questions for choosing an adequate set of traits at the beginning of any study, which constitutes one of the main challenges in functional ecology research. Overall, this proposal will provide a solid baseline for tackling gaps in trait nomenclature and ensuring a clearer future for functional ecology studies.

Toward Symptom Assessment Guided Symptom Investigation and Disease Diagnosis

Automatic Disease Diagnosis (ADD) has gained immense popularity and demand over the past few years, and it is emerging as an effective diagnostic assistant to doctors. Diagnosis assistants assist clinicians in conducting a thorough symptom investigation and identifying possible diseases. Doctors correctly diagnose patients by observing only a few symptoms in most cases, even though the diagnosed disease has numerous symptoms. Also, some common symptoms, such as fever and headache, usually emerge due to other symptoms, which do not play a major role in identifying suffering diseases. In this work, we investigate the role of symptom importance in disease diagnosis through several feature engineering techniques and propose a novel symptom assessment incorporated symptom investigation and disease diagnosis (SA-SIDD) assistant using hierarchical reinforcement learning. The proposed SA-SIDD assistant first collects an adequate set of symptoms/sign information through conversing with users and then diagnoses a disease based on the extracted symptoms. We incorporated a symptom assessment module with the diagnosis framework that evaluates the relevance of current inspected symptom at each turn and reinforces the assistant to investigate distinctive and context-aligned symptoms using an assessment critic. The proposed methodology outperforms the state-of-the-art method, HRL, on two publicly available datasets, which firmly establishes the crucial role of symptom importance in disease diagnosis and the need for the proposed symptom assessment incorporated disease diagnosis framework. Furthermore, we have also conducted a human evaluation, revealing that the diagnosis method greatly enhances end-user satisfaction because of context-aligned relevant and minimal symptom investigation <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>¹</label> The code and data are available at <uri>https://github.com/NLP-RL/SA-SIDD</uri> </fn> .

Identifying the influence of training policies on employee performance in the county government of Nyandarua

This study aimed to examine the relationship between training policies and the performance of employees in the County Government of Nyandarua. The study used a descriptive survey research design. The 66 county administrators at Nyandarua County Government were the survey's main target. The main tool for gathering data was a questionnaire. The metrics of central tendency and dispersion, the absolute and relative frequencies, and percentages of each were used in descriptive statistics of mean and standard deviation, respectively. The data was shown using frequency tables and graphs for simple comparison. Training policies and employee performance showed a coefficient of regression ????1 = 0.319, p-value = 0.022&lt; 0.05. This was clearly manifested in that training lowers staff turnover, absenteeism, dissatisfaction and discontent, as well as boosting morale and increasing productivity. This was shown by workers' ability to realise they all have an equal chance of succeeding; it is easier for them to give their all at work. In conclusion, through training, organisations can attain management development, boosting managerial capability, stimulating and helping retain competent managers and encouraging managers to pursue professional advancement. The study recommends that the county government should ensure they establish sound training policies so as to equip the employees with the adequate skill set and capacity to deliver quality services to the citizens.

Nonlinear radiation effects on water-based nanofluid containing CNTs subject to heat source/ sink past a wedge

In this paper, a two-dimensional and incompressible laminar flow comprised of water-based carbon nanotubes over convectively heated moving wedge under the magnetic field and nonlinear radiation and heat production/ absorption is investigated. The base nanofluid (water) contains single wall carbon nanotubes (SWCNTs) and multiple walls carbon nanotubes (MWCNTs). In order to convert the dimensional nonlinear partial differential equations in nondimensional nonlinear ordinary differential form, an adequate set of similarity variables had been used. These set of equations and boundary conditions are evaluated by the implementation of RKF-45 (Runge–Kutta–Fehlberg fourth-fifth) order scheme. The influence of several physical parameters on particular nanoparticle’s volume friction, temperature and velocity ratio parameter, heat source/ sink parameter, nonlinear radiative constraint, exponent constant, magnetic factor, Eckert and Biot numbers is studied. An opposite behavior of volume fraction and velocity ratio parameters on velocity and energy profiles is achieved.

Mobile Banking Service Quality and Customer Retention among Commercial Banks’ Customers: An Empirical Evidence from Southeast Nigeria

In the era of a cashless economy, customers now emphasize banking services that are convenient, reliable and responsive. Mobile banking (m-banking) is among the latest in a series of recent mobile technological wonders. This research is aimed at investigating the effect of mobile banking service quality on customer retention among commercial banks customers in Southeast, Nigeria. The study examined the effect of reliability, responsiveness, information quality, privacy issues and system quality on customer retention. The study used an explanatory research design method using quantitative approach. The sample, consisting of 400 respondents was drawn from a cross-section of commercial banks customers with mobile banking access in Southeast Nigeria. From the 400 copies of questionnaire distributed 395 were returned valid and useful for the final analysis study used Pearson’s Correlation Coefficient to measure the relationship between mobile banking service quality and customer retention. Then regression analysis was used to estimate the cause-and-effect relationship between the variables of the mobile banking service quality and customer retention. The result showed that a positive effect for responsiveness, systems quality, and information quality on customer retention while privacy and reliability of mobile banking service quality was not significant. Among all, responsiveness, system quality and information quality have a significant effect on customer retention while reliability and privacy issues do not affect customer retention in mobile services. Therefore, to increase customer retention, the bank management should implement programs that support awareness creation, customer satisfaction, brand image and reputation by continuously assessing the service quality and delivery of mobile banking services provided to their customers, and also provide an adequate set of security components of mobile banking that protects customer’s bank details from fraud.

Application and Analysis of Machine Learning Algorithms on Pima and Early Diabetes Datasets for Diabetes Prediction

Diabetes is a chronic condition that strike how your body burns food for energy. Much of the food you consume is converted by your body into sugar (glucose), which is then released into your bloodstream. Your pancreas releases insulin when your blood sugar levels rise. Over the years, several scholars have sought to create reliable diabetes prediction models. Due to a lack of adequate data sets and prediction techniques, this discipline still faces many unsolved research issues, which forces researchers to apply big data analytics and ML-based methodology. Four distinct machine learning algorithms are used in the study to analyze healthcare prediction analytics and solve the issues. In this investigation, the Pima and Early detection datasets were employed. We applied the Decision Tree, MLP, Naive Bayes, and Random Forest algorithms to these datasets and evaluated the accuracy and F-Measure. The goal of this research is to develop a system that could more precisely predict a patient's risk of developing diabetes.

A novel sparse polynomial chaos expansion technique with high adaptiveness for surrogate modelling

In this paper, a novel adaptive modelling framework for sparse polynomial chaos expansion is proposed, which can automatically determine adequate truncation degree and training sample set simultaneously. Moreover, the curse of dimensionality issue in polynomial chaos expansion, which generally arises in dealing with high input dimension or large truncation degree, can be alleviated to a large extent. In this framework, a new basis selection strategy, which leverages basis expansion, pruning and refinement, is pursued to adaptively select the polynomial terms of proper degree during the modelling process. Besides, an outstanding sequential sampling strategy is adopted to collect samples of high quality and in relatively small quantity for training polynomial chaos expansion model, and a sparse representation method, Bayesian compressive sensing, is employed for regression calculation. To reconcile the sequential sampling and adaptive basis selection in a consistent framework, a stability evaluation process which works in parallel with the sequential sampling process is performed. The performance of the proposed adaptive modelling framework is evaluated on two benchmark functions and a physical model through comparison with an existing adaptive polynomial chaos expansion modelling technique, a sequential sampling-only approach, and several basis adaptivity strategies. Results demonstrate that the proposed method has high adaptiveness in building surrogate models for various problems. It outperforms the existing adaptive polynomial chaos expansion modelling technique in terms of modelling precision and convergence rate, and it has similar performance with the sequential sampling-only method while lessening the burden in regression calculation and enabling adaptive determination of the truncation degree. Limitations of the proposed method are also summarized after comparing it with various basis adaptivity strategies.

Integrated foreign languages teaching: development of learning outcomes for IT majors

Introduction. Design of descriptors for achieving objectives of foreign language education in case of non-language majors at tertiary level on the basis of the existing scales of the Common European Competences in Foreign Language (CEFR) promotes the policy of cultural diversity and multilingualism implemented by the Council of Europe. The motivation for the research lies in the fact that Russian universities lack a clear and transparent system of criteria that can be used for planning and assessing foreign language competences for non-language majors. The paper presents a developed scale of descriptors for planning learning outcomes and assessing language proficiency of undergraduate students majoring in information technologies in the context of Russian education at tertiary level. With regard to federal state educational standards for higher education, foreign language competence is described as integrative foreign language professional communicative competence which combines professional and linguistic constituents. Materials and methods. The exploration adopted combined methods of analysis of previous studies, CEFR descriptors, interviews with master and bachelor students of IT majors (Togliatti State University, Russia), and foreign language communicative needs analysis in the IT industry. The obtained data were then processed in the backward design of targeted learning outcomes following the Common European Framework of Reference for Languages format. Research results. The study elicited cross-cultural communicative needs in the industry in question, and then there were selected appropriate CEFR descriptors for their subsequent modification. The choice of the CEFR format was justified by its high adaptability and the author’s previous successful experience of elaborating such scale descriptors for automotive engineering majors. The author designed a scale for A2, B1 and B2 levels, where learning outcomes are presented as descriptors of communicative language activities with CEFR level distinctions being combined with activities involved in on-the-job cross-cultural communication. Conclusion. The initiated research should be helpful to investigate ways of devising and testing adequate sets of CEFR-based descriptors to improve the quality of university syllabi. The proposed scale of descriptors has certain limitations, therefore, further exploration directions are outlined. Keywords: integrative foreign language professional communicative competence, learning outcomes, CEFR-based descriptors, assessment, IT majors

SYSTEMATIC LITERATURE REVIEW OF IOT METRICS

The Internet of Things (IoT) touches almost every aspect of modern society and has changed the way people live, work, travel and, do business. Because of its importance, it is essential to ensure that an IoT system is performing well, as desired and expected, and that this can be assessed and managed with an adequate set of IoT performance metrics. The aim of this study was to systematically inventory and classifies recent studies that have investigated IoT metrics. We conducted a literature review based on studies published between January 2010 and December 2021 using a set of five research questions (RQs) on the current knowledge bases for IoT metrics. A total of 158 IoT metrics were identified and classified into 12 categories according to the different parts and aspects of an IoT system. To cover the overall performance of an IoT system, the 12 categories were organized into an ontology. The findings results show that the category of network metrics was the most discussed in 43% of the studies and, with the highest number of metrics at 37%. This study can provide guidelines for researchers and practitioners in selecting metrics for IoT systems and valuable insights into areas for improvement and optimization.&#x0D;

User-relevant factors influencing the prosthesis use of persons with a transfemoral amputation or knee-disarticulation: A meta-synthesis of qualitative literature and focus group results.

Persons with a transfemoral amputation or knee-disarticulation are heavily reliant on an adequate set of components for their prosthesis. To improve the process of adjusting the specific prosthetic properties to the expectations of the prosthesis users, it is of importance to first identify which factors have an influence on prosthesis use. Therefore, we aimed to identify factors that influence prosthesis use in adults with a transfemoral amputation or knee-disarticulation. A qualitative meta-synthesis was conducted by searching five databases (last update January 20th 2022). Studies were considered eligible if they contained qualitative data about adult persons with a transfemoral amputation or knee-disarticulation with experience in using a prosthesis and focused on the users' opinions. All eligible studies were independently screened by two reviewers. The results sections of the included studies were entered in Atlas.ti software (v8) and coded using the framework approach. The quality of the included studies was assessed using the Critical Appraisal Skills Program (CASP) qualitative research checklist. Results of the meta-synthesis were validated with prosthesis users (n = 8) in a focus group. Out of 5757 articles, 14 studies were included. An overview of seven themes ('prosthesis related'; 'rehabilitation, costs and prosthetist'; 'mental'; 'physical'; 'social'; 'activities and participation' and 'walking') containing 84 factors was created. Ten factors were added during the focus group, resulting in an overview of 94 factors that may influence the prosthesis use of lower-limb prosthesis users. Participants would like more user-involvement from the rehabilitation team. The development of a patient decision aid could help this process in the future. The large number of factors demonstrates that there is a great variety between prosthesis users and the factors that influence their prosthesis use. Therefore, it is important to take individual preferences into account for the selection of a new prosthesis.

A local perspective of the socio-environmental vulnerability to environmental pollution and economic crises: a case of locals around a coal power plant in Sri Lanka.

People are vulnerable to increasing environmental pollution and unprecedented economic changes in countries like Sri Lanka. Development projects such as coal power plants have exaggerated the vulnerability of the communities to the threats of environmental pollution coupled with economic crises. This study is to present the concurrent socio-environmental issues related to the largest power plant in Sri Lanka-the Norochcholai coal power plant (NCPP). The vulnerability of the communities to environmental pollution due to the coal power plant and the prevailing economic crisis was studied. Results revealed that communities are extremely exposed to threats and are highly sensitive to poverty and yet they have no adequate sets of strategies to cope and/or adapt to threats or increase their resilience. This study suggests a human-centric approach focused on sustainable and autonomous adaptation strategies for the communities in the vicinity of the NCPP and to address their rising vulnerability to the impacts for both the NCPP and the prevailing economic crisis.