Explicit Indicator Research Articles

High-resolution mapping of tree species and associated uncertainty by combining aerial remote sensing data and convolutional neural networks ensemble

Mapping tree species diversity is essential for monitoring and managing forest ecosystems. Automating ecoforestry mapping using remote sensing images remains an important challenge due to the tremendous variability in forest covers and the conditions under which images used for classification are acquired. Deep learning algorithms have been increasingly used over the last few years to analyse remote sensing data for mapping tree species. However, most of these studies focus only on a small number of species or a limited area and avoid providing a spatially explicit representation of the uncertainty related to the predictions, rendering them unsuitable for operational use.In this study, we used an ensemble of convolutional neural networks to map forest species and land cover types across a 10,000 km2 area in Quebec, Canada, spanning mixed and boreal forests. We built a georeferenced label database to train and test nine models, which resulted from the combination of three training datasets and three-commonly used convolutional neural network architectures (VGG16, ResNet50v2, Densenet121). These models were trained on multiband aerial photographs and a canopy height model derived from airborne lidar point clouds and used to map the diversity and distribution of tree species and land cover types. The level of agreement among models was used to generate uncertainty maps. The performance of the super-ensemble using 1311 independent forest inventory plots and assessed the extent to which uncertainty maps could serve as a spatially explicit indicator of model performance.The super-ensemble achieved 90% global accuracy. Our results indicated that the performance of the super-ensemble surpassed that of all individual architectures while also showing a positive effect of the canopy height model on performance. The comparison of the super-ensemble map with the proportion of basal area measured in forest inventory plots confirmed the reliability of the model over the study area. Our results also indicated that mapping the inter-model agreement provides a reliable spatially explicit estimate of model performance. The robustness and reliability of the proposed approach support its use in an operational context while also providing a conceptual framework to evaluate the reliability of uncertainty maps.

Valuation and perception of the costs of climate change on health.

Climate change affects our societies and lives through our economies, our livelihoods, and our health. Economic losses of climate change are estimated at $23 trillion, largely through externalities due to premature mortality, healthcare expenditure, and health-related work losses. Even if there are established methods to quantify the health economic burden, there is limited information on how people perceive this information. The current study aimed to examine different health cost evaluation methods and observe perceptions of stakeholders in the climate change context. The participatory research approach of the World Café with 41 participants was applied to explore four topics associated with valuing the costs of climate change. The data were analyzed following an inductive approach. Despite the willingness-to-pay approach being widely applied, many experts see actual healthcare costs as a more explicit indicator of costs; however, this approach might underestimate actual costs. Participants experienced difficulties accepting and understanding cost estimates that indicated very high externalities as a percentage of gross domestic product. The cost-effectiveness of mitigation and adaptation measures was also challenged by a concern that while the costs of such measures are incurred now, the benefits do not come to fruition until later, for example, when building bike lanes or dams. Policies should favor environmentally friendly activities such as making cycling more convenient in cities with the health benefits presented in monetary terms, while limiting car driving. Moreover, the public might better understand the costs of climate change via tools that map how solutions influence different sectors and outlining the costs in evaluating the benefits for health and the environment.

Household fuel and direct carbon emission disparity in rural China

Universal access to clean fuels in household use is one explicit indicator of sustainable development while currently still billions of people rely on solid fuels for daily cooking. Despite of the recognized clean transition trend in general, disparities in household energy mix in different activities (e.g. cooking and heating) and historical trends remain to be elucidated. In this study, we revealed the historical changing trend of the disparity in household cooking and heating activities and associated carbon emissions in rural China. The study found that the poor had higher total direct energy consumption but used less modern energy, especially in cooking activities, in which the poor consumed 60 % more energy than the rich. The disparity in modern household energy use decreased over time, but conversely the disparity in total residential energy consumption increased due to the different energy elasticities as income increases. Though per-capita household CO2 and Black Carbon (BC) emissions were decreasing under switching to modern energies, the disparity in household CO2 and BC deepened over time, and the low-income groups emitted ∼ 10 kg CO2 more compared to the high-income population. Relying solely on spontaneous clean cooking transition had limited impacts in reducing disparities in household energy and carbon emissions, whereas improving access to modern energy had substantial potential to reduce energy consumption and carbon emissions and its disparity. Differentiated energy-related policies to promote high-efficiency modern heating energies affordable for the low-income population should be developed to reduce the disparity, and consequently benefit human health and climate change equally.

Climate change causes spatial shifts in the productivity of agricultural long-term field experiments

Long-term field experiments (LTE) are highly valuable infrastructures in agricultural- and soil sciences for understanding the long-term impacts of climate and management practices. While they are designed to run under constant conditions, climate change is expected to affect site conditions considerably. This needs to be quantified when interpreting experimental results and when redesigning the experimental setup. One way to achieve this is by utilizing vegetation growth and carbon dynamics, specifically the Net Primary Productivity (NPP), as a spatially explicit indicator. NPP facilitates the assessment and interpretation of yield performance in LTEs under future climatic conditions. Our study estimated the changes in NPP for 271 LTE sites in Germany, comparing a baseline (2000–2020) with two scenarios (2081–2100) that were based on the Shared Socioeconomic Pathways (SSPs) (SSP245) and SSP585) by the Intergovernmental Panel on Climate Change (IPCC). We used the NASA-CASA biogeochemical model to calculate NPP in baseline and IPCC scenarios using Germany as a test case. LTEs were grouped by land use (crop types) and soil information (soil type, texture), drawing on the geodata infrastructure “BonaRes Repository”. The total annual terrestrial NPP for the baseline was calculated as 202.4 Mt C (sum of forests, grasslands, and arable lands) in Germany, while total NPP was up to 56.0 Mt C for different land use types. For both scenarios, NPP was projected to increase in LTEs located in southern Germany, indicating increased crop productivity, while a decrease was projected for the central Germany. The decrease in NPP of numerous LTEs in central Germany was estimated to extend to the LTEs in the eastern part corresponding to the worst-case scenario SSP585. Explicitly, the use of the multi-model ensemble mean as the climate driver in modelling may overestimate projected NPP by reducing inter-annual variability, highlighting the importance of methodological choices for accurate future projections. Besides, the results indicated that poor soils are projected to experience a further decline in productivity, primarily attributed to escalating water scarcity. Conversely, soils with high quality are likely to witness enhanced productivity, largely driven by the extension of the growing seasons. The outcomes of this study provide a basis for considering the future conditions of German LTEs and facilitate distinguishing between the effects of climate change and the impact of agricultural management on productivity at the regional level. These outputs enable planning and developing research strategies for selecting future LTE sites and redesigning existing or newly planned experiments. Moreover, the integrated modelling framework presented here highlights the potential of LTE data for large-scale modelling studies of ecosystem functions.

261 Perceptions of and attitudes towards problematic polypharmacy and prescribing cascades in older adults: a stakeholder analysis

Abstract Background Research to date on appropriate prescribing in older adults has focused on characterising inappropriate prescribing using explicit prescribing indicator tools. Less is known about other causes of problematic polypharmacy such as prescribing cascades. Prescribing cascades occur when a medication is prescribed to treat or to prevent the symptoms of an adverse drug reaction and may be characterised as intentional or unintentional, as well as appropriate or inappropriate. Prescribing cascades, therefore, represent an important and under-researched area of problematic polypharmacy. The aim of this study was to conduct a stakeholder analysis (patients with polypharmacy; carers; General Practitioners [GPs]; hospital doctors; community pharmacists; professional organisation representatives and policymakers) to elicit perceptions of and attitudes towards problematic polypharmacy, with a focus on prescribing cascades. Methods Qualitative one-to-one semi-structured interviews were conducted. Participants were recruited via a gatekeeper approach. Interview duration ranged from 58 to 80 minutes. Interviews were analysed thematically. Results Thirty-one participants (six patients, two carers, seven GPs, eight community pharmacists, four hospital doctors, two professional organisation representatives, and two policy makers) were interviewed. The challenges of communication at the interface of primary and secondary care were outlined, including the lack of clear ownership of drug monitoring. The community pharmacist and, at times, the patient were relied upon to fill in these gaps in communication. Patients discussed the burden of taking multiple medications. GPs described the importance of ‘mindful prescribing’ to avoid, where possible, unintentional prescribing cascades. The need for further education for health care professionals and patients on prescribing cascades was highlighted, as was the importance of improved collaboration between health care professionals. Conclusion Understanding the perceptions of different stakeholders towards problematic polypharmacy and prescribing cascades is key to the development of effective tools or resources to improve patient care.

Spatiotemporal Evolution and Influencing Factors of Social-Ecological System Vulnerability in the Guangdong–Hong Kong–Macau Greater Bay Area

Catalyzed by global change and human activities, social and ecosystems are constantly under increasingly dynamic transformations. The Guangdong–Hong Kong–Macau Greater Bay Area (GBA), bordering the South China Sea and located in the Pearl River Delta Plain, is a typical region of complex SESs with rapid socioeconomic development but severe ecosystem degradation. Therefore, based on the relevant data of 11 GBA cities from 2010–2020, this paper constructs an indicator system for assessing land–sea SESs by extracting from three aspects: exposure, sensitivity, and adaptive capacity. Through the construction of a vulnerability assessment indicator system, via the explicit spatial vulnerability indicator calculation model, and vulnerability factor diagnostic model, this study comprehensively analyzes vulnerability levels, spatiotemporal evolution, and SES vulnerability factors. The study found that, since 2010, the SES vulnerability of the GBA has shown an overall trend of alleviation, and the overall geographical distribution of classified vulnerability levels is rather concentrated, with cities around the Pearl River Estuary relatively less vulnerable. The augmentation of per-capita fiscal expenditure, per-capita gross regional product, and decent air quality rate are the critical ingredients to remedy the vulnerability in the GBA.

Instance-Aware Spatial-Frequency Feature Fusion Detector for Oriented Object Detection in Remote-Sensing Images

In recent years, fusing multi-type features poses great potential for oriented object detection (OOD) in remote sensing images (RSIs). Due to the inexplicit operation of modeling orientation variations, convolutional neural networks (CNNs) are difficult to perceive objects under different transformations (angles and scales). In this paper, we propose a novel instance-aware spatial-frequency feature fusion detector (SFFD) for oriented object detection in remote sensing images. First, a layer-wise frequency-domain analysis (L-FDA) module is built along with CNN layers to extract frequency features. Getting rid of the constrains such as horizontal rectangular kernel in CNNs, our L-FDA possesses outstanding ability of locating mutational signals from frequency space. These mutational signals record the scale and angle information of the oriented instances in images. Subsequently, CNN and frequency features are sent into RoI Pooling layer to obtain multi-type instance-level RoI features. Moreover, the proposed instance-aware cross feature fusion (CFF) module explores the interaction between these diverse features which provides an explicit indicator to compensate the orientation information ignored by instance-level CNN features. Finally, our SFFD unifies the proposed L-FDA module and CFF module into the detection network to localize oriented instances in RSIs. We compare our method with many state-of-the-art methods on DOTA, HRSC2016, and NWPU VHR-10 datasets. Experimental results verify the validity of modeling instance-level object relations from frequency-domain and CNNs for OOD.

Future meaning in Vietnamese and English: Similarities and Differences

The paper describes in detail and employs one table and six discussions to compare expressions of future meaning in Vietnamese and English declaratives to find out their differences and similarities. The findings are the basis for giving some advice to learners of Vietnamese and English. Vietnamese neither distinguishes nor employs grammatical means to express various shades of future meaning. A lexical means like sẽ, định, tính, or sắp, or a combination of two or more like định sẽ, tính sẽ, or dự tính sẽ, does. The lexical means may be omitted when an adverbial of future time like sáng mai, meaning tomorrow morning, occurs. At first glance, Vietnamese learners face difficulty because one expression in their mother tongue separates into two or more in English, resulting in unnecessary differentiation; conversely, native speakers of English seem more enjoyable noticing that two or more expressions in their mother tongue merge into one in Vietnamese. However, to understand Vietnamese sentences, foreigners must depend more on contextual cues than when they process English sentences. This is uneasy for the native English speakers, who are accustomed to using a language in which all the modal meanings have signs, either lexical or grammatical or both, with an explicit indicator in the structure of the nuclear predication of the declaratives.

THE FUNCTION OF RECOMMENDATION OF A PHARMACISTS IN REDUCE PIMs (POTENTIALLY INAPPROPRIATE MEDICATIONS) OF GERIATRIC PATIENTS AT THE GENERAL HOSPITAL DR. MOEWARDI SURAKARTA


 The prevalence of potentially inappropriate medications (PIMs) is quite high in geriatric patients. PIMs can be detected using an explicit prescription indicator (a criterion). Explicit criteria that can be used in the identification of PIMs in geriatric patients include: BEERS Criteria, STOPP Criteria, and START Criteria. This study aims to analyze drugs included in the PIMs category based on BEERS Criteria, STOPP Criteria, and START Criteria. This research is a semi-experimental study (intervening in the form of giving recommendations) with prospective data collection in the geriatric ward of Regional General Hospital Dr. Moewardi Surakarta in the period from October 2019 to January 2020. The results showed that out of 70 patients, there were 47 PIMs, PIMs based on BEERS Criteria for 20 patients (28.6%), PIMs based on STOPP Criteria for 10 patients (14.3%) and PIMs based on START Criteria for 17 patients (24.3%). Bivariate analysis showed that there was a relationship between the incidence of adverse drug events (ADEs), with recommendations that were followed up on PIMs based on BEERS Criteria with an RR value of 25 (95% CI 1,2: 520,7), P = 0.04 and PIMs based on START Criteria with an RR value of 5 (95% CI 1: 35.9), P = 0.04, whereas the results of the bivariate analysis on PIMs based on STOPP Criteria showed that there was no relationship between the incidence of ADEs with the recommendations that were followed up with a RR value of 4 (95% CI 0.13: 119.2), P = 0.50. Based on the results of univariate and bivariate statistical analysis, it can be concluded that there are still many incidents of PIMs based on BEERs Criteria, STOPP Criteria, and START Criteria and pharmacists have an important function to reduce PIMs so that the incidence of ESO due to PIMs can be minimized in the regional general hospital Dr. Moewardi Surakarta.
 
 
 
 
 
 
 

Parental education's role in child survival

The end of the Millennium Development Goals era marked progress in global under-5 mortality, but this was largely inequitable.1 Since the introduction of the Sustainable Development Goals (SDGs) much evidence has indicated the necessity of an integrative and intersectoral approach to the global health agenda.2–4 In recognition of this, education has been considered a core social determinant of health, with SDG 4 focusing on ensuring inclusive and equitable quality education and promoting lifelong learning opportunities for all, with an explicit indicator on better education for girls.

Continental assessment of South American marsupial conservation priorities: A methodological approach using a spatially explicit conservation indicator

The distributional range of South American marsupials is the response to evolutionary history, geology and climate, resulting in a chorology characteristic of each taxon. We integrated information for each species using the global conservation status given by the International Union for Conservation of Nature (IUCN), plus the taxonomic singularity, and the chorotype using an explicit conservation indicator: the pixel conservation value (PCV). We generated a map of South America with 0.5° × 0.5° grids, where we ranked areas with different conservation values (PCVs), analyzed their overlap with protected areas and human pressure, and tested their use with a recent disturbance (fires during August 2019) at a continental scale. The highest PCVs were found in the Andean region from Perú to northern Argentina, and in Brazil between the Paranaense Forest and the Cerrado ecoregions. Only 21.5% of the total PCV area was protected, with 60% to 0% of PCV areas protected. When human pressure and conservation scenarios were analyzed, 99.9% of all pixels are under some kind of pressure, with 33.3% and 66.7% under high and low pressure, respectively. We present comparative information between areas, and show examples of the usefulness of this indicator to quantitatively evaluate the impact of environmental disturbances on their conservative value.

Information: stress or lifestyle

Communication is a process of information exchange between stakeholders, so it is not surprising that the life of each individual changes with time under the influence of information. Information, as part of our habit, enables the individual to be more agile in both everyday living and business sphere. One of the significant impacts on the process of receiving and processing information is the appearance of the Internet. The aim of this paper is to show that it is the Internet that has increased the flow of information that a person can absorb and that the internet affects the changes in society. Furthermore, the paper suggests that information overload is increasing at work and out of work, which is reflected and influences everyday life. Due to the flood of information, there is also a sense of information fatigue, which represents a syndrome, i.e. apathy, indifference and mental exhaustion resulting from exposure to too much information. /1/ Based on the research on a sample of 164 respondents, this paper confirmed hypothesis that information anxiety differs with age. Since the internet occupies an increasingly important role among its users, features of application are indicated. An explicit indicator is the synergy of the internet usage and possessing knowledge regarding ICT application.

RETRACTED: Research on vehicle license plate recognition technology based on deep convolutional neural networks

Convolutional Neural Networks (CNN) are used for visual type recognition. This is a first-order learning method. Types of Deep Neural Networks (DNNs) training to improve vehicle identification algorithms routinely. Symptoms are difficult to diagnose accurately. Recognition is done approximately in harsh environments. The recognition accuracy of Convolutional Neural Networks (CNN) is relatively high, which requires a large number of samples. A Vehicle Logo is the most explicit vehicle manufacturer indicator and is based on the Vehicle Logo Recognition (VMR) system. Mark recognition can still be a challenge because it is difficult to pinpoint the vehicle's identity in the image and seek stability in different imaging situations simultaneously. Vehicle Manufacturer Recognition (VMR) is proposed to eliminate the requirements for accurate identification and analysis of a Convolutional Neural Network (CNN) system. The lining is used based on information obtained from a CNN source. To train the neural network use the question expansion strategy to increase the training set of the synthetic transformation and to increase the rate at which the recall. This method can be maintained with high accuracy under light changes and noise conditions and is not adaptable to the environment. Experimental results show that data sets, which show that the algorithm is capable of strong generalizations, do not have high classification accuracy. The class's average accuracy will be higher than other methods; to show that CNN is better at recognizing vehicle identification with the results of comparative tests.

Integrated analysis of hypoxia-associated lncRNA signature to predict prognosis and immune microenvironment of lung adenocarcinoma patients

ABSTRACT Lung adenocarcinoma (LUAD) represents the main lung cancer (LC) subtype that possesses a disappointing clinical outcome over the decades. Tumor hypoxia is closely bound up with dismal survival for malignant tumor cases. We identified hypoxia-associated long non-coding RNA (lncRNA) signature to be an explicit indicator for predicting prognosis. The present work acquired RNA-seq and associated clinical data from The Cancer Genome Atlas (TCGA) database. Consensus cluster analysis characterized the hypoxia status of LUAD patients. Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) method determined significantly prognosis-related lncRNAs which were used to create a prognostic model. Diverse statistical approaches like the Kaplan-Meier curve, receiver operating characteristic (ROC) curve, and nomogram were adopted to verify the accuracy of the risk score. The potential immune environment landscape was unearthed by the CIBERSORT algorithm. Three hypoxia-related clusters were determined and 221 differentially expressed hypoxia-related lncRNAs were screened out. We developed a new predictive model based on seven lncRNAs (LINC00941, AC022784.1, AC079949.2, LINC00707, AL161431.1, AC010980.2 and AC090001.1). Kaplan-Meier curves and ROC plots uncovered the reliable predictive power of the risk score model. In addition, the immunosuppressive landscape was presented in the high-risk group by immune cell infiltration analysis. The seven hypoxia lncRNAs survival signature in our article are robust, accurate tools for predicting overall survival in LUAD patients.

Pinyin Spelling Promotes Reading Abilities of Adolescents Learning Chinese as a Foreign Language: Evidence From Mediation Models.

Pinyin is a phonological encoding system used to spell modern Chinese Mandarin due to the phonological opacity of Chinese characters. The present study examined the role of Pinyin spelling in the reading abilities of adolescents learning Chinese as a foreign language (CFL). A total of 158 Indonesian senior primary students were tested on Pinyin spelling, character production, listening comprehension, depth of vocabulary knowledge, and reading comprehension. Pinyin spelling skills were assessed by two measures, Pinyin Dictation (sentence dictation in Pinyin) and Pinyin Tagging (Pinyin writing for characters). Path analysis revealed that even after controlling for the effect of character production, Pinyin dictation performance influenced reading comprehension through the mediating effect of listening comprehension and the depth of vocabulary knowledge, and Pinyin tagging performance also influenced reading comprehension through the mediating effect of the depth of vocabulary knowledge. The results highlight the importance of Pinyin skills for Chinese reading abilities of CFL learners. As a reliable and explicit indicator of specifying Chinese phonological representations and processing, Pinyin spelling has a long-term and multifaceted influence on higher-level CFL abilities.

Assessing financial protection in health: Does the choice of poverty line matter?

Financial protection in health is an essential aspect of the universal health coverage discourse. It is about ensuring that paying for health services does not affect the ability of households and individuals to afford necessities. A well‐known way to assess financial protection is whether or not people are pushed into—or further into—poverty by paying out‐of‐pocket for health services. Although impoverishment from out‐of‐pocket health spending is not an explicit indicator of the sustainable development goals, it has gained prominence among researchers and policymakers because of its intuitive appeal and link to overall poverty reduction. Using data from Nigeria, this paper demonstrates that the choice of poverty line matters for assessing the impoverishing effect of paying out‐of‐pocket for health services. Among other things, the inconsistencies (or lack of dominance) could occur in ranking impoverishment levels by mutually exclusive groups within a country or in ranking different countries or a country over time. The implication is that the choice of poverty line could lead to manipulation of results for policy and for supporting an agenda that demonstrates an improvement in financial protection when this may not necessarily be the case.

Assessment of Building Robustness against Disproportionate Collapse

This paper presents a methodology to assess the robustness of moment-resisting RC frame buildings under multiple-column-loss scenarios. In the proposed approach, kinetic and strain energy are employed as objective measures of the dynamic effects and the plastic deformations induced in buildings by the sudden loss of a bearing element, whereas a unique chord rotation is used as an explicit indicator of the global system deformation. These quantities are utilized to define a critical path index ICP that allows identifying the areas most impacted by column loss and determining the location of the next critical column in the building. The proposed index is integrated into a systematic methodology that identifies the minimum number of columns that need to be removed to induce structural collapse. An extensive numerical investigation was conducted on three-dimensional RC buildings of varying height. Comparisons between the demand induced on the buildings by the identified critical path and random column removals confirm that the sequence identified by the proposed methodology is the one that induces the most severe demands on the structure. Correlation between the variation of the system strain energy and the activation of secondary resisting mechanisms that develop as more columns are removed also confirms the ability of taller buildings to engage larger portions of the structure in resisting progressive collapse. The proposed methodology provides an objective approach for comparing the relative robustness of buildings subjected to column losses and help advance the understanding and mitigation of disproportionate collapse.

Adaptive analysis using scaled boundary finite element method in 3D

In this paper, an adaptive refinement technique using the scaled boundary finite element method (SBFEM) is proposed. The salient feature of this technique is that it is not required to regenerate the mesh for the whole model during the iterations. To this end, a local mesh refinement strategy is implemented based on a polytree algorithm in three dimensions, which can be applied to polyhedral elements with arbitrary number of nodes, edges and faces. These elements constructed by the SBFEM can be used in analysis with their boundaries discretized only, which reduce the difficulty to connect elements with different sizes. An explicit residual based error indicator is developed using the discontinuity of the stress field to guide the adaptive mesh refinement. The accuracy and efficiency of the proposed method are demonstrated using five numerical examples, including complex geometry and stress singularity.

Administrative boundaries and urban areas in Italy: A perspective from scaling laws

Delineating boundaries of urban areas is no easy task, due to the inherent complexity of the problem, heterogeneity of relevant data and little consensus on how to properly measure the results. Any such delineation must eventually be cast onto administrative boundaries, an essential requirement for real-world applications. In the effort of relating administrative and alternative boundaries, we investigated in Italy the validity of general scaling laws, such as the area-population relation, and proposed a practical application. Relying on open data for population, settlements and road networks, we showed the extent to which scaling relations hold for different boundaries for urban areas, and how they compare to each other. We considered, beside Italian municipalities, urban areas based on the idea of “natural cities”, obtained using head/tail breaks of areas related to human mobility as an explicit indicator of existence of a city. Area-population data for administrative boundaries can be reconciled with scaling relations valid for both the world’s cities data and with those obtained from natural cities, provided an effective area is adopted in place of polygon planimetric area of municipalities. We eventually proposed an aggregation of administrative units using the empirical scaling relation as an objective function for accepting or rejecting pairwise fusion of boundaries. We suggest considering such a method, along with expert considerations, as an additional tool for real-world urban planning as seen from the very general perspective of seemingly abstract scaling laws.

Joint Label Prediction Based Semi-Supervised Adaptive Concept Factorization for Robust Data Representation

Constrained Concept Factorization (CCF) yields the enhanced representation ability over CF by incorporating label information as additional constraints, but it cannot classify and group unlabeled data appropriately. Minimizing the difference between the original data and its reconstruction directly can enable CCF to model a small noisy perturbation, but is not robust to gross sparse errors. Besides, CCF cannot preserve the manifold structures in new representation space explicitly, especially in an adaptive manner. In this paper, we propose a joint label prediction based Robust Semi-Supervised Adaptive Concept Factorization (RS2ACF) framework. To obtain robust representation, RS2ACF relaxes the factorization to make it simultaneously stable to small entrywise noise and robust to sparse errors. To enrich prior knowledge to enhance the discrimination, RS2ACF clearly uses class information of labeled data and more importantly propagates it to unlabeled data by jointly learning an explicit label indicator for unlabeled data. By the label indicator, RS2ACF can ensure the unlabeled data of the same predicted label to be mapped into the same class in feature space. Besides, RS2ACF incorporates the joint neighborhood reconstruction error over the new representations and predicted labels of both labeled and unlabeled data, so the manifold structures can be preserved explicitly and adaptively in the representation space and label space at the same time. Owing to the adaptive manner, the tricky process of determining the neighborhood size or kernel width can be avoided. Extensive results on public databases verify that our RS2ACF can deliver state-of-the-art data representation, compared with other related methods.