Considering important role of housing in contemporary urban areas, evaluating urban housing quality has become one of the most popular topics in recent researches. Housing has vast conceptual perspectives which include many aspects of urban life beside the dwelling purpose of it, such as recreation, primary schools, and play yards and so on. The most efficient tool for achieving such purposes is evaluation of qualitative and quantitative urban housing indicators. Using quantitative, objectivistic, positivistic methods and models frequently observes in Iranian scientific journals recently. This article aims to study and evaluate such researches, using scientific analytic methods which have accepted and published in domestic scientific journals. The emphasis of this article is to review papers which studied urban indicators in any area in Iranian urban areas which published between March 2011 and September 2019. To do so, the PRISMA method (Preferred Reporting Items for Systematic Reviews and Meta Analyses) has introduced and applied and 39 papers have selected. These papers selected among all papers had indexed in Scientific Information Database (SID.ir) and Comprehensive Iranian Humanities Portal (ensani.ir) which include one of these keywords: housing indicator, urban housing. Complete information of these papers has cited and method, data resources and conclusion of each paper discussed briefly. Some criteria introduced and measured to evaluate different aspects of selected papers considering PRISMA suggestions. The most cited sources (papers, books, dissertations and statistic reports) have identified and applied in conclusion.  The results have shown that applying quantitative methods on urban indicators led to analogous assessments in various contexts and using novel methods and models, especially applying qualitative and mixed methods would resulted in novel and efficient perspectives to future urban housing programming and applying the PRISMA method would increase quality of survey reviews and strongly recommend to achieve comprehensive and qualified study of similar researches.

The role of forests in biodiversity assessment and planning is substantial as these ecosystems support approximately 80% of the world’s terrestrial biodiversity. Forests provide food, shelter, and nesting environments for numerous species, and deliver multiple ecosystem services. It has been widely recognised that forest vegetation structure and its complexity influence local variations in biodiversity. As forests are facing threats globally caused by human activities, there is a need to map the biodiversity of these ecosystems. The main objective of this review was to summarise the use of airborne laser scanning (ALS) data in biodiversity-related assessment of forests. We draw attention to topics related to animal ecology, structural diversity, dead wood, fragmentation and forest habitat classification. After conducting a thorough literature search, we categorised scientific articles based on their topics, which served as the basis for the section division in this paper. The majority of the research was found to be conducted in Europe and North America, only a small fraction of the study areas was located elsewhere. Topics that have received the most attention were related to animal ecology (namely richness and diversity of forest fauna), assessment of dead trees and tree species diversity measures. Not all studies used ALS data only, as it were often fused with other remote sensing data – especially with aerial or satellite images. The fusion of spectral information from optical images and the structural information provided by ALS was highly advantageous in studies where tree species were considered. Relevant ALS variables were found to be case-specific, so variables varied widely between forest biodiversity studies. We found that there was a lack of research in geographical areas and forest types other than temperate and boreal forests. Also, topics that considered functional diversity, community composition and the effect of spatial resolution at which ALS data and field information are linked, were covered to much lesser extent. | 2023

The energy transition represents an economic opportunity in many countries, but it can also disproportionately affect vulnerable households. Although increasing attention has been paid on energy poverty at homes, there is another dimension in which research has not yet placed enough emphasis: Transport poverty. This study proposes a framework for measuring transport poverty that uses information from the Household Budget Survey (HBS), a standardized database that is available every year in many countries. We develop and test 3 indicators that cover the affordability dimension of transport poverty (10%, 2 M, LHIC) and another one that also includes the accessibility dimension (VTU). Our research is supplemented by a statistical analysis that enables us to identify the drivers of transport poverty and by an analysis of the advantages and disadvantages of each metric. This framework offers a new common base for measuring transport poverty in different regions and enables transport poverty to be tracked over time. © 2023 The Authors | This research is supported by the European Climate Foundation (grant G-2205-64027) and by the Spanish State Research Agency through María de Maeztu Excellence Unit accreditation 2018–2022 (Ref. MDM-2017-0714).

Climate variability places significant pressure on climate-sensitive sectors such as agriculture. Information on crop-related climate and water indicators under climate change becomes essential. The Copernicus Climate Change Service (C3S) provides reliable information about climate change based on climate models, Earth Observations, and in situ data to support adaptation policies. Within this C3S context, the Global Agriculture Sectoral Information System (SIS) has developed a new product based on global crop productivity indicators to document the effects of climate variability on crop yield variability. This study focuses on the evaluation of climate-enhanced, earth observation (EO) based crop productivity indicators i.e., Total Weight Storage Organs (TWSO, equivalent to yield), Total Above Ground Production (TAGP). These variables provide insights into the Spatio-temporal variability of yield, productivity, and development of four major staple crops (rice, maize, soybean, and wheat) for their main production regions at the global scale for the period 2000–2018. In this study, the evaluation was carried out spatially and temporally for the United States of America, India, and China for the period 2000–2018, using reported yield statistics aggregated to the lowest available administrative level of each region. For almost all the crops in the three countries, the skilled error can be reduced by more than 25% for both the indicators TAGP and TWSO compared to the trend of reported yield, suggesting considerable performance in assessing interannual yield variability. Results indicated both indicators individually performed well in many important producing areas of the USA, India, and China. However, if we compare both the indicator's performance in terms of production totals in the USA, India, and China, TAGP contributes ∼27–50% of total crop production in the USA and India, and ∼47–75% in China. While, TWSO contributes ∼24–41% in the USA and India, and 5–65% in China.

Maize is one of the important grain crops grown globally, and growth will directly affect its yield and quality, so it is important to monitor maize growth efficiently and non-destructively. To facilitate the use of unmanned aerial vehicles (UAVs) for maize growth monitoring, comprehensive growth indicators for maize monitoring based on multispectral remote sensing imagery were established. First of all, multispectral image data of summer maize canopy were collected at the jointing stage, and meanwhile, leaf area index (LAI), relative chlorophyll content (SPAD), and plant height (VH) were measured. Then, the comprehensive growth monitoring indicators CGMI_CV and CGMI_CR for summer maize were constructed by the coefficient of variation method and the CRITIC weighting method. After that, the CGMI_CV and CGMI_CR prediction models were established by the partial least-squares (PLSR) and sparrow search optimization kernel extremum learning machine (SSA-KELM) using eight typical vegetation indices selected. Finally, a comparative analysis was performed using ground-truthing data, and the results show: (1) For CGMI_CV, the <i>R</i>² and RMSE of the model built by SSA-KELM are 0.865 and 0.040, respectively. Compared to the model built by PLSR, <i>R</i>² increased by 4.5%, while RMSE decreased by 0.3%. For CGMI_CR, the <i>R</i>² and RMSE of the model built by SSA-KELM are 0.885 and 0.056, respectively. Compared to the other model, <i>R</i>² increased by 4.6%, and RMSE decreased by 2.8%. (2) Compared to the models by single indicator, among the models constructed based on PLSR, the CGMI_CR model had the highest <i>R</i>². In the models constructed based on SSA-KELM, the <i>R</i>² of models by the CGMI_CR and CGMI_CV were larger than that of the models by SPAD (<i>R</i>² = 0.837), while smaller than that of the models by LAI (<i>R</i>² = 0.906) and models by VH (<i>R</i>² = 0.902). In summary, the comprehensive growth monitoring indicators prediction model established in this paper is effective and can provide technical support for maize growth monitoring.

Walnut grading is an important step before the product enters the market. However, traditional walnut grading primarily relies on manual assessment of physiological features, which is difficult to implement efficiently. Furthermore, walnut kernel grading is, at present, relatively unsophisticated. Therefore, this study proposes a novel deep-learning model based on a spatial attention mechanism and SE-network structure to grade walnut kernels using machine vision to ensure accuracy and improve assessment efficiency. In this experiment, we found through the literature that both the lightness (L* value) and malondialdehyde (MDA) contens of walnut kernels were correlated with the oxidation phenomenon in walnuts. Subsequently, we clustered four partitionings using the L* values. We then used the MDA values to verify the rationality of these partitionings. Finally, four network models were used for comparison and training: VGG19, EfficientNetB7, ResNet152V2, and spatial attention and spatial enhancement network combined with ResNet152V2 (ResNet152V2-SA-SE). We found that the ResNet152V2-SA-SE model exhibited the best performance, with a maximum test set accuracy of 92.2%. The test set accuracy was improved by 6.2, 63.2, and 74.1% compared with that of ResNet152V2, EfficientNetB7, and VGG19, respectively. Our testing demonstrated that combining spatial attention and spatial enhancement methods improved the recognition of target locations and intrinsic information, while decreasing the attention given to non-target regions. Experiments have demonstrated that combining spatial attention mechanisms with SE networks increases focus on recognizing target locations and intrinsic information, while decreasing focus on non-target regions. Finally, by comparing different learning rates, regularization methods, and batch sizes of the model, we found that the training performance of the model was optimal with a learning rate of 0.001, a batch size of 128, and no regularization methods. In conclusion, this study demonstrated that the ResNet152V2-SA-SE network model was effective in the detection and evaluation of the walnut kernels.

This study aimed at evaluating the fermentation levels of pearl millet [Pennisetum Glaucum (L.) R. Br] stovers silage during storage based on pH evolution. A completely randomized experimental design in a 6×2×2 factorial scheme with three replications for each treatment was used to evaluate three factors (6 cultivars, 2 different cutting stages, and with or without salt addition to the cultivars). The silages were prepared in plastic bags and stored for 60 days at room temperature. The results revealed that the pH values of the treatments were significantly (P<0.05) higher on the first day than in the other periods and a rapid drop in pH, with significant differences (P<0.05), to levels below 4 was obtained on the third day of storage for the majority of local Sadoré and Siaka Millet silages (Niger). Four types of pH evolution were recorded and the variation was statistical significant among cultivars. Also, analysis of the relationships between pH, chemical composition parameters and In Vitro Digestibiliy of Organic Matter (IVDOM) showed that increasing pH values were associated with increasing Dry Matter content of stovers before silage (DM_BE), Dry Matter content of silages (DM_S), Neutral Detergent Fiber (NDF), Acid Detergent Fiber (ADF), Acid Detergent Lignin (ADL) values and decreasing Crude Protein (CP), Metabolizable Energy (ME), IVOMD, and Ash values. However, the pH values obtained for all silages showed that all the millet stovers used were suitable for silage. At the maturity stage, it is thus possible to use the grain for human consumption and to ensile the stovers for animal feed. This study also shows that monitoring the pH in the silo makes it possible to evaluate the quality of the fermentations to avoid losses on the farms.

Crayfish (Procambarus clarkii) is nutritious and delicious, but its safety always worries consumers. This study aimed to investigate the microbial safety of frozen cooked crayfish. The total volatile basic nitrogen (TVB-N) content and pH of crayfish samples were measured to determine the end point of the shelf life. The structure and diversity of the microbial communities in the viscera and gills, muscle and shell of crayfish and the marinade were analyzed by high-throughput sequencing. The results showed that during the frozen storage of cooked crayfish, the pH of the viscera, gills and muscle decreased slowly, while the pH of the marinade and the shell increased first and then decreased. The TVB-N contents of all four samples showed an upward trend. On the 180th day, the TVB-N values of the muscle as well as the viscera and gills were 32.42 and 31.26 mg/100 g, respectively, which were higher than the accepted upper limit. The results of high-throughput sequencing showed that there were differences in the bacterial community structure of the four crayfish samples at the end of the shelf life. The decreasing order of the abundance of bacteria in these samples were marinade > viscera and gills > shell > muscle. The dominant bacterial genera in these samples were also different; Lactobacillus was dominant in the marinade. Psychrobacter and Vibrio were dominant in the viscera and gills, the dominant bacteria in the shell were Vibrio, Lactobacillus and Psychrobacter, and the dominant bacteria in the muscle were Psychrobacter and Acinetobacter. In conclusion, the microbial safety risk of the marinade and the viscera and gills in frozen cooked crayfish products was relatively high and could be reduced by combined use of various cleaning methods, adjusting the acidity of the marinade and using antibacterial agents.

Abstract Measurement is critical for assessing and monitoring food security. Yet, it is difficult to comprehend which food security dimensions, components, and levels the numerous available indicators reflect. We thus conducted a systematic literature review to analyse the scientific evidence on these indicators to comprehend the food security dimensions and components covered, intended purpose, level of analysis, data requirements, and recent developments and concepts applied in food security measurement. Data analysis of 78 articles shows that the household-level calorie adequacy indicator is the most frequently used (22%) as a sole measure of food security. The dietary diversity-based (44%) and experience-based (40%) indicators also find frequent use. The food utilisation (13%) and stability (18%) dimensions were seldom captured when measuring food security, and only three of the retrieved publications measured food security by considering all the four food security dimensions. The majority of the studies that applied calorie adequacy and dietary diversity-based indicators employed secondary data whereas most of the studies that applied experience-based indicators employed primary data, suggesting the convenience of collecting data for experience-based indicators than dietary-based indicators. We confirm that the estimation of complementary food security indicators consistently over time can help capture the different food security dimensions and components, and experience-based indicators are more suitable for rapid food security assessments. We suggest practitioners to integrate food consumption and anthropometry data in regular household living standard surveys for more comprehensive food security analysis. The results of this study can be used by food security stakeholders such as governments, practitioners and academics for briefs, teaching, as well as policy-related interventions and evaluations.