ROLE OF WOMEN ENTERPRISE FUND ON IMPROVING FOOD SECURITY OF WOMEN IN KARAPUL SUB LOCATION, SIAYA SUB COUNTY, KENYA

Purpose: The purpose of this study was to examine the role of WEF on food security of women in Karapul Sub-location of Siaya Sub-County. Materials and Methods: The study was guided by the Capability theory by Amartya Sen which emphasizes the importance of considering social and environmental variations in analysis of impact of programs. This study used cross sectional research design. The target population was 551 women from all women groups that received the WEF between the years 2011 to 2014.Simple random sampling was used to select 155 respondents which is 30% of the target population. The Chief Township location and the Constituency Women Enterprise Fund Officer were purposively selected as key informants. The study used triangulation of mixed methods that included questionnaires, focus group discussions, and key informant interviews. The Statistical Package for Social Sciences version 21 was used to run descriptive statistics such as frequency and percentages so as to present the quantitative data in form of tables, pie charts and bar graphs based on the major research questions. Qualitative data was summarized, grouped and ranked accordingly noting the similarities and differences in the responses from the interviews and presented in narration. Results: Findings of this study showed that an improvement in the food security of women was determined by the social variations and environmental diversity of the individual women. Women in formal employment at 67 per cent reported that they could consume three meals in a day same to those with established businesses at 38 per cent. Delay in release of loans affected women doing farming who depend on seasons similar to those who targeted certain peak periods to sell their products. Women who did group projects at 38.1 per cent reported to have increased food access due to higher returns from their businesses. Findings also indicate a big disparity in the varieties of food eaten to constitute a nutritious diet with more consumption of cereals and food from animal sources. Unique contribution to theory, practice and policy: The study was guided by the Capability theory by Amartya Sen which emphasizes the importance of considering social and environmental variations in analysis of impact of programs. The study recommends that emphasis should be laid on group projects to maximize profits and increase incomes. The WEF secretariat should ensure timely disbursement of funds and the GOK in its Food Security and Nutrition policy should increase farm inputs of women engaged in farming to ensure WEF improves their food security.

Inter-Zone Differences of Convective Development in a Convection Outbreak Event over Southeastern Coast of China: An Observational Analysis

Among the densely-populated coastal areas of China, the southeastern coast has received less attention in convective development despite having been suffering from significantly increasing thunderstorm activities. The convective complexity under such a region with extremely complex underlying and convective conditions deserves in-depth observational surveys. This present study examined a high-impact convection outbreak event with over 40 hail reports in the southeastern coast of China on 6 May 2020 by focusing on contrasting the convective development (from convective initiation to supercell occurrences) among three adjacent convection-active zones (north (N), middle (M), and south (S)). The areas from N to S featured overall flatter terrain, higher levels of free convection, lower relative humidity, larger convective inhibition, more convective available potential energy, and greater vertical wind shears. With these mesoscale environmental variations, distinct inter-zone differences in the convective development were observed with the region’s surveillance radar network and the Himawari-8 geostationary satellite. Convection initiated in succession from N to S and began with more warm-rain processes in N and M and more ice-phase processes in S. The subsequent convection underwent more vigorous vertical growth from N to S. The extremely deep convection in S was characterized by the considerably strong precipitation above the freezing level, echo tops of up to 18 km, and a great amount of deep (even overshooting) and thick convective clouds with significant cloud-top glaciation. Horizontal anvil expansion in convective clouds was uniquely apparent over S. From N to S, more pronounced mesocyclone and weak-echo region signatures indicated high risks of severe supercell hailstorms. These results demonstrate the strong linkage between the occurrence likelihood of severe convection and associated weather (such as supercells and hailstones) and the early-stage convective development that can be well-captured by high-resolution observations and may facilitate fine-scale convection nowcasting.

DAFI

WiFi-based Device-free Passive (DfP) indoor localization systems liberate their users from carrying dedicated sensors or smartphones, and thus provide a non-intrusive and pleasant experience. Although existing fingerprint-based systems achieve sub-meter-level localization accuracy by training location classifiers/regressors on WiFi signal fingerprints, they are usually vulnerable to small variations in an environment. A daily change, e.g., displacement of a chair, may cause a big inconsistency between the recorded fingerprints and the real-time signals, leading to significant localization errors. In this paper, we introduce a Domain Adaptation WiFi (DAFI) localization approach to address the problem. DAFI formulates this fingerprint inconsistency issue as a domain adaptation problem, where the original environment is the source domain and the changed environment is the target domain. Directly applying existing domain adaptation methods to our specific problem is challenging, since it is generally hard to distinguish the variations in the different WiFi domains (i.e., signal changes caused by different environmental variations). DAFI embraces the following techniques to tackle this challenge. 1) DAFI aligns both marginal and conditional distributions of features in different domains. 2) Inside the target domain, DAFI squeezes the marginal distribution of every class to be more concentrated at its center. 3) Between two domains, DAFI conducts fine-grained alignment by forcing every target-domain class to better align with its source-domain counterpart. By doing these, DAFI outperforms the state of the art by up to 14.2% in real-world experiments.

Comparison of artificial intelligence techniques to failure prediction in contaminated insulators based on leakage current

Contamination in insulators results in an increase in surface conductivity. With higher surface conductivity, insulators are more vulnerable to discharges that can damage them, thus reducing the reliability of the electrical system. One of the indications that the insulator is losing its insulating properties is its increase in leakage current. By varying the leakage current over time, it is possible to determine whether the insulator will develop an irreversible failure. In this way, by predicting the increase in leakage current, it is possible to carry out maintenance to avoid system failures. For forecasting time series, there are many models that have been studied and the definition of which model is suitable for evaluation depends on the characteristics of the data associated with the analysis. Thus, this work aims to identify the most suitable model to predict the increase in leakage current in relation to the time the insulator is outdoors, exposed to environmental variations using the same database to compare the methods. In this paper, the models based on linear regression, support vector regression (SVR), multilayer Perceptron (MLP), deep neural network (DNN), and recurrent neural network (RNN) will be analyzed comparatively. The best accuracy results for prediction were found using the RNN models, resulting in an accuracy of up to 97.25%.

Morphometric and morphological characterization of chicken resources adapted to pastoral and agropastoral areas of southern Ethiopia

Thirteen qualitative and six quantitative variables taken from 303 adult chickens (95 cocks and 208 hens) from three locations/districts were used to phenotypically characterize the indigenous chicken populations in pastoral areas of South Omo Zone, Ethiopia. The studied traits were influenced by the effect of location and sex, where chicken populations from Hamer district and females of all districts were the smallest and lightest. Qualitative characteristics of the studied chicken populations such as normal feather morphology and distribution, plain plumage pattern, flat head shape, triangular body shape, and dominant red eye, earlobe and plumage colour suggest that they constitute previously undescribed populations. Chest circumference, wingspan and body length were the three most important morphometric traits used in discriminating the studied chicken populations. On average, 61% of the sampled populations were classified correctly into their respective locations. The multivariate analysis results discriminate the chicken populations into two groups: the Hamer group and the Omo group (chickens from Bena Tsemay and Male districts). However, such grouping should be confirmed and advanced to ecotype level using further genetic characterization studies as the observed phenotypic differences might be due to genetic or environmental variations. Such confirmation is important to design breeding programmes (for sustainable utilization) specific to each ecotype.

SPATIAL-TEMPORAL DISTRIBUTION OF POLYCHAETA IN URBANIZED SANDY BEACHES OF NORTHEASTERN BRAZIL: TOOLS FOR ENVIRONMENTAL ASSESSMENT

In the last years, the use of Polychaeta as indicators of marine pollution has intensified, due to the sensitivity of these organisms to environmental variations and their significant presence in quantitative and qualitative terms when compared to other benthic fauna organisms. We aimed to analyze the Polychaeta assemblage of two urbanized beaches in São Luís – Maranhão (Brazil), focusing on spatial-temporal distribution and look for the relation of the species sampled and possible contamination to indicate if they are suitable for environmental assessment. Sediment collection was carried out in the intertidal zone of both beaches in dry (September and November/2015) and rainy periods (March and May/2016). The samples were screened for extraction of the species, which were classified at the lowest possible taxonomic level. The samples revealed the following taxa: Lumbrineridae (Scoletoma tetraura), Nereididae (Laeonereis culveri) and Spionidae (Scolelepis sp.). The diversity and abundance of Polychaeta were greater on Caolho Beach. On these beaches, organic enrichment is not the determining variable in the structure of the Polychaeta assembly. Other studies are needed to improve the knowledge on other macrofauna species of the studied areas, comparing the richness among microhabitats and seasons, and thus elaborate conservation strategies for these ecosystems; and to test the hypothesis of the influence of tourism on this assemblage.

Gesture-Based Human Machine Interaction Using RCNNs in Limited Computation Power Devices

The use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already well-known problems of image processing: changes in lighting conditions, partial occlusions, variations in color, among others. To solve all these potential issues, deep learning techniques have been proven to be very effective. This research proposes a hand gesture recognition system based on convolutional neural networks and color images that is robust against environmental variations, has a real time performance in embedded systems, and solves the principal problems presented in the previous paragraph. A new CNN network has been specifically designed with a small architecture in terms of number of layers and total number of neurons to be used in computationally limited devices. The obtained results achieve a percentage of success of 96.92% on average, a better score than those obtained by previous algorithms discussed in the state of the art.

Behavior Individuality: A Focus on Drosophila melanogaster

Among individuals, behavioral differences result from the well-known interplay of nature and nurture. Minute differences in the genetic code can lead to differential gene expression and function, dramatically affecting developmental processes and adult behavior. Environmental factors, epigenetic modifications, and gene expression and function are responsible for generating stochastic behaviors. In the last decade, the advent of high-throughput sequencing has facilitated studying the genetic basis of behavior and individuality. We can now study the genomes of multiple individuals and infer which genetic variations might be responsible for the observed behavior. In addition, the development of high-throughput behavioral paradigms, where multiple isogenic animals can be analyzed in various environmental conditions, has again facilitated the study of the influence of genetic and environmental variations in animal personality. Mainly, Drosophila melanogaster has been the focus of a great effort to understand how inter-individual behavioral differences emerge. The possibility of using large numbers of animals, isogenic populations, and the possibility of modifying neuronal function has made it an ideal model to search for the origins of individuality. In the present review, we will focus on the recent findings that try to shed light on the emergence of individuality with a particular interest in D. melanogaster.