scholarly journals Impact of COVID-19 Lockdown on the Fisheries Sector: A Case Study from Three Harbors in Western India

2021 ◽  
Vol 13 (2) ◽  
pp. 183
Author(s):  
Ram Avtar ◽  
Deepak Singh ◽  
Deha Agus Umarhadi ◽  
Ali P. Yunus ◽  
Prakhar Misra ◽  
...  
Keyword(s):  
Global Economy ◽  
Emergency Situation ◽  
Support Vector ◽  
Small Scale ◽  
Western India ◽  
Western Coast ◽  
Land Area ◽  
Fishing Activity ◽  
South West Monsoon ◽  
The Impact

The COVID-19 related lockdowns have brought the planet to a standstill. It has severely shrunk the global economy in the year 2020, including India. The blue economy and especially the small-scale fisheries sector in India have dwindled due to disruptions in the fish catch, market, and supply chain. This research presents the applicability of satellite data to monitor the impact of COVID-19 related lockdown on the Indian fisheries sector. Three harbors namely Mangrol, Veraval, and Vankbara situated on the north-western coast of India were selected in this study based on characteristics like harbor’s age, administrative control, and availability of cloud-free satellite images. To analyze the impact of COVID in the fisheries sector, we utilized high-resolution PlanetScope data for monitoring and comparison of “area under fishing boats” during the pre-lockdown, lockdown, and post-lockdown phases. A support vector machine (SVM) classification algorithm was used to identify the area under the boats. The classification results were complemented with socio-economic data and ground-level information for understanding the impact of the pandemic on the three sites. During the peak of the lockdown, it was found that the “area under fishing boats” near the docks and those parked on the land area increased by 483%, 189%, and 826% at Mangrol, Veraval, and Vanakbara harbor, respectively. After phase-I of lockdown, the number of parked vessels decreased, yet those already moved out to the land area were not returned until the south-west monsoon was over. A quarter of the annual production is estimated to be lost at the three harbors due to lockdown. Our last observation (September 2020) result shows that regular fishing activity has already been re-established in all three locations. PlanetScope data with daily revisit time has a higher potential to be used in the future and can help policymakers in making informed decisions vis-à-vis the fishing industry during an emergency situation like COVID-19.

scholarly journals Impact of mechanized clam dredging on the discarded megabenthic fauna on the Catalan coast (NW Mediterranean)

2021 ◽  
pp. 1-9
Author(s):  
Marc Baeta ◽  
Claudia Rubio ◽  
Françoise Breton
Keyword(s):  
Small Scale ◽  
Fishing Activity ◽  
Lethal Damage ◽  
Catalan Coast ◽  
Nw Mediterranean ◽  
Chamelea Gallina ◽  
Level Scale ◽  
Nw Mediterranean Sea ◽  
The Impact ◽  
Partial Damage

Abstract There is an important small-scale fishery using mechanized dredges and targeting clams (mainly wedge clam Donax trunculus and striped venus clam Chamelea gallina) along the Catalan coast (NW Mediterranean Sea). This study evaluated for the first time the discards and impact of mechanized clam dredging on the Catalan coast. To this end, three surveys were performed on board standard clam vessels (September and November 2016 and January 2017). Surveys were conducted in the three main clam fishing areas (Rosas Bay, South Barcelona and Ebro Delta). The composition of discards and the impact caused to discarded species was assessed using a three-level scale (undamaged; minor or partial damage; and lethal damage). Our study revealed that a large proportion of the catch (between 67–82% weight) is discarded. Even though about 63% of the discarded species were undamaged, 11% showed minor or partial damage and 26% lethal damage. Infaunal and epifaunal species with soft-body or fragile shells were the most impacted by the fishing activity (e.g. the sea urchin Echinocardium mediterraneum (~89%) and the bivalve Ensis minor (~74%)). Our results showed different levels of impact by target species and fishing area.

Craft and Small Scale Production in the Global Economy: Gujarat and Kachchh in the Eighteenth and Twenty-first Centuries

Itinerario ◽  
2013 ◽  
Vol 37 (2) ◽  
pp. 23-45 ◽  
Author(s):  
Maxine Berg
Keyword(s):  
Global Economy ◽  
International Markets ◽  
Recent Book ◽  
Small Scale ◽  
Oral Histories ◽  
Western India ◽  
Scale Production ◽  
World Markets ◽  
History Of ◽  
Craft Skills

India's production of fine luxury and craft goods for world markets was discovered and exploited by Europeans in the seventeenth and eighteenth centuries. Textile producers in Gujarat, the Coromandel Coast, and Bengal applied fine craft skills to European designs, colour codes, and textile lengths and widths. Through the intervention of the East India Companies and private traders as well as their intermediaries, brokers and local merchants, weavers, and printers produced the goods to satisfy Western markets just as they had done for Eastern and African markets in the centuries before.Today Indian craftspeople are engaging in a new phase of production for global markets. They are using traditional techniques of the kind that attracted Western buyers in the seventeenth and eighteenth centuries: hand weaving, hand block printing, and natural dyes. Accessing the niche national and international markets needed to provide a future for these crafts is a major challenge. This article focuses on the artisans, skills and markets in one area of India—the region of Kachchh in northern Gujarat, even now considered a remote part of the new global India. It sets this within a wider context of Gujarat and the earlier and more recent history of its textile industries. Douglas Haynes's recent book, Small Town Capitalism in Western India (2012) provides a framework for the study of small-scale industry, and the article will address his subject and methods. The new sources used are a collection of oral histories of craftspeople in a range of industries. These oral histories address skills and training across generations, and how these crafts have adapted and continue to adapt to the demands of national and world markets.

scholarly journals IMPACT OF FUEL SUBSIDIES ON FISHERMEN INCOME AN EMPIRICAL STUDY OF SMALL-SCALE FISHERMEN IN KEDAH AND PERLIS

2020 ◽  
Vol 2 (No.1) ◽  
pp. 35-47
Author(s):  
Wan Hafidzatul Akmal Mgt Husain ◽  
Jamal Ali ◽  
Amizam Arzemi
Keyword(s):  
Socioeconomic Background ◽  
Small Scale ◽  
Monthly Income ◽  
Group Discussions ◽  
Fishing Activity ◽  
Explanatory Variables ◽  
Incentive Value ◽  
Fuel Subsidies ◽  
The Cost ◽  
The Impact

Even though fuel subsidy can give more profit to the fisheries sector, it also has drawbacks to the ecosystem that tends to lead to overcapacity. This study investigated the impact of fuel subsidies on the income of fishermen operating in Zone A and Zone B in Kedah and Perlis. Focus group discussions with fisherman association committees were conducted to understand the issues related to fuel subsidy. The regression analysis method was used to examine the relationship between the fishermen’s monthly income and various explanatory variables, such as oil subsidy received by the fishermen, the incentive value of catch received, the monthly allowance, the location of fishing activity, and the socioeconomic background of fishermen. The result of the study revealed that Boat B fishermen gained more benefits from the implementation of fuel subsidized policy than Boat A fishermen because the former used larger boat sizes and better fishing gear. In addition, variables, such as fuel subsidy, operating costs, and duration of fishing hours, influenced fishermen's income. Although fuel subsidies may contribute toward overfishing, fuel subsidy is still needed by small-scale fishermen since it can reduce the cost of fishing activities and thus increase their monthly income. This research indicates that it is important to understand the contribution of fuel as the total cost of fishing activities and how fuel subsidies can reduce these costs to improve the income of fishermen in the study area.

scholarly journals Effect of Coronavirus on Aquaculture in Oyo state, Nigeria

2021 ◽  
Vol 13 (11) ◽  
pp. 153
Author(s):  
Selorm Omega ◽  
Alexander T. K. Nuer ◽  
Enoch Ametepey
Keyword(s):  
Coping Strategies ◽  
Survey Design ◽  
Health Concern ◽  
Primary Data ◽  
Small Scale ◽  
Cross Sectional Survey ◽  
Fishing Activity ◽  
Cross Sectional ◽  
Small Scale Farmers ◽  
The Impact

Coronavirus 2019 is a global health concern that has left most countries in a state of severe economic meltdown. Scientific research has been down on the virus and its impact on various sectors but that of the Nigerian aquaculture industry has been missing. This paves the way for this research to aim at bridging this gap by looking at the perception of fish farmers on the influence of coronavirus on their activities, the challenges they face during the period of the virus, and the coping strategies adopted to mitigate the impact of the virus. The research used cross sectional survey design with the sample size being 11 fish farmers living in Oyo state, Nigeria. Homogeneous purposive sampling was used and primary data collected through the use of google form. The data collected was analysis using SPSS version 25.0. The result of the analysed data showed that: on socioeconomic characteristics; the majority of the respondent reported that Coronavirus has had an effect on their fishing activity and they were mostly small scale farmers with catfish being the predominate fish farmed. The majority of fish farmers perceived demand decline, high cost of production, fish being more expensive, and reduction of manpower on the farm due to lockdown measures. Reduction in walk-in customers to the farm was revealed as the major challenge posed by the pandemic, while the inability to get technical support as least. On coping strategies adopted, it was revealed that farmers have resorted to the development of their own feed.

scholarly journals Transfer Detection of YOLO to Focus CNN’s Attention on Nude Regions for Adult Content Detection

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 26
Author(s):  
Nouar AlDahoul ◽  
Hezerul Abdul Karim ◽  
Mohd Haris Lye Abdullah ◽  
Mohammad Faizal Ahmad Fauzi ◽  
Abdulaziz Saleh Ba Wazir ◽  
...  
Keyword(s):  
Recent Literature ◽  
False Negative ◽  
False Negative Rate ◽  
Regions Of Interest ◽  
Support Vector ◽  
Small Scale ◽  
High False Negative Rate ◽  
Diverse Backgrounds ◽  
Ablation Study ◽  
The Impact

Video pornography and nudity detection aim to detect and classify people in videos into nude or normal for censorship purposes. Recent literature has demonstrated pornography detection utilising the convolutional neural network (CNN) to extract features directly from the whole frames and support vector machine (SVM) to classify the extracted features into two categories. However, existing methods were not able to detect the small-scale content of pornography and nudity in frames with diverse backgrounds. This limitation has led to a high false-negative rate (FNR) and misclassification of nude frames as normal ones. In order to address this matter, this paper explores the limitation of the existing convolutional-only approaches focusing the visual attention of CNN on the expected nude regions inside the frames to reduce the FNR. The You Only Look Once (YOLO) object detector was transferred to the pornography and nudity detection application to detect persons as regions of interest (ROIs), which were applied to CNN and SVM for nude/normal classification. Several experiments were conducted to compare the performance of various CNNs and classifiers using our proposed dataset. It was found that ResNet101 with random forest outperformed other models concerning the F1-score of 90.03% and accuracy of 87.75%. Furthermore, an ablation study was performed to demonstrate the impact of adding the YOLO before the CNN. YOLO–CNN was shown to outperform CNN-only in terms of accuracy, which was increased from 85.5% to 89.5%. Additionally, a new benchmark dataset with challenging content, including various human sizes and backgrounds, was proposed.

scholarly journals Sound Classification Based on Multihead Attention and Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lei Yang ◽  
Hongdong Zhao
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Classification Systems ◽  
Support Vector ◽  
Small Scale ◽  
Sound Classification ◽  
Comparable Performance ◽  
Public Datasets ◽  
Recurrent Architecture ◽  
The Impact

Sound classification is a broad area of research that has gained much attention in recent years. The sound classification systems based on recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have undergone significant enhancements in the recognition capability of models. However, their computational complexity and inadequate exploration of global dependencies for long sequences restrict improvements in their classification results. In this paper, we show that there are still opportunities to improve the performance of sound classification by substituting the recurrent architecture with the parallel processing structure in the feature extraction. In light of the small-scale and high-dimension sound datasets, we propose the use of the multihead attention and support vector machine (SVM) for sound taxonomy. The multihead attention is taken as the feature extractor to obtain salient features, and SVM is taken as the classifier to recognize all categories. Extensive experiments are conducted across three acoustically characterized public datasets, UrbanSound8K, GTZAN, and IEMOCAP, by using two commonly used audio spectrograms as inputs, respectively, and we fully evaluate the impact of parameters and feature types on classification accuracy. Our results suggest that the proposed model can reach comparable performance with existing methods and reveal its strong generalization ability of sound taxonomy.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

International trade: Research of the reasons for the fall

2019 ◽  
pp. 79-91 ◽  
Author(s):  
V. S. Nazarov ◽  
S. S. Lazaryan ◽  
I. V. Nikonov ◽  
A. I. Votinov
Keyword(s):  
Growth Rate ◽  
International Trade ◽  
Exchange Rate ◽  
Global Economy ◽  
Structural Changes ◽  
Global Value Chains ◽  
Value Chains ◽  
Negative Contribution ◽  
Basic Commodity ◽  
The Impact

The article assesses the impact of various factors on the growth rate of international trade. Many experts interpreted the cross-border flows of goods decline against the backdrop of a growing global economy as an alarming sign that indicates a slowdown in the processes of globalization. To determine the reasons for the dynamics of international trade, the decompositions of its growth rate were carried out and allowed to single out the effect of the dollar exchange rate, the commodities prices and global value chains on the change in the volume of trade. As a result, it was discovered that the most part of the dynamics of international trade is due to fluctuations in the exchange rate of the dollar and prices for basic commodity groups. The negative contribution of trade within global value chains in 2014 was also revealed. During the investigated period (2000—2014), such a picture was observed only in the crisis periods, which may indicate the beginning of structural changes in the world trade.

Structural Modeling of Institute of Entrepreneurship in Russia

2019 ◽  
pp. 34-48
Author(s):  
Svetlana L. Sazanova
Keyword(s):  
Global Economy ◽  
Structural Model ◽  
Institutional Environment ◽  
Small And Medium Enterprises ◽  
Structural Modeling ◽  
Entrepreneurial Activity ◽  
Pattern Modeling ◽  
Medium Enterprises ◽  
The Impact ◽  
The Relationship

Entrepreneurship plays an important role in the modern global economy; the share of products of small and medium enterprises in the gross product and exports not only of the developed but also of developing countries is growing. Innovation processes cover all sectors of the economy, and more and more people are involved in entrepreneurial activity, which contributes to the penetration of entrepreneurial thinking and business values in all areas of the socioeconomic life of society. The Institute of Entrepreneurship plays an increasingly prominent role in the institutional environment of socio-economic systems. This actualizes the problem of studying the relationship of the institution of entrepreneurship with the institutions of law, culture, management. This requires a methodology that allows you to explore the impact on the institute of entrepreneurship not only economic, but also non-economic factors. The methodology of the “old” institutionalism possesses such a tool, it is structural modeling (pattern modeling), which allows to explore the diversity of interrelationships of the institution of entrepreneurship with other components of the institutional and economic environment. The article explored the features of the development of the institution of entrepreneurship in Russia, established the relationship between the institution of entrepreneurship, values, motives and incentives for entrepreneurial activity, built a structural model of the institution of entrepreneurship based on the methodology of the old institutionalism (pattern modeling). The structural model of the institution of entrepreneurship reveals the relationship between the institution of entrepreneurship, the values of entrepreneurial activity, its motives and incentives; as well as the relationship between the institution of entrepreneurship with the institutions of governance, cultural and religious institutions, legal institutions and society.

Sign in / Sign up

Export Citation Format

Share Document