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Fishing Performance and Marketing Channel of the Marine Fisheries: An Empirical Study Using Primary and Secondary Data
No full textThe Bay of Bengal is an important resourceful asset of Bangladesh, which has a lot of impact
on the economy through its proper use. Marine fish and fishermen are the essential mechanisms
for the economy. Thus, the investigation was undertaken to explore the assessment of marine
fishing performance and fish marketing channels. Two marine fishing locations and 90 marine
fishermen were selected. Information was gathered with a structured interview schedule. About
50% of fishing crafts were large; 8 to 17 fishermen operated those in the deep region of the
Bay of Bengal and the majority of them (52.2%) were rented. About 48% of fishermen used
their gear, 51% had large gear mostly rented and 54.4% received loans with interest from
traders, banks, and NGOs. Shrimp/prawn production is lucrative for marine fishers and
supportive of the national economy. The major problems faced by most of the fishermen of the
two locations were the weather, engine breakdown, and prohibition of fishing time and Robbers
in the Moheshkhali deep sea, whereas the use of pass cards, wild animals, protection by forest
office and use of poison in Mongla. There are four marketing channels for marine fish and
most (81%) sell to the whole sellers. A present marine fish marketing channel is more timeconsuming
for a great number of intermediaries, insufficient road links and landing
points/centers. To minimize this problem, modern information technology should be
introduced, set up collective markets at coastal landing places, and reduce the number of
middlemen
Underwater Image Recognition using Machine Learning
No full textMachine Learning is the branch of Artificial Intelligence in which a computer is fed with data
and based on that data it tries to find out solution on its own. It encompasses the procedure for
feeding algorithms information to create the algorithms realize patterns in the data and then
increase the performance of the algorithms. A Convolutional Neural Network (CNN) is a type
of a deep learned an algorithm that has been created for image processing when using
convolutional layers to automatically and in a hierarchical way learn features from the input
images. Computers can perform well when it comes to image recognition and classification
because of its capacity to detect and record such features as edges, or texture, and shapes among
others. A rise in focusing on processing underwater images is essential for various research
purposes necessary in marine biology, economy as well as in the management of species’
biodiversity. Observance of such organisms as plankton and Posidonia Oceanic allows
determining environmental shifts, global warming, and impact of people on sea creatures.
These include respectively planktons that are fundamental for oxygen generation, climatic
events and the Posidonia Oceanic, which helps improve the sea Biodiversity and water quality.
In the organisation study, image processing supplement the physio-chemical analysis and the
sonar detection system. The performances of deep learning models, especially the CNNs, in
underwater image processing are significantly better than the conventional methodologies. Preprocessing
is important because images are often low-quality; data augmentation and transfer
learning tackle the problems of a small dataset and class imbalance, which allow you to save
computations during training. Through human activities, marine trash remains a menace to
deep sea ecosystems and marine organisms calling for proper debris control
Exploring Rice Yield Variability Under Climate Change Through NDVI Analysis
No full textThis study presents a novel approach to predicting paddy yields in Brunei's Wasan Rice
Scheme using projected normalized difference vegetation index (NDVI) values derived from
climate projections under three time periods: near future (2020–2046), mid-future (2047–2073),
and far future (2074–2100). Employing CMIP6 socioeconomic pathways (SSP245, SSP370,
SSP585), random forest (RF) and multiple linear regression (MLR) models were utilised to link
historical NDVI with meteorological factors such as rainfall and temperature. Results indicate that
main-season yields are expected to decline or stabilize across scenarios, while off-season NDVI
consistently increases, reflecting robust vegetation recovery. These findings emphasise the
differential impacts of climate change across growing seasons, providing critical insights for
agricultural planning and adaptation strategies. By integrating scenario-based NDVI projections
and predictive modeling, this study offers a comprehensive framework for understanding future
crop dynamics under changing climatic conditions
Effectiveness of Ultrasound on Gait and Range of Motion among Athletes with Achilles Tendinitis - A Narrative Review
No full textBackground: Achilles tendinitis is a common condition among sports players. It is caused by repetitive action and overuse of the Achilles tendon, with clinical characteristics such as inflammation, increased pain, improper gait, and decreased range of motion in the ankle. The objective of this study is to identify the potential effects of therapeutic ultrasound and range of motion in athletes with Achilles tendinitis.
Method: This review includes studies obtained from Google Scholar, PubMed, Cochrane, and ResearchGate databases. Therapeutic ultrasound is specifically examined in all the studies included in the analysis of gait and the range of motion of the ankle. A systematic narrative review form is used to analyze the studies that meet the present study criteria. In total, five studies were included: one pilot study, one case study, three randomized control trials, one experimental study, and two longitudinal studies.
Conclusion: From this review, we conclude that therapeutic ultrasound was found to be effective in improving the participants’ gait and ankle range of motion following Achilles tendinitis
Body Motion Auto Tracking Camera System for Online Class Education Supporting Device Using OpenCV and Microcontroller
No full textIn the online teaching process, a teacher is necessary to describe the material clearly and have a
broad perspective so that the material can be delivered completely. The problem occurs because
the camera has different viewing angles, which results in limited movement of people who are
teachers whenever explain material on a broad chalkboard. For this reason, a motion tracking
camera is designed like a common camera that can move to follow the teacher's upper body. The
camera will be connected to servo motors such as MG996R to move the camera on the X-Axis and
Y-Axis. Then with OpenCV technology, the camera will track the teacher as an object and follow
the direction of his/her movement. The results from thisresearch, it is concluded that by integrating
Python into a video conference application and without it, the system can detect the upper body at
2-6 meters. For the X-Axis servo motor angle for conditions using video conference and without
video conference application, the servo can rotate by detecting the upper body at angles of 30deg,
60deg, 90deg, 120deg, and 150deg. Then on the Y-Axis movement, it can rotate by detecting the
upper body at 90deg, 100deg, 110deg, 120deg and 130deg angles. The test was carried out in a
room with an area of 9.1 m x 7.4 m, the light condition of the room was 116 lux the object height
was 1.5 m, and the camera height was 0.8 m above the floo
Firm Characteristics and Earnings Management Practices (EMP): Comparative Analysis in Sub-Saharan Africa
No full textThe study examines the influence of firms’ characteristics on accrual and real Earnings Management Practices (EMP). Firm characteristics include Asset Structure (AS), Capital Structure (CS), Dividend Payout Ratio (DPR), Firm Profitability (FP), Free Cash Flow (FCF), and Working Capital (WC). EMP was proxied by Discretionary Accrual (DA) and Real Earnings Management (REM). Two hundred and seventy-nine (279) non-financial listed firms with the data required for the study from 2010 to 2020 were selected from six (6) countries in sub-Saharan Africa. The study found that AS has a negative and significant influence on DA in Kenya and Tanzania. DPR positively and significantly influence DA in Zimbabwe and WC on DA in South Africa. Also, CS has a positive and significant impact on DA in Nigeria and DPR and CS on DA in Ghana. Also, AS and CS have a positive and significant impact on REM in Ghana. However, AS, FCF, DPR, FP, and WC have a negative and significant effect on REM in Nigeria. The study concludes that DA and REM significantly influence firm characteristics in sub-Saharan Africa. This study fills exiting gaps on EMP in Sub-Saharan Africa by considering the effect of firms’ characteristics on both DA and REM. The study expands knowledge on the importance of firm characteristics on EMP in sub-Saharan Africa regions where the majority of countries are developing nations and REM has not received adequate attention
Prevention of Unauthorized Access to Electronic Health Records using Docker
No full textThis research focuses on securing electronic health records (EHRs) in cloud environments using Docker. The goal is to prevent unauthorized access and data loss while uploading EHRs to the cloud. By leveraging Docker's containerization capabilities, we propose a security framework that includes encryption, access control, and authentication protocols. Through extensive testing, we demonstrate the effectiveness of our approach in enhancing EHR security. This research provides valuable insights for healthcare organizations and cloud service providers seeking to protect sensitive medical data while leveraging the advantages of cloud computing
Oil Price and Energy Intensity Dynamics in Nigeria: Does Technical Change Matter?
No full textIt is critical to understand the mechanism needed to control energy intensity especially in an oilexporting
country like Nigeria, because of its consequential effect on carbon dioxide emissions
and environmental pollution. Increases in energy prices can lead to promotion of better technology
and consequently, a reduction in energy intensity through a reduction in energy demand
(consumption). This paper explores the dynamics between energy price and energy intensity to
reveal the role of technical change in the equation. The study utilizes an autoregressive distributed
lag (ARDL) and Toda-Yamamoto approaches. The study sample covers the period 1980 through
2021. The key contribution of this study to the literature is rooted in an understanding of the
dynamics of energy intensity and its interplay with technical change in a country study as a critical
piece of information for policymakers. The results indicate a change in oil price significantly
affects technical innovation. However, there is no link between technical innovation and energy
intensity. The plausible justification for the results is the enormity of oil subsidy policy of the
Nigerian government
Exploration of AI and AR Technologies in the Character Design of "Dream of the Red Chamber"
No full textThis study explores the potential of artificial intelligence (AI) and augmented reality (AR)
technologies in recreating the character of Wang Xifeng, a core figure in the Chinese classical
literary masterpiece "Dream of the Red Mansions". By integrating two advanced tools, Midjourney
and Kivicube, the study successfully created and displayed a digital virtual avatar of Wang Xifeng.
The research employed qualitative research methods, including content analysis, experimental
research, and a questionnaire survey. The feedback results showed that most participants were
satisfied with the reshaped image of Wang Xifeng using AI and AR technologies. This study not
only opens up new avenues for the modern interpretation of traditional literary characters but also
provides valuable experience and theoretical support for the inheritance and innovative practice of
traditional culture
Integrated RES With Intelligent MPPT For Efficient Power Generation & Wireless Transmission for PV Applications
No full textThe increasing global energy demand and the urgent need to shift to sustainable practices have made the integration of renewable energy sources (RESs) into distribution power systems imperative. This project's main objective is to implement rooftop photovoltaic (PV) systems as an essential part of low-voltage (LV) DC networks' building-integrated centralized generation Maximum Power Point Tracking (MPPT) using Artificial Neural Networks (ANNs) is used to maximize power extraction from photovoltaic (PV) panels and boost power generation efficiency, particularly in partially shaded circumstances. After that, the PV system's generated power is sent to a Luo converter, which effectively tracks and optimizes the power production. For wireless power transmission, the Luo converter's output is then fed into a high-frequency converter. This wireless power transmission improves the system's adaptability and scalability by facilitating smooth energy transfer without the requirement for physical connections. An isolation transformer is attached to the high-frequency converter's output in order to guarantee the system's dependability and safety. The high-frequency converter is isolated from the downstream components by the isolation transformer, which also offers protection against electrical risks. Finally, a battery made especially for use in electric vehicle (EV) applications receives the transformer's isolated output, which is also supplied to DC loads. Finally, we will use the MATLAB 2021a / Simulink program to do a number of numerical simulations in order to validate the suggested controls. The output voltage of 130v is stored in the battery if a voltage of 65 v is taken as output from the photovoltaic system