A Two-Stage Optimisation of Ship Hull Structure Combining Fractional Factorial Design Technique and NSGA-II Algorithm

The intricate nature of ships and floating structures presents a significant challenge for ship designers when determining suitable structural dimensions for maritime applications. This study addresses a critical research gap by focusing on a three-cargo hold model for a multipurpose cargo ship. The complex composition of these structures, including stiffening plates, deck plates, bottom plates, frames, and bulkheads, necessitates thorough structural analysis to facilitate effective and cost-efficient design evaluation. To address this challenge, the research utilises FEMAP-integrated NX NASTRAN software (2021.2) to assess hull girder stress. Furthermore, a novel approach is introduced, integrating the Design of Experiments (DOE) principles within Minitab 21.4.1 software to identify critical parameters affecting hull girder stress and production costs. This method determined the top five key parameters influencing hull girder stress: Hatch coaming plate, Hatch coaming top plate, Main deck plate, Shear strake plate, and Bottom plate, while also highlighting key parameters that impact production costs: the inner bottom plate, Inner side shell plate, Bottom plate, Web frame spacing, and Side shell plate. Ship design optimisation is then carried out by incorporating regression equations from Minitab software into the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which is managed using Python software (PyCharm Community Editon 2020.3.1). This optimisation process yields a significant 10% reduction in both ship weight and production costs compared to the previous design, achieved through prudent adjustments in plate thickness, web frame positioning, and stiffener arrangement. The optimally designed midship section undergoes rigorous validation to ensure conformity with industry standards and classification society regulations. Necessary adjustments to inner bottom plates and double bottom side girders are made to meet these stringent requirements. This research offers a comprehensive framework for the structural optimisation of ship hulls, potentially enhancing safety, sustainability, and competitiveness within the maritime engineering industry

Linear Longitudinal Strength Analysis of a Multipurpose Cargo Ship under Combined Bending and Torsional Load

Cargo ships with wide hatches usually have thin walls and limited torsional rigidity. Consequently, conducting a comprehensive torsional analysis is important because these loads can exert a significant impact. In this paper, the structural response of a multipurpose cargo ship to combined bending and torsional loads is studied using finite element analysis. The bending and torsional moments are calculated following the rules and standard regulations followed by the classification society. The ship’s 3D finite element model was verified using beam theory and direct calculations. In contrast, the accuracy of torsional stress was confirmed by comparing thin wall girder theory with direct calculation results. This study thoroughly examined the impacts of the still water bending moment, the vertical wave bending moment, and the wave-induced torsional moment on the structural response of ships. Furthermore, it scrutinised the impact of torsion on both open-deck and closed-deck ships. Hull girder normal stresses at midship due to still water and the vertical wave bending moment are shown to contribute to almost 70% of total stress in an inclined condition; stresses resulting from the horizontal wave bending moment contribute nearly 10%, while warping stresses contribute approximately 20% in open-deck ships. It is also shown that torsion has little impact on closed-deck ships. Finally, a buckling analysis was conducted to assess the ship’s buckling criteria, confirming that the linear buckling criteria were satisfied

A survey of location inference techniques on Twitter

The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such as earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or an author’s location remains a challenge, thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state of the art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features

HARC-New Hybrid Method with Hierarchical Attention Based Bidirectional Recurrent Neural Network with Dilated Convolutional Neural Network to Recognize Multilabel Emotions from Text

We present a modern hybrid paradigm for managing tacit semantic awareness and qualitative meaning in short texts. The main goals of this proposed technique are to use deep learning approaches to identify multilevel textual sentiment with far less time and more accurate and simple network structure training for better performance. In this analysis, the proposed new hybrid deep learning HARC model architecture for the recognition of multilevel textual sentiment that combines hierarchical attention with Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM) outperforms other compared approaches. BiGRU and BiLSTM were used in this model to eliminate individual context functions and to adequately manage long-range features. Dilated CNN was used to replicate the retrieved feature by forwarding vector instances for better support in the hierarchical attention layer, and it was used to eliminate better text information using higher coupling correlations. Our method handles the most important features to recover the limitations of handling context and semantics sufficiently. On a variety of datasets, our proposed HARC algorithm solution outperformed traditional machine learning approaches as well as comparable deep learning models by a margin of 1%. The accuracy of the proposed HARC method was 82.50 percent IMDB, 98.00 percent for toxic data, 92.31 percent for Cornflower, and 94.60 percent for Emotion recognition data. Our method works better than other basic and CNN and RNN based hybrid models. In the future, we will work for more levels of text emotions from long and more complex text

Use of Non-Steroidal Anti-Inflammatory Drugs and Prostate Cancer Risk: A Population-Based Nested Case-Control Study

BACKGROUND: Despite strong laboratory evidence that non-steroidal anti-inflammatory drugs (NSAIDs) could prevent prostate cancer, epidemiological studies have so far reported conflicting results. Most studies were limited by lack of information on dosage and duration of use of the different classes of NSAIDs. METHODS: We conducted a nested case-control study using data from Saskatchewan Prescription Drug Plan (SPDP) and Cancer Registry to examine the effects of dose and duration of use of five classes of NSAIDs on prostate cancer risk. Cases (N = 9,007) were men aged ≥40 years diagnosed with prostatic carcinoma between 1985 and 2000, and were matched to four controls on age and duration of SPDP membership. Detailed histories of exposure to prescription NSAIDs and other drugs were obtained from the SPDP. RESULTS: Any use of propionates (e.g., ibuprofen, naproxen) was associated with a modest reduction in prostate cancer risk (Odds ratio = 0.90; 95%CI 0.84-0.95), whereas use of other NSAIDs was not. In particular, we did not observe the hypothesized inverse association with aspirin use (1.01; 0.95-1.07). There was no clear evidence of dose-response or duration-response relationships for any of the examined NSAID classes. CONCLUSIONS: Our findings suggest modest benefits of at least some NSAIDs in reducing prostate cancer risk

Automatic lung health screening using respiratory sounds

Significant changes have been made on audio-based technologies over years in several different fields. Healthcare is no exception. One of such avenues is health screening based on respiratory sounds. In this paper, we developed a tool to detect respiratory sounds that come from respiratory infection carrying patients. Linear Predictive Cepstral Coefficient (LPCC)-based features were used to characterize such audio clips. With Multilayer Perceptron (MLP)-based classifier, in our experiment, we achieved the highest possible accuracy of 99.22% that was tested on a publicly available respiratory sounds dataset (ICBHI17) (Rocha et al. Physiol. Meas. 40(3):035,001 20) of size 6800+ clips. In addition to other popular machine learning classifiers, our results outperformed common works that exist in the literature

Exploring Women’s Decision-Making Power and HIV/AIDS Prevention Practices in South Africa

Decisions regarding sexual and reproductive health significantly impact women’s health and their protection against HIV/AIDS and other sexually transmitted infections. These decisions also impact females’ ability to reach their reproductive goals. Women’s autonomy is recognized to be vital to women’s access to reproductive healthcare, the use of contraceptives, the capacity to avoid or receive treatment for STIs (including HIV), and other reproductive and sexual health issues. This research investigated the association between the decision-making power of South African women (of reproductive age) and their knowledge and practices regarding HIV/AIDS preventive measures. The present study used data from the South Africa Demographic and Health Survey 2016. A total of 8514 women aged 15–49 years who participated in the survey were used for this research. The mean age of the women was 30.21 years, with an SD of 9.86. Approximately 38.5% of the women decided on contraceptive use, and only 11.7% of women’s partners and 49.8% of respondents were jointly involved in the decision-making process of contraceptive use. All HIV preventive measures under study were statistically significantly associated with high decision-making power; the use of a condom by the husband or partner of the women was the most significant; husbands or partners of the women with high autonomy were three times more likely to use condoms

Management of Temporomandibular Joint Dysfunction Syndrome: An Overview

Temporomandibular joint dysfunction is a complex and multifactorial disorder of oro-facial region. It is one of the most common disorders in maxillofacial region. The usual complain of the patients with this syndrome are pain in the area of the jaw and associated muscles, eating problem, chewing and locking of the jaw. It is more common in female than male. It's etiology is not yet well established. However it's successful management depends on identification and controlling of the etiological factors.J Bangladesh Coll Phys Surg 2017; 35(3): 133-14

Neonatal and child mortality data in retrospective population-based surveys compared with prospective demographic surveillance: EN-INDEPTH study.

BACKGROUND: Global mortality estimates remain heavily dependent on household surveys in low- and middle-income countries, where most under-five deaths occur. Few studies have assessed the accuracy of mortality data or determinants of capturing births in surveys. METHODS: The Every Newborn-INDEPTH study (EN-INDEPTH) included a large, multi-country survey of women aged 15-49 interviewed about livebirths and their survival status in five Health and Demographic Surveillance Systems (HDSSs). The HDSSs undertake regular household visits to register births and deaths for a given population. We analysed EN-INDEPTH survey data to assess background factors associated with not recalling a complete date-of-birth. We calculated Kaplan-Meier survival estimates for both survey and HDSS data and describe age-at-death distributions during the past 5 years for children born to the same women. We assessed the proportion of HDSS-births that could be matched on month-of-birth to survey-births and used regression models to identify factors associated with matching. RESULTS: 69,176 women interviewed in the survey reported 109,817 births and 3064 deaths in children under 5 years in the 5 years prior to the survey. In the HDSS data, the same women had 83,768 registered births and 2335 under-five deaths in the same period. A complete date-of-birth was not reported for 1-7% of survey-births. Birthdates were less likely to be complete for dead children and children born to women of higher parity or with little/no education. Distributions of reported age-at-death indicated heaping at full weeks (neonatal period) and at 12 months. Heaping was more pronounced in the survey data. Survey estimates of under-five mortality rates were similar to HDSS estimates of under-five mortality in two of five sites, higher in the survey in two sites (15%, 41%) and lower (24%) in one site. The proportion of HDSS-births matched to survey-births ranged from 51 to 89% across HDSSs and births of children who had died were less likely to be matched. CONCLUSIONS: Mortality estimates in the survey and HDSS were not markedly different for most sites. However, neither source is a "gold standard" and both sources miss some events. Research is required to improve capture and accuracy to better track newborn and child survival targets