The marine pollution control from industrial sources in Bangladesh : a legal study

Marine pollution from industrial sources is a key concern for the government of Bangladesh. Bangladesh is not an industrialised country, but the rapid growth of industry has created a serious marine pollution problem in this country. Numerous guidelines and principles on the basis of controlling procedures have been formulated by the government in the long run for addressing the matter. Despite the initiatives, no important progress has been achieved. This study intends to discover the shortcomings of the problem and recommend necessary measures to control the pollution effectively. This thesis discusses different international legal instruments of regional and universal application and regional initiatives to control marine pollution from industrial sources. Further, from the management viewpoint, a useful scheme to control pollution is impossible to establish unless the efforts are cooperatively put in practice. This research study evaluates the prevailing legal systems, studies current institutional arrangements, developing state laws and procedures related to marine pollution in an effort to pinpoint lawful modification that may assist the country to safeguard itself due to marine pollution. This research established that there are too many laws regarding marine pollution control, but no firm law to prevent marine pollution from industrial sources. Thus, a separate and integrated marine pollution prevention law (from land-based sources including industrial sources) and policy are needed to prevent pollution from the said source. To check, decrease and regulate pollution at marine environment, the marine pollution prevention law will be the umbrella for relevant legislation that specify for overall marine environmental protection of Bangladesh. A combined strategy has a part both at the procedure and the preparation levels. stakeholders’ consciousness on a broad strategy helps to manage and coordinate mutual concerns and to reduce the potential of disagreement when policy moves to plan and finally actions. Arrangements for marine pollution from industrial sources should emphasise collaboration among stakeholders local, national and global platforms, because the decisions about marine pollution arrangement would include assistance and procedures that bring various interests and proficiency of lawmakers, professional experts and stakeholders together

A survey of image-based computational learning techniques for frost detection in plants

Frost damage is one of the major concerns for crop growers as it can impact the growth of the plants and hence, yields. Early detection of frost can help farmers mitigating its impact. In the past, frost detection was a manual or visual process. Image-based techniques are increasingly being used to understand frost development in plants and automatic assessment of damage resulting from frost. This research presents a comprehensive survey of the state-of the-art methods applied to detect and analyse frost stress in plants. We identify three broad computational learning approaches i.e., statistical, traditional machine learning and deep learning, applied to images to detect and analyse frost in plants. We propose a novel taxonomy to classify the existing studies based on several attributes. This taxonomy has been developed to classify the major characteristics of a significant body of published research. In this survey, we profile 80 relevant papers based on the proposed taxonomy. We thoroughly analyse and discuss the techniques used in the various approaches, i.e., data acquisition, data preparation, feature extraction, computational learning, and evaluation. We summarise the current challenges and discuss the opportunities for future research and development in this area including in-field advanced artificial intelligence systems for real-time frost monitoring

Nutritional assessment of mutants of Calocybe indica produced by protoplast mutagenesis

Mushrooms are rich source of protein, minerals and antioxidants. Nutritive value of mushrooms differs not only among genus but also among species. Nutritional content of mushrooms are being constantly reported. In present study, emphasis is laid on effect of mutational study on nutritional parameters of mushroom. A total of seven mutants of Calocybe indica obtained through physical and chemical mutagenic treatment were subjected to nutritional evaluation. Five mutants (CMU-5, CMN-9, CMN-11, CMN-2 and CMB-4) indicated higher protein content while ash content was also found more for all the mutants except CMN-9. Tocopherol content was also higher for all the mutants except CMN-3. ?-carotene was more from 2 mutants, CMU-2 and CMN-9. Lycopene content was better in CMU-2, CMN-9, CME-2 and CMB-4 while ascorbic acid content for CMU-2 and CMN-3 was better than that of the parent. Fat content was found to be significantly low only in mutant CMN-9 (1.24g/100g). CMN-9, mutant obtained through NTG treatment, was found better than the parent, Ci-3, not only in protein content but also in amount of vitamin A, C and E. It is indicated from the study that mutagenesis which leads to genotypic variation has effect on biochemical aspects as well. Therefore, various genetic manipulations can be exploited for nutritional enhancement aspects which need to be emphasized keeping in view the need of food quality in today’s scenario

Children Learning About Second-hand Smoke (CLASS II) : a mixed methods process evaluation of a school-based intervention

Background: Children are vulnerable to the effects of second-hand smoke exposure. Creating smoke-free homes is an effective strategy to limit exposure. We developed a smoke-free intervention (SFI) using children as a catalyst for change and teaching skills to negotiate a smoke-free home. In this paper, we present the process evaluation conducted within a pilot trial. Methods: This was a mixed-methods study comprising qualitative interviews and quantitative fidelity assessment of SFI delivery. Interviews in the six intervention schools were conducted with six headteachers and 12 teachers. These explored experiences of delivering the SFI, perceived impact, barriers and facilitators to success, and ideas for improvement and for scaling up. The data were analysed using framework analysis. Delivery of the SFI was observed and fidelity scores calculated. Results: The SFI was acceptable to headteachers and teachers. Fidelity scores ranged from 27/40 to 37/40. Didactic components were more fully implemented than interactive components. Time to complete the sessions, timing in the school day and school calendar were key challenges. Embedding the SFI into the curriculum was a potential solution. Conclusions: These findings provide useful information to finalise the content and delivery and inform the scale-up of the SFI for our definitive trial, which is now underway. Trial registration: ISRCTN6869057

A behaviour change intervention to reduce home exposure to second hand smoke during pregnancy in India and Bangladesh : a theory and evidence-based approach to development

Background: Home exposure to secondhand smoke (SHS) is highly prevalent amongst pregnant women in low- and middle-income countries like India and Bangladesh. The literature on the efficacy of behaviour change interventions to reduce home exposure to SHS in pregnancy is scarce. Methods: We employed a theory and evidence-based approach to develop an intervention using pregnant women as agents of change for their husband’s smoking behaviours at home. A systematic review of SHS behaviour change interventions led us to focus on developing a multicomponent intervention and informed selection of behaviour change techniques (BCTs) for review in a modified Delphi survey. The modified Delphi survey provided expert consensus on the most effective BCTs in reducing home exposure to SHS. Finally, a qualitative interview study provided context and detailed understanding of knowledge, attitudes and practices around SHS. This insight informed the content and delivery of the proposed intervention components. Results: The final intervention consisted of four components: a report on saliva cotinine levels of the pregnant woman, a picture booklet containing information about SHS and its impact on health as well strategies to negotiate a smoke-free home, a letter from the future baby to their father encouraging him to provide a smoke-free home, and automated voice reminder and motivational messages delivered to husbands on their mobile phone. Intervention delivery was in a single face-to-face session with a research assistant who explained the cotinine report, discussed key strategies for ensuring a smoke-free environment at home and practised with pregnant women how they would share the booklet and letter with their husband and supportive family members. Conclusion: A theory and evidence-based approach informed the development of a multicomponent behaviour change intervention, described here. The acceptability and feasibility of the intervention which was subsequently tested in a pilot RCT in India and Bangladesh will be published later

Right hemisphere has the last laugh: neural dynamics of joke appreciation

Understanding a joke relies on semantic, mnemonic, inferential, and emotional contributions from multiple brain areas. Anatomically constrained magnetoencephalography (aMEG) combining high-density whole-head MEG with anatomical magnetic resonance imaging allowed us to estimate where the humor-specific brain activations occur and to understand their temporal sequence. Punch lines provided either funny, not funny (semantically congruent), or nonsensical (incongruent) replies to joke questions. Healthy subjects rated them as being funny or not funny. As expected, incongruous endings evoke the largest N400m in left-dominant temporo-prefrontal areas, due to integration difficulty. In contrast, funny punch lines evoke the smallest N400m during this initial lexical–semantic stage, consistent with their primed “surface congruity” with the setup question. In line with its sensitivity to ambiguity, the anteromedial prefrontal cortex may contribute to the subsequent “second take” processing, which, for jokes, presumably reflects detection of a clever “twist” contained in the funny punch lines. Joke-selective activity simultaneously emerges in the right prefrontal cortex, which may lead an extended bilateral temporo-frontal network in establishing the distant unexpected creative coherence between the punch line and the setup. This progression from an initially promising but misleading integration from left frontotemporal associations, to medial prefrontal ambiguity evaluation and right prefrontal reprocessing, may reflect the essential tension and resolution underlying humor

Effect of Age on Variability in the Production of Text-Based Global Inferences

As we age, our differences in cognitive skills become more visible, an effect especially true for memory and problem solving skills (i.e., fluid intelligence). However, by contrast with fluid intelligence, few studies have examined variability in measures that rely on one’s world knowledge (i.e., crystallized intelligence). The current study investigated whether age increased the variability in text based global inference generation–a measure of crystallized intelligence. Global inference generation requires the integration of textual information and world knowledge and can be expressed as a gist or lesson. Variability in generating two global inferences for a single text was examined in young-old (62 to 69 years), middle-old (70 to 76 years) and old-old (77 to 94 years) adults. The older two groups showed greater variability, with the middle elderly group being most variable. These findings suggest that variability may be a characteristic of both fluid and crystallized intelligence in aging

Diel variations of heat fluxes across the air-sea interface from the Bay of Port Blair

The heat fluxes of sensible and latent heat over the May of Port Blair in August and September, 1988 based on the surface meteorological data collected over a period of 24 hrs in each month at an interval of 1 hour have been represented on the timescale. The latent heat throughout the 24 hrs of the day was positive during August and the air layer remained below saturation level. During September Intent heat transfer was from air to sea (ie. negative) during the period 0700 - 1500 hrs indicating that condensation has taken place. This was supported by the observation of drizzling during the period with saturated condition of the air layer in the latter month. During the day time hours in both months, the relative humidity range was from 70 to98%. Probably the high water vapor content in the air gets heated up higher than the sea surface. This may be one of the possible reasons for the transfer of sensible heat from air to sea surface during daylight hours. During the rest of the hours of the day the transfer of heat was from sea surface to air in both the months

Machine learning-based detection of freezing events using infrared thermography

Frost can cause irreversible damage to plant tissue and can significantly reduce yields and quality. A thorough understanding of the freezing dynamics is crucial to developing strategies for frost protection and the prevention of freezing damage. This study investigated artificial intelligence machine learning (ML) models to capture the thermodynamic patterns of freezing based on infrared thermography (IRT) imagery, which would help to automate the image analysis process in real-time or post frost events. A small-scale dataset of IRT images was collected to capture the freezing process from sample droplets containing ice-nucleating bacterium. We performed several ML experiments on the data to detect the transitions in temperatures from the images. We evaluated five popular ML models, namely support vector machines, random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron, and convolutional neural networks. We analysed the dataset to classify adjacent temperature transitions. The results show that ML models can consistently capture the thermodynamics of frost events, i.e., ice-nucleation and freezing points on typical freezing curves. Amongst the ML models, RF and XGBoost achieved the best results, both with an average accuracy of 87–88% in classifying the temperatures. With 0.25 °C temperature transitions, RF model identified the ice nucleation and freezing points at around −2.25 °C to −2.75 °C and −4.25 °C to −4.75 °C, respectively. RF and XGBoost took about 7.3 ms and 5.5 ms time per image respectively, which indicates that these models can be used in real-time applications. Our study shows that ML models using IRT imagery can be used as an automatic real-time tool to accurately detect the critical temperatures for frost formation