Machine Learning Approach to Forecast Average Weather Temperature of Bangladesh

Weather prediction is gaining popularity very rapidly in the current era of Artificial Intelligence and Technologies It is essential to predict the temperature of the weather for some time In this research paper we tried to find out the pattern of the average temperature of Bangladesh per year as well as the average temperature per season We used different machine learning algorithms to predict the future temperature of the Bangladesh region In the experiment we used machine learning algorithms such as Linear Regression Polynomial Regression Isotonic Regression and Support Vector Regressor Isotonic Regression algorithm predicts the training dataset most accurately but Polynomial Regressor and Support Vector Regressor predicts the future average temperature most accuratel

Collaboration and Innovation in Food Industry

In the dynamic economic environment where knowledge is vastly distributed companies can no longer rely on their own research and are pushed to utilize outside sources to sustain growth. At the same time food industry involves large number of horizontal and vertical relationships, the very dynamic nature of these relationships play role in innovation. In order to fully capitalize on supplier-customer collaboration it becomes vital to understand the dynamic relation between packaging and processing industry and need to operate closely, develop ways to identify good partners and create & maintain fruitful collaboration. Based on this understanding the primary purpose of the research is to study interactions and relations between stakeholders in food industry, to gain an understanding of the driving forces for development in food processing and packaging technologies.This understanding can then be utilized to identify the barriers for collaboration.Title Collaboration and Innovation in Food Industry - Study on collaboration of packaging and process equipment industry with food manufacturing. Author Mustafa Ali Ashfaq Bombaywala Supervisor Malin Göransson, PhD Student at Division of Packaging Logistics, Department of Design Sciences, Faculty of Engineering, Lund University. Issue of study In the dynamic economic environment where knowledge is vastly distributed companies can no longer rely on their own research and are pushed to utilize outside sources to sustain growth. At the same time food industry involves large number of horizontal and vertical relationships, the very dynamic nature of these relationships play role in innovation. In order to fully capitalize on supplier-customer collaboration it becomes vital to understand the dynamic relation between packaging and processing industry and need to operate closely, develop ways to identify good partners and create & maintain fruitful collaboration. However the research on collaboration with packaging and processing equipment industries as well as academia is rather limited. Purpose The primary purpose of the research is to study interactions and relations between stakeholders in food industry, to gain an understanding of the driving forces for development in food processing and packaging technologies. Also gain insight into the innovation process at major Packaging solution provider (PSP) and Process equipment manufacturers (PEM), their interaction, collaboration and information sharing with food manufacturing companies (FMC). This understanding can then be utilized to identify the barriers for collaboration. Method The research follows an inductive approach which starts with a premise and structure is built around the conceptual framework and the research objectives. Secondary data collected through literature survey was utilized to develop a conceptual model. Primary data was collected through interviews with experts from the industry and academia who have experience in working with innovation and collaboration. A non-probability sampling technique was adopted and II Semi-structured interview technique was followed. The interviews were transcribed to text and categorized under common themes which for analysis and comparison. To ascertain the credibility of the data it was triangulated and compared to literature. Conclusion The views of industry experts strongly reflect that the role of suppliers of processing and packaging in food industry is “contractual” in nature, whereas ingredient suppliers tend to be more mature partners in the innovation process. The innovation process at major food machinery and packaging companies corresponds well to the ‘food-machinery framework’ of open innovation (Bigliardi et al., 2010). It is apparent that food industry is taking steps to integrate external knowledge sources in the innovation process, still suppliers continues to play limited strategic role in innovation. Some barriers to collaboration were identified and they can be grouped into two types: technical and perspective. Technical factors constitute lack of technical expertise amongst food manufacturer, requirement for legal framework and difficulty in predicting future needs. But the more imperative barriers are lack of trust, skepticism about new technologies and conflict of interest Trust continues to be the major barrier for collaboration and further research needs to be focused on this aspect

Karo Kari : the murder of honour in Sindh Pakistan : an ethnographic study

This paper aims to discuss the wider context, in which honour murders occur, the social structures which contribute to the occurrence and perpetuation of the practice of honour murders. An ethnographic fieldwork was conducted in Jacobabad Sindh, Pakistan. The study found that honour murders were not solely driven by customs and traditions, but also by a feudal culture, male-dominated social structures, the complicit role of state institutions and law enforcement agencies and a web of vested interests. Therefore, honour murders may be prevented by reducing the influence and interference of feudal lords on state institutions, in particular law enforcement agencies, and by promoting education that challenges a patriarchal and feudal mind-set in the community

Structural and functional analysis of seeligeriolysin O by homology modeling

Seeligeriolysin O (LSO) is a cholesterol-dependant cytolysin of Listeria seeligeri. These toxins are produced by various species of Gram-positive bacteria, including members of the genera Streptococcus, Clostridium, and Listeria. Apart from the cytolytic, LSO has been reported to perform cytokine-inducing activity as well. The present study deals with the prediction of three dimensional model, as well as structural and functional analysis of Seeligeriolysin O. MODELLER9 v8 was used for building the homology model. These predicted 3-dimensional models were evaluated with ProSa and PROCHECK software, and the best 3-dimensional models were selected. Multiple alignment was performed with CLUSTALX. Based on the similarity of predicted three dimensional structure of seeligeriolysin O with perfringolysin O, the seeligeriolysin might have similar structure and function with the later. The predicted three dimensional model of seeligeriolysin O had extended rod shaped structure, having ample beta sheets arranged in four domains. The C-terminal region of seeligeriolysin O might have function similar to perfringolysin O. It has been predicted that seeligeriolysin O insertion occurs more readily in an environment having loosely packed lipid.Key words: Bacterial toxins, tryptophan, Perfringolysin, Listeria seeligeri, cholesterol-dependant cytolysins and domain 4, target protein, template protein

Fault Tree Analysis for Reliability Analysis of Wind Turbines Considering the Imperfect Repair Effect

Wind turbines are complex and expensive equipment, requiring high reliability and low maintenance costs. However, most of the existing fault tree analysis (FTA) methods for reliability analysis of wind turbines assume that the repair of wind turbines can restore them to as good as new condition, which is called perfect repair. This assumption may not be realistic in practice, as the repair may not fully recover the original performance or functionality of the equipment or may introduce new defects or errors. This phenomenon is called imperfect repair, which can reduce the reliability of wind turbines over time. To consider the imperfect repair effect in reliability analysis, we present a new FTA approach in this study. In order to predict and assess the failure intensity and dependability of wind turbines under imperfect repair, the proposed FTA technique uses a log-linear proportional intensity model (LPIM). Failure probability, failure rate, and mean time to failure can all be improved with the suggested FTA technique for wind turbines operating with poor repair. The proposed FTA method can also identify the critical components or failure modes most affected by the imperfect repair effect and suggest preventive maintenance actions to improve the reliability of wind turbines. We demonstrate the applicability and effectiveness of the proposed FTA method through a case study on a real or hypothetical wind turbine system under imperfect repair. The findings indicate that the proposed FTA method offers a more precise and authentic assessment of the reliability of wind turbines in the presence of imperfect repair, in contrast to existing FTA methods that assume perfect repair. The findings also demonstrate that the electrical system, hydraulic system, gearbox, generator, and blade are the most critical components or failure modes affecting the system's reliability

Machine Learning Approach to Forecast Average Weather Temperature of Bangladesh

Weather prediction is gaining popularity very rapidly in the current era of Artificial Intelligence and Technologies. It is essential to predict the temperature of the weather for some time. In this research paper, we tried to find out the pattern of the average temperature of Bangladesh per year as well as the average temperature per season. We used different machine learning algorithms to predict the future temperature of the Bangladesh region. In the experiment, we used machine learning algorithms, such as Linear Regression, Polynomial Regression, Isotonic Regression, and Support Vector Regressor. Isotonic Regression algorithm predicts the training dataset most accurately, but Polynomial Regressor and Support Vector Regressor predicts the future average temperature most accurately

Isolation of Caffeine from Carbonated Beverages

The work presented on the isolation of naturally occurring alkaloid from carbonated beverages. The extensive presence of caffeine in different plants plays an important role in the long-standing acceptance of caffeine-containing products. Caffeine (3,7-dihydro-1, 3,7-trimethyl-1H-purine-2,6-dione or 1,3,7-trimethylxanthine) is an alkaloid belongs to Methylxanthine family. Liquid-liquid extraction methods were used in the assay of research work. Chloroform was taken as extracting solvent. Solid residue of caffeine was recrystallized from 95% ethanol using 5ml/gram (5ml per gram). It is declared to raise caffeine, effects a number of different drugs include Paracetamol, Benzodiazepines and Aspirin and amount of plasma free Fatty acids increases. While inform that in regular sleeping interaction caffeine take place and raise the absorption of certain drugs. Changes in drug metabolizing enzymes, acts as an agent in a microsomal system of the body. The highest amount of caffeine dry crystal is extracted in sting sample while the 7up sample is free from caffeine

New approach to forecasting agro-based statistical models

This paper uses various forecasting methods to forecast future crop production levels using time series data for four major crops in Pakistan: wheat, rice, cotton and pulses. These different forecasting methods are then assessed based on their out-of-sample forecast accuracies. We empirically compare three methods: Box- Jenkins’ ARIMA, Dynamic Linear Models (DLM) and exponential smoothing. The best forecasting models are selected from each of the methods by applying them to various agricultural time series in order to demonstrate the usefulness of the models and the differences between them in an actual application. The forecasts obtained from the best selected exponential smoothing models are then compared with those obtained from the best selected classical Box-Jenkins ARIMA models and DLMs using various forecast accuracy measures

Optimizing Lead-free MASnBr3 Perovskite Solar Cells for High-Efficiency and Long-Term Stability Using Graphene and Advanced Interface Layers

Perovskite solar cells (PSCs) have garnered significant attention in the scientific community due to their rapid increase in performance. Inorganic perovskite devices have been noted for their high performance and long-term stability. This study introduces a device optimization process guided by modeling to produce high-efficiency PSCs using lead-free n-i-p methylammonium tin bromide (MASnBr3) materials. We have thoroughly examined the impact of both the absorber and interface layers on the optimized structure. Our approach utilized graphene as the interface layer between the hole transport and absorber layers. We employed zinc oxide (ZnO)/Al and 3C-SiC as interface layers between the absorber and electron transport layers. The optimization process involved adjusting the thicknesses of the absorber layer and interface layers and minimizing defect densities. Our proposed optimized device structure, ZnO/3C-SiC/MASnBr3/graphene/CuO/Au, demonstrates theoretical power conversion efficiencies of 31.97%, fill factors of 89.38%, a current density of 32.54 mA/cm2, a voltage of 1.112 V, and a quantum efficiency of 94%. This research underscores the ability of MASnBr3 as a nontoxic perovskite material for sustainable energy from renewable sources' applications