A Cost-effective Multispectral Sensor System for Leaf-Level Physiological Traits

With the concern of the global population to reach 9 billion by 2050, ensuring global food security is a prime challenge for the research community. One potential way to tackle this challenge is sustainable intensification; making plant phenotyping a high throughput may go a long way in this respect. Among several other plant phenotyping schemes, leaf-level plant phenotyping needs to be implemented on a large scale using existing technologies. Leaf-level chemical traits, especially macronutrients and water content are important indicators to determine crop’s health. Leaf nitrogen (N) level, is one of the critical macronutrients that carries a lot of worthwhile nutrient information for classifying the plant’s health. Hence, the non-invasive leaf’s N measurement is an innovative technique for monitoring the plant’s health. Several techniques have tried to establish a correlation between the leaf’s chlorophyll content and the N level. However, a recent study showed that the correlation between chlorophyll content and leaf’s N level is profoundly affected by environmental factors. Moreover, it is also mentioned that when the N fertilization is high, chlorophyll becomes saturated. As a result, determining the high levels of N in plants becomes difficult. Moreover, plants need an optimum level of phosphorus (P) for their healthy growth. However, the existing leaf-level P status monitoring methods are expensive, limiting their deployment for the farmers of low resourceful countries. The aim of this thesis is to develop a low-cost, portable, lightweight, multifunctional, and quick-read multispectral sensor system to sense N, P, and water in leaves non-invasively. The proposed system has been developed based on two reflectance-based multispectral sensors (visible and near-infrared (NIR)). In addition, the proposed device can capture the reflectance data at 12 different wavelengths (six for each sensor). By deploying state of the art machine learning algorithms, the spectroscopic information is modeled and validated to predict that nutrient status. A total of five experiments were conducted including four on the greenhouse-controlled environment and one in the field. Within these five, three experiments were dedicated for N sensing, one for water estimation, and one for P status determination. In the first experiment, spectral data were collected from 87 leaves of canola plants, subjected to varying levels of N fertilization. The second experiment was performed on 1008 leaves from 42 canola cultivars, which were subjected to low and high N levels, used in the field experiment. The K-Nearest Neighbors (KNN) algorithm was employed to model the reflectance data. The trained model shows an average accuracy of 88.4% on the test set for the first experiment and 79.2% for the second experiment. In the third and fourth experiments, spectral data were collected from 121 leaves for N and 186 for water experiments respectively; and Rational Quadratic Gaussian Process Regression (GPR) algorithm is applied to correlate the reflectance data with actual N and water content. By performing 5-fold cross-validation, the N estimation shows a coefficient of determination (R^2) of 63.91% for canola, 80.05% for corn, 82.29% for soybean, and 63.21% for wheat. For water content estimation, canola shows an R^2 of 18.02%, corn of 68.41%, soybean of 46.38%, and wheat of 64.58%. Finally, the fifth experiment was conducted on 267 leaf samples subjected to four levels of P treatments, and KNN exhibits the best accuracy, on the test set, of about 71.2%, 73.5%, and 67.7% for corn, soybean, and wheat, respectively. Overall, the result concludes that the proposed cost-effective sensing system can be viable in determining leaf N and P status/content. However, further investigation is needed to improve the water estimation results using the proposed device. Moreover, the utility of the device to estimate other nutrients as well as other crops has great potential for future research

Understanding and Managing Non-functional Requirements for Machine Learning Systems

Background: Machine Learning (ML) systems learn using big data and solve a wide range of prediction and decision making problems that would be difficult to solve with traditional systems. However, increasing use of ML in complex and safety-critical systems has raised concerns about quality requirements, which are defined as Non-Functional requirements (NFRs). Many NFRs, such as fairness, transparency, explainability, and safety are critical in ensuring the success and acceptance of ML systems. However, many NFRs for ML systems are not well understood (e.g., maintainability), some known NFRs may become more important (e.g., fairness), while some may become irrelevant in the ML context (e.g., modularity), some new NFRs may come into play (e.g., retrainability), and the scope of defining and measuring NFRs in ML systems is also a challenging task.Objective: The research project focuses on addressing and managing issues related to NFRs for ML systems. The objective of the research is to identify current practices and challenges related to NFRs in an ML context, and to develop solutions to manage NFRs for ML systems.Method: We are using design science as a base of the research method. We carried out different empirical methodologies–including interviews, survey, and a part of systematic mapping study to collect data, and to explore the problem space. To get in-depth insights on collected data, we performed thematic analysis on qualitative data and used descriptive statistics to analyze qualitative data. We are working towards proposing a quality framework as an artifact to identify, define, specify, and manage NFRs for ML systems.Findings: We found that NFRs are crucial and play an important role for the success of the ML systems. However, there is a research gap in this area, and managing NFRs for ML systems is challenging. To address the research objectives, we have identified important NFRs for ML systems, and NFR and NFR measurement-related challenges. We also identified preliminary NFR definition and measurement scope and RE-related challenges in different example contexts.Conclusion: Although NFRs are very important for ML systems, it is complex and difficult to define, allocate, specify, and measure NFRs for ML systems. Currently the industry and research is does not have specific and well organized solutions for managing NFRs for ML systems because of unintended bias, the non-deterministic behavior of ML, and expensive and time-consuming exhaustive testing. Currently, we are working on the development of a quality framework to manage (e.g., identify important NFRs, scoping and measuring NFRs) NFRs in the ML systems development process

THE MATHEMATICS LEARNING MODEL’S FOR EARLY GRADE STUDENTS: CONTEXTUAL OR PROBLEM-BASED LEARNING

This research aimed to know the appropriate learning model was applied in early grade mathematics learning by showed the differences in mathematics learning outcomes of students who use contextual teaching-learning and problem-based learning. This research method was quasi-experiment with a posttest only control group design. The population of this research were 3rd grade students of SD Negeri Wonosari, Semarang City. The sampling technique was used to cluster random sampling with two experimental classes. The first experimental class used contextual teaching-learning and the second experimental class used problem-based learning. The data were analyzed using a one-way ANOVA test.  The results showed that there were differences between student mathematics learning outcomes in three classes. Student learning outcomes that applied contextual teaching and learning were better than mathematics learning outcomes of students who applied problem-based learning or conventional learning because contextual teaching and learning more emphasis on meaningful learning from real life that made it easier for students to understand. The teacher should be able to choose the appropriate model to be applied in mathematics learning following the grade level so that mathematics learning in the early grade could run optimally. So, this model was suitable to apply in early grade mathematics learning

The Impact of Corruption on Economic Development of Bangladesh:Evidence on the Basis of an Extended Solow Model

The purpose of this thesis is to examine the long run relationship between economic growth and corruption in Bangladesh over the period 1984-2008. In this study, I have extended the neoclassical model of economic growth by Solow (1956) including human capital and public sector explicitly at first. Then, I have incorporated corruption into the augmented model using a specific functional form for total factor productivity and three other channels to show impact of corruption on real GDP per capita. To investigate empirically the existence of a long run relationship or co-integration between corruption and real GDP per capita, I have used Auto-Regressive Distributed Lag (ARDL) Bounds Test method. The results of co-integration test confirms that there is a long run relation among corruption, GDP per capita and other determinants of GDP over the study period. The long run estimates indicate that corruption has direct negative impact on per capita GDP i.e. economic development of Bangladesh.ARDL Bounds test; Co-Integration; Corruption; Economic Growth; Neoclassical Model

Does Digitalization Matter for Grievance Redress Mechanism? An Analysis of the E-Government Procurement in the Local Government

Public procurement denotes the attainment of goods, works, or services required by governments through contracts. Public procurement is usually plagued with covert practices and exclusive forms of corruption in Bangladesh. The cost of development finance is much higher in public procurement than that of others in Bangladesh. There have been no unified rules or laws regarding government purchases since 2006. The Government of Bangladesh enacted its ever first act of public procurement, the Public Procurement Act, 2006 (PPA, 2006), and the Public Procurement Rules, 2008 (PPR, 2008). The procurement entity must disseminate procurement information through electronic medium i.e. Electronic Government Procurement (hereinafter e-GP). Despite passing over an era since its enactment, the role of the PPA, 2006 remains seriously under-researched area, specifically there is no research on the issue of the grievance redress mechanisms. The study has been done using a qualitative case study methodology, backed by both primary and secondary documentary analysis. Based on empirical data, this paper explained the role of digitalization in the process of government procurement and depicted changes that have been brought by the implementation of e-GP in government purchases. However, it concluded that the grievance redress mechanism in the process of procurement is only existing in the policy papers which need mandatory implementation for a transparent and accountable governance system. Despite the significant changes and up-gradation in digital government procurement, it is hardly found that the GRM process is digitalized. And the mass tenderers are yet to be acquainted with the improvised system apart from the consistent efforts of the government

The Impact of Islamic Civilization on the European Intellectual Awakening: An Analytical Study

Islam, a religion originating with the command 'Iqra' (Read), places a significant emphasis on learning and the pursuit of various sciences, considering it a duty for all Muslims from infancy to old age. Consequently, the cultivation of science and knowledge assumed a central role in all aspects of Muslim affairs. In stark contrast to the ignorance and darkness prevailing in Europe during the Middle Ages, where matters were viewed through the ecclesiastical lens, the flourishing Islamic civilization emerged. The relentless endeavours of Muslim scholars propelled a range of sciences, including natural sciences, mathematics, astronomy, philosophy, history, literature, and geology, to unprecedented heights. Witnessing the advancements of Muslims in diverse scientific domains, Europeans, mired in their own scientific stagnation, regarded Muslims as apostates, accusing them of prioritizing materialistic and worldly pursuits, hindering salvation. The scientific accomplishments of Muslims profoundly influenced the Renaissance and the awakening of Europe. At a time when reason and knowledge were confined by the Church, it was Muslim scientists who not only translated the science and philosophy of the Greeks into Arabic but also preserved, developed, and expanded these intellectual pursuits. Following the Crusades, Muslim knowledge and technology permeated the Western world through interactions in Spain, Sicily, and Italy involving merchants, soldiers, and translators, laying the groundwork for the European Renaissance and intellectual awakening

EKSTRAKSI TINGGI BANGUNAN DENGAN MENGGUNAKAN FOTO UDARA ORTHO DAN DATA LIDAR

Penginderaan jauh semakin berkembang khususnya dalam hal resolusi spasial. Foto udara ortho banyak digunakan karena resolusi spasial tinggi dan telah mengalami koreksi sehingga aspek relief displacement dapat dikurangi. Kemudian data LiDAR sebagai data detil yang dapat memberikan informasi ketinggian objek dengan akurasi tinggi.Penelitian ini bertujuan untuk melakukan ekstraksi data geometri bangunan, yaitu tinggi bangunan dan mengetahui akurasi dari ekstraksi informasi tinggi bangunan. Tahap awal dilakukan interpertasi tipe atap bangunan dari foto udara ortho. Ekstraksi tinggi bangunan dilakukan dengan menghitung nilai ketinggian maksimum bangunan dari N-DSM yang didapatkan dari kalkulasi DSM dan DTM LiDAR. Nilai tinggi tersebut dikoreksi dengan nilai tinggi objek atap bangunan berdasarkan tipe atapnyaHasil perhitungan uji akurasi cukup baik. Uji akurasi pemetaan dengan menggunakan data pengukuran lapangan menunjukkan bahwa akurasi ekstraksi tinggi bangunan sebesar 86.63%

The role of Islamic bond (sukuk) in green and sustainable Islamic finance: its connotation on social prosperity by realizing ESG

Abstract: Islamic finance offers a different route to meet the large funding requirements for sustainable development projects and activities in poor nations because of its social and moral ethos and asset-backed nature. Islamic finance and Environmental Social and Governance (ESG) investing often share guiding principles, which makes them complementary as capital-raising and investment strategies. By adding a second layer of governance (Shari’ah) that guarantees the ring-fencing of issue profits and directs them toward projects that align with Shari’ah rules and ESG criteria, Islamic finance encourages sustainable development and is in line with those requirements. Promoting financial inclusion for individuals who might refrain from using the financial system due to ethical considerations or a lack of access, are two significant obstacles to escaping poverty. This study aims to demonstrate how it can draw in funds from sources now unexplored by traditional green and sustainable finance. Due to the fundamental principle of avoiding speculative and toxic financial products based on derivatives, generally full collateralization due to its asset-backed structure, and intimate ties to the actual economy, it can preserve sustainability and stability during financial crises. Proceeds from green, sustainability, and social bonds are meant to fund legitimate initiatives or assets that support particular goals. Even though these bonds are not always asset-backed or asset-based, the ESG bond framework creates a strong link between the capital raised and the issuer's underlying strategy and asset mix, which is consistent with the fundamentals of Islamic financing. In order to accomplish this goal, a systematic literature review methodology is used in this study to explore the fact that Green Sukuk (bonds) are currently being issued with the goal of funding environmentally friendly, climate-resilient, and sustainable growth. The market's organic growth, investors' growing interest in moral and ethical investing, the stringent capital requirements for infrastructure project financing, and the growing trend toward adopting green Sukuk (bonds) have all influenced this development

Exploring the Impact of Nutrition and Physical Activity on Human Metabolism

Background: The human body acquires energy substances through diet and consumes them through exercise, completing the metabolism process. This article aims to analyze the impact of metabolism concerning diet and scientific exercise to emphasize the importance of these factors in promoting physical health. Methods: The research delves into the effects of diet and exercise on energy metabolism, focusing on energy balance, nutrient oxidation, and metabolic flexibility. Various methodologies are employed to measure energy intake and expenditure accurately, crucial for understanding energy homeostasis and developing effective interventions. Findings: Exercise and Diet Influence Metabolism: Physical exercise and dietary interventions play a significant role in influencing energy metabolism, improving metabolic flexibility, and managing cardiometabolic diseases like obesity and diabetes. Energy Metabolism Measurement: Current methodologies for measuring energy intake and expenditure provide valuable insights into energy homeostasis regulation. These methods help researchers conduct high-quality obesity research by assessing various aspects of energy metabolism. Impact of Exercise on Metabolism: Studies suggest that extreme exercise combined with calorie restriction may not lead to sustainable weight loss due to metabolic adaptations like reduced resting metabolic rates. Muscle loss during rapid weight loss can contribute to lower metabolic rates. Dietary Carbohydrates and Exercise: Research highlights the interplay between dietary carbohydrate intake, exercise, appetite regulation, and energy intake. Low-carbohydrate diets combined with exercise show promising effects on body mass reduction and improved fat and carbohydrate metabolism. Conclusion: The analysis underscores the critical role of diet and exercise in human metabolism. By understanding how these factors impact energy balance, nutrient oxidation, and metabolic flexibility, individuals can make informed choices to enhance their physical health through scientific and reasonable lifestyle modifications

Non-Functional Requirements for Machine Learning: An Exploration of System Scope and Interest

Systems that rely on Machine Learning (ML systems) have differing demands on quality—non-functional requirements (NFRs)— compared to traditional systems. NFRs for ML systems may differ in their definition, scope, and importance. Despite the importance of NFRs for ML systems, our understanding of their definitions and scope—and of the extent of existing research—is lacking compared to our understanding in traditional domains.Building on an investigation into importance and treatment of ML system NFRs in industry, we make three contributions towards narrowing this gap: (1) we present clusters of ML system NFRs based on shared characteristics, (2) we use Scopus search results— as well as inter-coder reliability on a sample of NFRs—to estimate the number of relevant studies on a subset of the NFRs, and (3), we use our initial reading of titles and abstracts in each sample to define the scope of NFRs over parts of the system (e.g., training data, ML model). These initial findings form the groundwork for future research in this emerging domain