The modern landscape of managing effects for the working programmer

The management of side effects is a crucial aspect of modern programming, especially in concurrent and distributed systems. This thesis analyses different approaches for managing side effects in programming languages, specifically focusing on unrestricted side effects, monads, and algebraic effects and handlers. Unrestricted side effects, used in mainstream imperative programming languages, can make programs difficult to reason about. Monads offer a solution to this problem by describing side effects in a composable and referentially transparent way but many find them cumbersome to use. Algebraic effects and handlers can address some of the shortcomings of monads by providing a way to model effects in more modular and flexible way. The thesis discusses the advantages and disadvantages of each of these approaches and compares them based on factors such as expressiveness, safety, and constraints they place on how programs must be implemented. The thesis focuses on ZIO, a Scala library for concurrent and asynchronous programming, which revolves around a ZIO monad with three type parameters. With those three parameters ZIO can encode the majority of practically useful effects in a single monad. ZIO takes inspiration from algebraic effects, combining them with monadic effects. The library provides a range of features, such as declarative concurrency, error handling, and resource management. The thesis presents examples of using ZIO to manage side effects in practical scenarios, highlighting its strengths over other approaches. The applicability of ZIO is evaluated by implementing a server side application using ZIO, and analyzing observations from the development process

Scanning of Organic Agricultural Products From Production and Market Side

This research aims to: scan organic agricultural products from the production side, by comparing production and revenue through the application of two kinds organic fertilizers formulation; and scan from the market side, by observing marketing channels, and consumer responses to organic products produced. Both organic fertilizer formulations are obtained through the Linear Programming (LP) application that was conducted in May 2018-July 2018. Research conducted by the method of action research. For plants that need planting sweet corn, spinach, kale, and beans by applying both formulations in two experimental plots, with an area of respectively 40 m2, 14 m2, 14 m2, and 40 m2. Then, from the market side, crop yields are distributed through institutions involved in the marketing of these commodities, and then market data is obtained. The results show that, in terms of farm production and analysis, the provision of production can be done at a more competitive price, as indicated by high crop productivity, and minimal fertilizer costs; and from the market side, shows that 80% of distribution takes place through channels that require the services of intermediary traders, and consumer responses show that organic farming products are better than conventional agricultural products that they consumed previously.Introductio

Mixed-integer-linear-programming-based energy management system for hybrid PV-wind-battery microgrids: Modeling, design, and experimental verification

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMicrogrids are energy systems that aggregate distributed energy resources, loads, and power electronics devices in a stable and balanced way. They rely on energy management systems to schedule optimally the distributed energy resources. Conventionally, many scheduling problems have been solved by using complex algorithms that, even so, do not consider the operation of the distributed energy resources. This paper presents the modeling and design of a modular energy management system and its integration to a grid-connected battery-based microgrid. The scheduling model is a power generation-side strategy, defined as a general mixed-integer linear programming by taking into account two stages for proper charging of the storage units. This model is considered as a deterministic problem that aims to minimize operating costs and promote self-consumption based on 24-hour ahead forecast data. The operation of the microgrid is complemented with a supervisory control stage that compensates any mismatch between the offline scheduling process and the real time microgrid operation. The proposal has been tested experimentally in a hybrid microgrid at the Microgrid Research Laboratory, Aalborg University.Peer ReviewedPostprint (author's final draft

INTERNET MAPPING

Geographical Information System (GIS) are methods to visualize, manipulate, analyze, and display spatial data. GIS application software such as Arc View or Maplnfo allows the integration of such GIS maps with programming language software such as Visual Basic. As we all know, internet has been a major information distribution channel. If we could integrate both GIS map and serves it in a web browser medium, we could provide GIS applications online, the users could manipulate and handle each map with their own choice. In order to create the Internet map that allows GIS map, Arc IMS will be use. ArcIMS runs in a distributed environment and consists of both client and server components. The ArcIMS HTML Viewer and ArcIMS Java Viewers are client-side components. The ArcIMS Spatial Server, ArcIMS Application Server, ArcIMS Application Server Connectors, and ArcIMS Manager are server-side components. As a conclusion, Internet mapping benefits the user by clearly indication GIS data to them

Database Access through Java Technologies

As a high level development environment, the Java technologies offer support to the development of distributed applications, independent of the platform, providing a robust set of methods to access the databases, used to create software components on the server side, as well as on the client side. Analyzing the evolution of Java tools to access data, we notice that these tools evolved from simple methods that permitted the queries, the insertion, the update and the deletion of the data to advanced implementations such as distributed transactions, cursors and batch files. The client-server architectures allows through JDBC (the Java Database Connectivity) the execution of SQL (Structured Query Language) instructions and the manipulation of the results in an independent and consistent manner. The JDBC API (Application Programming Interface) creates the level of abstractization needed to allow the call of SQL queries to any DBMS (Database Management System). In JDBC the native driver and the ODBC (Open Database Connectivity)-JDBC bridge and the classes and interfaces of the JDBC API will be described. The four steps needed to build a JDBC driven application are presented briefly, emphasizing on the way each step has to be accomplished and the expected results. In each step there are evaluations on the characteristics of the database systems and the way the JDBC programming interface adapts to each one. The data types provided by SQL2 and SQL3 standards are analyzed by comparison with the Java data types, emphasizing on the discrepancies between those and the SQL types, but also the methods that allow the conversion between different types of data through the methods of the ResultSet object. Next, starting from the metadata role and studying the Java programming interfaces that allow the query of result sets, we will describe the advanced features of the data mining with JDBC. As alternative to result sets, the Rowsets add new functionalities that enhance the flexibility of the applications. These are analyzed and the approach is described

Scalable Parallel Computers for Real-Time Signal Processing

We assess the state-of-the-art technology in massively parallel processors (MPPs) and their variations in different architectural platforms. Architectural and programming issues are identified in using MPPs for time-critical applications such as adaptive radar signal processing. We review the enabling technologies. These include high-performance CPU chips and system interconnects, distributed memory architectures, and various latency hiding mechanisms. We characterize the concept of scalability in three areas: resources, applications, and technology. Scalable performance attributes are analytically defined. Then we compare MPPs with symmetric multiprocessors (SMPs) and clusters of workstations (COWs). The purpose is to reveal their capabilities, limits, and effectiveness in signal processing. We evaluate the IBM SP2 at MHPCC, the Intel Paragon at SDSC, the Gray T3D at Gray Eagan Center, and the Gray T3E and ASCI TeraFLOP system proposed by Intel. On the software and programming side, we evaluate existing parallel programming environments, including the models, languages, compilers, software tools, and operating systems. Some guidelines for program parallelization are provided. We examine data-parallel, shared-variable, message-passing, and implicit programming models. Communication functions and their performance overhead are discussed. Available software tools and communication libraries are also introducedpublished_or_final_versio

From Controlled Data-Center Environments to Open Distributed Environments: Scalable, Efficient, and Robust Systems with Extended Functionality

The past two decades have witnessed several paradigm shifts in computing environments. Starting from cloud computing which offers on-demand allocation of storage, network, compute, and memory resources, as well as other services, in a pay-as-you-go billingmodel. Ending with the rise of permissionless blockchain technology, a decentralized computing paradigm with lower trust assumptions and limitless number of participants. Unlike in the cloud, where all the computing resources are owned by some trusted cloud provider, permissionless blockchains allow computing resources owned by possibly malicious parties to join and leave their network without obtaining permission from some centralized trusted authority. Still, in the presence of malicious parties, permissionlessblockchain networks can perform general computations and make progress. Cloud computing is powered by geographically distributed data-centers controlled and managed by trusted cloud service providers and promises theoretically infinite computing resources. On the other hand, permissionless blockchains are powered by open networks of geographically distributed computing nodes owned by entities that are not necessarily known or trusted. This paradigm shift requires a reconsideration of distributed data management protocols and distributed system designs that assume low latency across system components, inelastic computing resources, or fully trusted computing resources.In this dissertation, we propose new system designs and optimizations that address scalability and efficiency of distributed data management systems in cloud environments. We also propose several protocols and new programming paradigms to extend the functionality and enhance the robustness of permissionless blockchains. The work presented spans global-scale transaction processing, large-scale stream processing, atomic transaction processing across permissionless blockchains, and extending the functionality and the use-cases of permissionless blockchains. In all these directions, the focus is on rethinking system and protocol designs to account for novel cloud and permissionless blockchain assumptions. For global-scale transaction processing, we propose GPlacer, a placement optimization framework that decides replica placement of fully and partial geo-replicated databases. For large-scale stream processing, we propose Cache-on-Track (CoT) an adaptive and elastic client-side cache that addresses server-side load-imbalances that occur in large-scale distributed storage layers. In permissionless blockchain transaction processing, we propose AC3WN, the first correct cross-chain commitment protocol that guarantees atomicity of cross-chain transactions. Also, we propose TXSC, a transactional smart contract programming framework. TXSC provides smart contract developers with transaction primitives. These primitives allow developers to write smart contracts without the need to reason about the anomalies that can arise due to concurrent smart contract function executions. In addition, we propose a forward-looking architecture that unifies both permissioned and permissionless blockchains and exploits the running infrastructure of permissionless blockchains to build global asset management systems

Modifications To Web Processing Service Standard For Client-Side Geoprocessing

Nowadays we see the rapid growth of solutions number for geospatial data processing in the Web (i.e. geoprocessing). One of the main trends of Web geotechnologies evolution is the transition from Web map applications to the Web GIS applications, which are supplement the maps delivery with the analytic tools providing to the end user through Web interface. In fact, the only general open standard describes implementation rules for Web geoprocessing services. This is the Open Geospatial Consortium Web Processing Service standard, which is fully server-oriented. Moreover, the vast majority of currently used solutions (both open source and proprietary) are server-oriented, i.e. assume the server resources only as the computational resource. However, some researchers underline that it is possible way to transmit the executable code to the client for client-side computations and geoprocessing. Also, some general Web architecture concepts assume the effectiveness of client-side computations, e.g. Fog Computing concept. Our practical experience also shows that in some cases it is useful to have ability of client-side geoprocessing, which is not opposite but complement technology to the server-side processing technologies. In addition, we believe that it is more useful to have the ability to run the same processing tool by choice on server or client side. We name such double-sided services as Hybrid Geoprocessing Web Services. We study and discuss the approaches to gap filling in client-side geoprocessing general schema. For this purpose, we implemented previously the getProcess request as addition to the WPS protocol. Additionally at the previous steps of our study, we proposed a possible structure of getProcess request and draft XML file structure for its response, which describes the list of executable resources and their dependencies. Currently we working on detailed methodology of processing tools implementation and testing. We use the Python programming language as primary development tool, because of its applicability to build both server- and client-side crossplatform processing tools using single core program code. We use Python also for implementation of needed infrastructure components, such as HGWS server that supports the getProcess request/response performing, and client-side Runtime Environment that provides executable code orchestration on the client. Achieved results need to be discussed widely and carefully. However, main conclusion of our current work is that client-side geoprocessing schema in general could be relatively simple and compatible backward with current standards. The HGWS concept is applicable when implementing client-side geoprocessing Web services in small-scale projects and could be the entering point for study of distributed geoprocessing systems implementation