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For fixed-application scenarios in embedded soft-realtime computing, the ideal (w.r.t. energy consumption) heterogeneous multi-core CPU design within given chip dimensions can be configured by composing it from given pre-layouted, rectangular chip submodules for each of a number K > 1 of core types, where K in practice is a small constant. For example, K = 2 in traditional ARM big.LITTLE designs. Nevertheless, even better solutions might be achieved for K > 2, and many feasible combinations can exist. For this purpose, we investigate finding all combinations of instances of K > 1 different types of given axis-parallel rectangles that can be packed within a given fixed-size 2D rectangle, and we propose two new packing heuristics: the corner heuristic for K Funding Agencies|ELLIIT (Excellence Center at Linkoping - Lund in Information Technology), through the project C05 GPAI (General-Purpose AI Computing)

To evaluate the effectiveness of the simple flowcharts for practical periodontal diagnosis based on the 2018 new periodontal classification.In this randomized two-period crossover trial, 152 participants assigned a periodontal diagnosis for 25 case scenarios using the flowcharts (test group) and using the consensus reports (control group). Self-confidence questionnaires were completed at the end of each period. Questionnaires about the perception of the flowcharts were completed at the end of the study. The accuracy of the diagnosis, the time used, and participants' confidence in assigning a diagnosis between using the flowcharts and using the consensus reports were compared.The mixed model analysis indicated that using the flowcharts was associated with a significant increase in the accuracy of the periodontal diagnosis, compared with using the consensus reports (P = 0.008). After the subgroup analysis, a significant increase in the accuracy of the full diagnosis and the accuracy of periodontal health, gingivitis, and periodontitis identification was seen in only the dental students. The time used for making the diagnosis using flowcharts and using consensus reports was similar. The self-confidence scores significantly increased when using flowcharts for diagnosis.The simple flowcharts are user-friendly tools that can aid clinicians in accurately assigning a periodontal diagnosis and improve clinicians' confidence in assigning a periodontal diagnosis using the new periodontal classification.The flowcharts can be used for periodontal screening and assigning periodontal diagnosis using the new classification in routine practice.

BitTorrent protokol propisuje tehnike za dijeljenje velikog broja velikih datoteka velikom broju korisnika, u mreži ravnopravnih sudionika. Ovaj rad opisuje oblikovanje i implementaciju jednostavnog BitTorrent klijenta, uporabom programskog jezika c++ i biblioteka otvorenog koda. The BitTorrent protocol specifies the techniques for distribution of a large number of large files to a large amount of users in a peer to peer network. This paper provides insight into the design and implementation of a simple BitTorrent client, using the c++ programming language and open-source libraries.

A Design Science approach of stability, evolvability, and modularity on a C# software artifact.

Poleg besedilnega dela diplomske naloge je bil izdelan tudi računalniški program. V diplomskem delu predstavimo proces prehoda podjetja na poslovanje v oblaku, kjer podrobneje opišemo potek razvoja in ne programiranja ali tehničnih podrobnosti. Uporaba programske rešitve v gospodarstvu namreč pomeni razvoj, saj uporaba informacijskih tehnologij izboljša učinkovitost poslovanja podjetij. Digitalna preobrazba pa ni nujno enostavna. Dodatno delo z visokimi stroški v začetni dobi zahteva naklonjenost vodilnega kadra. Večja podjetja morajo ustvariti nov oddelek za informacijske tehnologije in s tem dodatna delovna mesta. Digitalno preobrazbo olajšajo ponudniki gostovanj, kajti lastno strojno opremo lahko nadomestimo z gostovanjem. Ta cenovno ugodna spletna storitev nudi strokovno opravljanje IT nalog. To pa je zgolj računalnik nekje drugje – v oblaku. Predstavljen bo stvarni primer podjetja in možnosti uporabe oblaka. Če poznamo ovire in prednosti uporabe spletnih tehnologij, zmoremo prepoznati uporabne rešitve, katere nudi. Skozi besedilo želimo pokazati ustrezno strokoven pristop k razvoju, ki smo se ga naučili tekom študija. The thesis project also includes a computer program. The thesis presents the process of a company’s transition to cloud-based operations, outlining the development process in detail, without delving into programming or technical details. Software solutions drive business growth and improvement, as the use of IT enhances efficiency. The digital transformation can be challenging, with initial costs and the need for management support make the transition less straightforward. Larger companies may need to establish a new information technology department, creating new job opportunities. The cost-effective hosting services offered by providers make the transition easier, as they offer an alternative to owning and maintaining hardware. With the help of these professional IT services, the computer is simply located elsewhere - in the cloud. The thesis presents a real-life example of a company and the potential use of cloud technology. By understanding the challenges and benefits of web technologies, one can identify the best solutions available. The aim of this thesis is to show a professional and well-informed approach to software development, which was gained through the course of study.

Digitalization could provide efficient and cost-effective health and well-being services to the rapidly aging population. However, digital services do not always meet their needs. We investigated the experiences and service needs of older employees by collecting quantitative and qualitative data through a survey (n = 497). The results suggested a negative association between user satisfaction and age during retirement transition. Peak experiences were meaningful, explaining a 26% variation in the overall evaluation of the portal. The negative peak experiences concerned poorly functioning features, and the positive ones the ability to take care of one’s health smoothly and easily. The respondents had high expectations for functionality, efficiency, and ease of use. They wanted more support for self-managing health: controlling weight, sleeping, recovery, and exercising.

Funding Information: We thank Nordplus for funding the series of courses, and the students for the permission to use the course feedback data for publishing papers on the course development and the proposal of the UCD Sprint. Virpi Roto was supported by Business Finland grant 81/31/2020. Marta Larusdottir was granted from Reykjavik University Education Fund to build the supporting website: ucdsprint.com. Publisher Copyright: © 2023 Owner/Author. Exploring innovative ideas for interactive software has its challenges. A new process called the User-Centred Design (UCD) Sprint process has been suggested to support teams in exploring users' needs and the future usage of the software with the active involvement of users. Research study results show great benefits for the sprint participants. The course introduces the UCD Sprint process, and participants practices two steps from the UCD Sprint: the user group analysis and stating user experience goals. This in-person course appeals to researchers and developers interested in exploring their innovative ideas through a user-centered step-by-step process.

In this paper, we are going to consider a current challenge in a robotic software system. We consider a problem, which is the lack of separation of concerns in robotic systems, and propose a software model to address the problem and resolve the current challenges. The core purpose of this paper is to demonstrate the advantages of using separation of concerns principles to create a well-ordered model of independent components that address separated concerns individually. Considering the problem, we developed a software model with the help of a proactive engine to address the challenges. We use robotic operating systems to help us to implement the robot simulator.

This paper presents DistRDF2ML, the generic, scalable, and distributed framework for creating in-memory data preprocessing pipelines for Spark-based machine learning on RDF knowledge graphs. This framework introduces software modules that transform large-scale RDF data into ML-ready fixed-length numeric feature vectors. The developed modules are optimized to the multimodal nature of knowledge graphs. DistRDF2ML provides aligned software design and usage principles as common data science stacks that offer an easy-to-use package for creating machine learning pipelines. The modules used in the pipeline, the hyper-parameters and the results are exported as a semantic structure that can be used to enrich the original knowledge graph. The semantic representation of metadata and machine learning results offers the advantage of increasing the machine learning pipelines’ reusability, explainability, and reproducibility. The entire framework of DistRDF2ML is open source, integrated into the holistic SANSA stack, documented in scala-docs, and covered by unit tests. DistRDF2ML demonstrates its scalable design across different processing power configurations and (hyper-)parameter setups within various experiments. The framework brings the three worlds of knowledge graph engineers, distributed computation developers, and data scientists closer together and offers all of them the creation of explainable ML pipelines using a few lines of code.

Background: This study on the use of personas was carried out during a building trade related software development EU project. Participants were introduced to the personas tool and encouraged to use it in their work in technical development. Methods: A questionnaire was delivered three months after presentation and explanation of the remote tool, inquiring about benefits, drawbacks, intention of using personas in the future, and the role of usage related information as a whole in technical development. Results: Previous knowledge about the tool, perceptions of the tool and its actual use seemed to influence developers in their decision to use it or not. Conclusions: Almost half of the organizations in our study use real user-centric design methods in their technical development; this can ease the path for wider adoption of the methods when needed. One possible solution to strengthen the understanding and adoption of the user-centric methods is to spread information among technical developers, by identifying and using efficient communication channels, such as tailored training. User-centric methods seem to have gained followers in the technical development arena, indicating that this may be a feasible way for further adoption of user centricity in product development.

The quest for a healthier life can be nowadays assisted by digital technology, for example, through digital health interventions. These are types of health campaigns based on the use of technology and digital tools, such as smartphones or web applications. These health interventions can be applied in a multitude of scenarios, such as increasing physical activity or promoting healthy eating. Digital health interventions are complex by design and consist of multiple active components that assist the intervention, for instance, sharing information and education, or using monitoring tools. We define computerized intervention components as active intervention components that can be realized by means of a computerized algorithm or software. This thesis investigates the suitability of three such computerized intervention components: coaching-based strategies, social networks, and behaviour change techniques. In doing so, the suitability of these components is tested and discussed by looking at two distinct concepts, namely, the digital health intervention’s user acceptability and effectiveness. In this project, we have designed and developed two digital health interventions including these computerized components. In addition, we have used the rich datasets from two externally organized digital health interventions, to test the components’ suitability. This research combines a multitude of quantitative and qualitative methods: from thematic analysis and focus groups to agent-based modeling, from statistical analysis to the technical development of complex digital interventions, with a shared aim of answering the question of suitability. The core of this thesis is composed of three parts, each of which focuses on answering the suitability question for the three computerized intervention components of interest. Overall, the work presented in this thesis provides new insights into the complex field of digital health interventions. In addition, our studies offer a set of practical design tips for developers of similar systems, based on the discovered user preferences and concerns. The three investigated components have been applied in previous research in multiple health campaigns, and this thesis presents additional knowledge about their contribution to effective and user-acceptable interventions. We have tackled a small piece of the complex puzzle of developing successful digital health interventions, however, we do expect that this work contributes to the further maturity of advanced interventions.

The quest for a healthier life can be nowadays assisted by digital technology, for example, through digital health interventions. These are types of health campaigns based on the use of technology and digital tools, such as smartphones or web applications. These health interventions can be applied in a multitude of scenarios, such as increasing physical activity or promoting healthy eating. Digital health interventions are complex by design and consist of multiple active components that assist the intervention, for instance, sharing information and education, or using monitoring tools. We define computerized intervention components as active intervention components that can be realized by means of a computerized algorithm or software. This thesis investigates the suitability of three such computerized intervention components: coaching-based strategies, social networks, and behaviour change techniques. In doing so, the suitability of these components is tested and discussed by looking at two distinct concepts, namely, the digital health intervention’s user acceptability and effectiveness. In this project, we have designed and developed two digital health interventions including these computerized components. In addition, we have used the rich datasets from two externally organized digital health interventions, to test the components’ suitability. This research combines a multitude of quantitative and qualitative methods: from thematic analysis and focus groups to agent-based modeling, from statistical analysis to the technical development of complex digital interventions, with a shared aim of answering the question of suitability. The core of this thesis is composed of three parts, each of which focuses on answering the suitability question for the three computerized intervention components of interest. Overall, the work presented in this thesis provides new insights into the complex field of digital health interventions. In addition, our studies offer a set of practical design tips for developers of similar systems, based on the discovered user preferences and concerns. The three investigated components have been applied in previous research in multiple health campaigns, and this thesis presents additional knowledge about their contribution to effective and user-acceptable interventions. We have tackled a small piece of the complex puzzle of developing successful digital health interventions, however, we do expect that this work contributes to the further maturity of advanced interventions.

The evolution of Cloud Computing into a service utility, along with the pervasive adoption of the IoT paradigm, has promoted a significant growth in the need of computational and storage services. The traditional use of cloud services, focused on the consumption of one provider, is not valid anymore due to different shortcomings being the risk of vendor lock-in a critical. We are assisting to a change of paradigm, from the usage of a single cloud provider to the combination of multiple cloud service types, affecting the way in which applications are designed, developed, deployed and operated over such heterogeneous ecosystems. The result is an effective heterogeneity of architectures, methods, tools, and frameworks, copying with the multi-cloud application concept. The goal of this study is manifold. Firstly, it aims to characterize the multi-cloud concept from the application development perspective by reviewing existing definitions of multi-cloud native applications in the literature. Secondly, we set up the basis for the architectural characterization of these kind of applications. Finally, we highlight several open research issues drawn up from the analysis carried out. To achieve that, we have conducted a systematic literature review (SLR), where, a large set of primary studies published between 2011 and 2021 have been studied and classified. The in-depth analysis has revealed five main research trends for the improvement of the development and operation DevOps lifecycle of "multi-cloud native applications". The paper finishes with directions for future work and research challenges to be addressed by the software community. This work has been partially funded by the European projects DECIDE (Horizon 2020 research and innovation programme, under grant agreement 731533), PIACERE (Horizon 2020 research and innovation Programme, under grant agreement no 101000162), MEDINA (Horizon 2020 research and innovation Programme, under grant agreement no 952633), from the University of Naples Federico II (Finanziamento delle Ricerca di Ateneo 2020) and SwForum.eu (Horizon 2020 research and innovation Programme, under grant agreement no 957044).

Software reliability is prioritised as the most critical quality attribute. Reliability prediction models participate in the prevention of software failures which can cause vital events and disastrous consequences in safety-critical applications or even in businesses. Predicting reliability during design allows software developers to avoid potential design problems, which can otherwise result in reconstructing an entire system when discovered at later stages of the software development life-cycle. Several reliability models have been built to predict reliability during software development. However, several issues still exist in these models. Current models suffer from a scalability issue referred to as the modeling of large systems. The scalability solutions usually come at a high computational cost, requiring solutions. Secondly, consideration of the nature of concurrent applications in reliability prediction is another issue. We propose a reliability prediction model that enhances scalability by introducing a system-level scenario synthesis mechanism that mitigates complexity. Additionally, the proposed model supports modeling of the nature of concurrent applications through adaption of formal statistical distribution toward scenario combination. The proposed model was evaluated using sensors-based case studies. The experimental results show the effectiveness of the proposed model from the view of computational cost reduction compared to similar models. This reduction is the main parameter for scalability enhancement. In addition, the presented work can enable system developers to know up to which load their system will be reliable via observation of the reliability value in several running scenarios.

Changeability is a non-functional property of software that affects the length of its lifecycle. In this work, the microservices architectural pattern and TypeScript are studied through a literature review, focusing on how they enable the changeability of a web application. The modularity of software is a key factor in enabling changeability. The micro-services architectural pattern and the programming language TypeScript can impact the changeability of web applications with modularity. Microservices architecture is a well-suited architectural pattern for web applications, as it allows for the creation of modular service components that can be modified and added to the system individually. TypeScript is a programming language that adds a type system and class-based object-oriented programming to JavaScript offering an array of features that enable modularity. Through discussion on relationships between the changeability of web applications and their three key characteristics, scalability, robustness, and security, this work demonstrates the importance of designing for change to ensure that web applications remain maintainable, extensible, restructurable, and portable over time. Combined, the micro-services architecture and TypeScript can enhance the modularity and thus changeability of web applications.

Context Software selection in large-scale software development continues to be ad hoc and ill-structured. Previous proposals for software component selection tend to be technology-specific and/or do not consider business or ecosystem concerns. Objective Our main aim is to develop an industrially relevant technology-agnostic method that can support practitioners in making informed decisions when selecting software components for use in tools or in products based on a holistic perspective of the overall environment. Method We used method engineering to iteratively develop a software selection method for Ericsson AB based on a combination of published research and practitioner insights. We used interactive rapid reviews to systematically identify and analyse scientific literature and to support close cooperation and co-design with practitioners from Ericsson. The model has been validated through a focus group and by practical use at the case company. Results The model consists of a high-level selection process and a wide range of criteria for assessing and for evaluating software to include in business products and tools. Conclusions We have developed an industrially relevant model for component selection through active engagement from a company. Co-designing the model based on previous knowledge demonstrates a viable approach to industry-academia collaboration and provides a practical solution that can support practitioners in making informed decisions based on a holistic analysis of business, organisation and technical factors.

The software value chain is knowledge-based since it is highly dependant on people. Consequently, a lack of practice in managing knowledge as a resource may jeopardise its application in software development. Knowledge-Based Resources (KBRs) relate to employees’ intangible knowledge that is deemed to be valuable to a company's competitive advantage. In this study, we apply a grounded theory approach to examine the role of KBRs in Agile Software Development (ASD). To this aim, we collected data from 18 practitioners from five companies. We develop the Knowledge-Push theory, which explains how KBRs boost the need for change in ASD. Our results show that the practitioners who participated in the study utilise, as primary strategies, task planning, resource management, and social collaboration. These strategies are implemented through the team environment and settings and incorporate an ability to codify and transmit knowledge. However, this process of codification is non-systematic, which consequently introduces inefficiency in the domain of knowledge resource utilisation, resulting in potential knowledge waste. This inefficiency can generate negative implications for software development, including meaningless searches in databases, frustration because of recurrent problems, the unnecessary redesign of solutions, and a lack of awareness of knowledge sources. © 2022 The Authors open access

Intelligent technology parameter choosing software is the future development trend of realizing precisely control and efficient production. However, the technology parameter choosing software has not been reported probably due to the lack of in-depth understanding of quantitative relationship between technology parameter and magnetic properties for soft ferromagnetic composites. This paper describes a novel strategy to construct quantitative relationship model and develop technology parameter choosing software. Using gas precursor concentration parameter during fluidized bed chemical vapor deposition as an example, the microstructure and performances of Fe(Si)–SiO2 soft ferromagnetic composites obtained were systematically studied. The quantitative relationships between gas precursor concentration and magnetic performances were built based on the gas precursor concentration dependence of SiO2 insulating layer thickness and other classical theoretical models. The findings suggest that the saturation magnetization increase with the gas precursor concentration; however, the impacts of the gas precursor concentration on electrical resistivity and eddy current loss are relatively complex. Informed by these quantitative relationship models, the supporting software has been designed, which can configure the technology parameter automaticity according to the target performance of soft ferromagnetic composites. The results may deepen the understanding on the quantitative relationship and provide ideas for developing efficient production control software.

The bottleneck between the processor and memory is the most significant barrier to the ongoing development of efficient processing systems. Therefore, a research effort begun to shift from processor-centric architectures to memory-centric architectures. Various in-memory processor architectures have been proposed to break this barrier to pave the way for ever-demanding memory-bound applications. Associative in-memory processing is a successful candidate for truly in-memory computing, in which processor and memory are combined in the same location to eliminate the expensive data access costs. The architecture exhibits an unmatched advantage for data-intensive applications due to its memory-centric design principles. On the other hand, this advantage can be revealed fully by an efficient design methodology. This study puts further progressive effort by proposing a hardware/software design methodology for associative in-memory processors. The methodology aims to decrease energy consumption and area requirement of the processor architecture specifically programmed to perform a given task. According to the evaluation of nine different benchmarks, such as fast Fourier transform and multiply-accumulate, the proposed design flow accomplishes an average 7% reduction in memory area and 18% savings in total energy consumption.

[EN]Artificial intelligence, and the ability to learn optimized solutions that comply with a set of safety rules, could facilitate the human-based design process of safety-critical systems. However, the reconciliation of state-of-the-art artificial intelligence technology with current safety standards and safety engineering processes is a challenge to be addressed. This publication describes a method based on optimization and on formal verification for the design of safety-critical systems that are defined by Boolean algebra. Several diverse optimization techniques and a hybrid of these approaches are used to find an optimized design that considers performance requirements, availability rules and complies with all defined safety rules. Subsequently, this solution is translated into an alternative knowledge representation that can be formally verified and developed in compliance with currently considered safety standards. This method is evaluated with a simplified safety-critical case study., 10.13039/501100003329-Ministerio de Economa y Competitividad (Grant Number: PID2019-104156GB-I00) 10.13039/501100003086-Eusko Jaurlaritza (Grant Number: KK-2019-00035) 10.13039/100010661-Horizon 2020 Framework Programme (Grant Number: 761758 and 872944)

It is becoming common practice to push interactive and location-based services from remote datacenters to resource-constrained edge domains. This trend creates new management challenges at the network edge, not least to ensure resilience. These challenges now need to be investigated and overcome. In this paper, we explore the use of open-source programmable asset orchestration at edge cloud systems to guarantee operational resilience and a satisfactory performance level despite system incidents such as faults, congestion, or cyber-attacks. We discuss the design and deployment of a new cross-level configurable solution, Resilient Edge Cloud Systems (RECS). Results from appropriate tests made on RECS highlight the positive effects of deploying novel service and resource management algorithms at both data and control planes of the programmable edge system to mitigate against disruptive events such as control channel issues, service overload, or link congestion. RECS offers the following benefits: i) the switch automatically selects the standalone operation mode after its disconnection from the upper-level controllers; ii) deployment of edge virtualized services is made, according to client requests; iii) the client requests are served by edge services and the related traffic is balanced among the alternative on-demand routing paths to the edge location where each service is available for its clients; iv) the TCP traffic quality is protected from unfair competitiveness of UDP flows; and v) a set of redundant controllers is orchestrated by a top-level multi-thread cluster manager, using a novel management protocol with low overhead., Comment: Accepted for publication in IEEE Access; Please watch an associated video in https://ieeexplore.ieee.org/document/9751692

User interface is an essential element of an information system from the user perspective. The use of text in the user interface presents a challenge to some users, such as illiterate, elderly, non-local language speakers, or users with any disabilities. Numerous studies have proposed text-free user interfaces based on non-textual aids such as pictograms, audio, video, or specialized hardware. Such rich elements in the user interface have implications for an information system’s storage and processing requirements. We propose using concept hierarchies to present a dynamic interface to a user based upon user characteristics. A drill-down approach may present more specific interface elements to a user, and a roll-up scheme can identify more generalized interface elements. The proposed scheme can be used to manage internal data of large user interfaces with a rich set of features more effectively as it is computationally inexpensive than sequential processing techniques. We also explore various physical and psychological characteristics of elements and evaluate user preference for them. The results of a survey of 200 respondents show that users consider the use of various colors, alignments, mental models, and look & feel to be essential criteria in interface design. We also conclude that size of interface elements and the use of characters for expressing emotions are significantly important characteristics in interface design. The future work will apply the proposed scheme to a full spectrum of applications ranging from enterprise systems to mobile applications.

Sustainability is becoming an increasingly important topic. Information Technology (IT) is an important factor for sustainability; it consumes a substantial, and growing, part of the world supply of energy, but it can also enable significant insights and improvements related to sustainability. These factors need to be taken into account in the design of IT systems, meaning that we need to architect for sustainability. This paper provides insights into the experience and beliefs of IT practitioners and researchers into current and desired practices of architecting for sustainability. It reports on the results of three workshop sessions with practitioners and researchers, providing insight into the state of research and practice.

Background This research investigated student satisfaction and self-confidence in learning with virtual simulations in a prelicensure class. Method A descriptive mixed-method approach used the National League for Nursing's Student Satisfaction and Self-Confidence in Learning questionnaire with additional items to survey 28 students' perception after using three vSim for Nursing™ Pharmacology clinical scenarios. Results Although the initial use of vSims were perceived as helpful and effective, difficulty with navigation and unrealism was reported by most students as limitations. Conclusion These findings reveal a consistent trend with previous research that suggests upgrades in vSim software design might ease the navigation, realism, and fluid actions of the computer-animated nurse and patient avatar to improve the usefulness of vSims as an active learning strategy in class. Additional research is warranted into students' perceptions with vSims and the effect on various learning outcomes to assist nurse educators with integrating vSims effectively into a didactic course.

One of the integral components in the architectural design and development of Internet of Things (IoT) is Microservice. Microservices are basically an architectural and organizational approach in the process of software development where the software is composed of small but independent services that would communicate over well-defined APIs (Application Programming Interfaces). It is quite challenging to ensure data integrity and data availability in the architecture design of microservices. As Blockchain technology has emerged as a panacea to many of the other domains, the distributed microservice architecture can also utilize it. We know that the consensus algorithms are used in the Blockchain technology to validate the transactions alongside providing extra level of security. Taking the advantage of consensus algorithms in blockchain-based architecture models, in this paper, we propose TCA (Trustworthy Consensus Algorithm), which is designed to solve the data integrity and availability challenges in microservice architectures. We have evaluated the proposed algorithm against the known alternatives and it shows good level of efficiency. © 2023 Elsevier Ltd

This paper discusses the key technologies for the fusion of Radio Frequency Identification (RFID) technology and wireless sensor networks through a fusion approach; meanwhile, it addresses the problem of energy imbalance infusion networks. This paper introduces the network energy consumption calculation methods under direct transmission and minimum energy multi-hop routing protocols, and designs an improved network structure model to improve the communication scale. The RFID reading and writing system based on Zig Bee technology is developed, and focuses on the hardware interface circuit design of the fusion system, the development of compatible communication protocols, the anti-collision algorithm, and the transmission performance of the system under two outdoor environments. The design of the environmental collection terminal uses relevant sensors to collect the transport environment data in the carriage. And Wireless Sensor Network (WSN) combined with General Packet Radio Service (GPRS) technology realizes the function of the main aggregation node of wireless sensors. The method achieves data aggregation and data transmission, and the development of the application platform of the monitoring system uses software technology. Based on the browser/server (B/S) architecture, the development of the monitoring system is finally realized. Finally, the cold chain transportation simulation experiment was conducted to test the system, and the results showed that the hardware of the cold chain coordination monitoring system can work normally and the software design of the monitoring system can meet the basic requirements of the system for the third-party logistics enterprises. The efficiency of our results is improved by about 10% compared to other studies, and their accuracy is improved to a higher degree, which makes our model more practical.

International audience; CRISPR-based genome editing systems have been successfully and effectively used in many organisms. However, only a few studies have reported the comparison between CRISPR/Cas9 and CRISPR/Cpf1 systems in the whole-genome applications. Although many web-based toolkits are available, there is still a shortage of comprehensive, user-friendly, and plant-specific CRISPR databases and desktop software. In this study, we identified and analyzed the similarities and differences between CRISPR/Cas9 and CRISPR/Cpf1 systems by considering the abundance of proto-spacer adjacent motif (PAM) sites, effects of GC content, optimal proto-spacer length, potential universality within the plant kingdom, PAM-rich region (PARR) inhibiting ratio, and effects of G-quadruplex (G-Q) structures. Using this information, we built a comprehensive CRISPR database (including 138 plant genome data sources, www.grapeworld.cn/pc/index.html), which provides search tools for the identification of CRISPR editing sites in both CRISPR/Cas9 and CRISPR/Cpf1 systems. We also developed a desktop software on the basis of Perl/TK tool, which facilitates and improves the detection and analysis of CRISPR editing sites at the whole-genome level on Linux and/or Windows platform. Therefore, this study provides helpful data and software for easy selection and application of CRISPR-based genome editing systems in plants.

The road traffic collisions and injuries, is still a major concern in automotive field. A disproportionate number of fatal accidents happen at nighttime. The major part of the collision is contributed by the human factors. Yet the technology in automobile is continuously assisting drivers while driving. Driving in the dark is not always easy as roads aren’t always illuminated, in such cases a powerful headlight is needed to illuminate most of the road. But such headlights cause glare to the oncoming traffic and again the chances of collision increase, with risking lives of passengers. Therefore, a need of smart headlamps which can illuminate the road far ahead without glaring the oncoming traffic is generated. This research aims to build a Laser based adaptive headlight system which can fulfil the need. The headlight design is proposed using Tinkercad software in which Arduino circuit has been used and software design is presented. The system works well with responding to the steering angle and controls the intensity of light preventing oncoming traffic from getting glared. Keywords: Adaptive headlight system, laser headlights, Arduino, Arduino software design, design of headlight system, cornering lights, effects of laser headlights, Tinkercad

The potential of land in Indonesia which is quite large and has not been used optimally is one of the problems. this study focused on developing fuzzy logic models to predict plants that are suitable for planting on agricultural land to enable the land use more optimal. In conducting this study, there were two stages of implementation, namely hardware design, and software design which included system workflow design using the Fuzzy Logic Classifier method where three input variables were used, namely soil pH, soil temperature, and humidity. The findings of this study are in the form of predictions consisting of eight outputs, namely Unfavorable Land, Spinach, Cayenne Pepper, Beans, Long Beans, Cucumber, Eggplant, and Tomatoo. The prediction results generated were directly displayed on the LCD of the instrument that has been designed.

Software design patterns are well-known solutions for solving commonly occurring problems in software design. Detecting design patterns used in the code can help to understand the structure and behavior of the software, evaluate the quality of the software, and trace important design decisions. To develop and maintain a software system, we need sufficient knowledge of design decisions and software implementation processes. However, the acquisition of knowledge related to design patterns used in complex software systems is a challenging, time-consuming, and costly task. Therefore, using a suitable method to detect the design patterns used in the code reduces software development and maintenance costs. In this paper, we proposed a new method based on conceptual signatures to improve the accuracy of design pattern detection. So we used the conceptual signatures based on the purpose of patterns to detect the patterns’ instances that conform to the standard structure of patterns, and cover more instances of patterns’ variants and implementation versions of the patterns and improve the accuracy of pattern detection. The proposed method is a specific process in two main phases. In the first phase, the conceptual signature and detection formula for each pattern is determined manually. Then in the second phase, each pattern in the code is detected in a semi-automatic process using the conceptual signature and pattern detection formula. To implement the proposed method, we focused on GoF design patterns and their variants. We evaluated the accuracy of our proposed method on five open-source projects, namely, Junit v3.7, JHotDraw v5.1, QuickUML 2001, JRefactory v2.6.24, and MapperXML v1.9.7. Also, we performed our experiments on a set of source codes containing the instances of GoF design patterns’ variants for a comprehensive and fair evaluation. The evaluation results indicate that the proposed method has improved the accuracy of design pattern detection in the code.

Building information modelling (BIM) represents a workflow whose application on a construction project will enable all involved players to compile as well as work with information on every aspect of a building in a common model/database. Through BIM, the entire building can be virtually designed and built on a computer. BIM touches every part of a building’s life cycle starting from the design phase well into the construction phase and beyond that into asset management. This research examines the experiences of early adopters of BIM and use that insight to introduce BIM, specially focusing on the structural analysis and design stage of a building. The study demonstrates how all structural design activities can be integrated with each other and how cross-discipline collaboration with the architect can be achieved through the adoption of BIM without leaving ones customary structural design platform. As a demonstration, a sample building is modelled using Revit along with conventional structural software packages ETABS and SAFE. Plugins and applications were developed for these software packages to facilitate interoperability amongst them so that they all act together as a single platform. Modelling, analysis, design and clash detections were facilitated by applying BIM. Major benefits of employing BIM in a structural design project are illustrated through this research.

Trichoscopy represents a non-invasive diagnostic modality widely used in daily practice. Despite the common perception that this technique has been fairly established, some key issues remain to be addressed. Complexity and inconsistency in terminology in past literature are likely to confuse investigators when they are recording, reporting, and retrieving the findings. In addition, a diagnostic algorithm adopting sufficiently integrated and updated findings is not readily available. By adopting a systematic review approach, this review attempted to redefine major trichoscopic findings and integrate their synonyms individually into the most frequently used terms besides identifying and discussing terms which potentially cause confusion. The findings are categorized into five subgroups: hair shaft, follicular, perifollicular, scalp findings, and hair distribution pattern abnormalities. The calculation of sensitivities and positive predictive values of such redefined findings was conducted by reviewing the descriptions in the past literature on major hair diseases, including alopecia areata, androgenetic alopecia/female pattern hair loss, telogen effluvium, trichotillomania, lichen planopilaris, frontal fibrosing alopecia, central centrifugal cicatricial alopecia, discoid lupus erythematosus, folliculitis decalvans, tinea capitis, and dissecting cellulitis, to confirm the diagnostically meaningful findings for representative diseases. This attempt redefined, for instance, yellow dots, short vellus hairs, exclamation mark hairs, black dots, and broken hairs as the findings of diagnostic significance for alopecia areata and hair diameter diversity, peripilar sign, and focal atrichia for androgenetic alopecia/female pattern hair loss. An updated diagnostic flowchart is proposed with the instructions to maximize its usefulness. Current limitations and future perspectives of trichoscopy as well as other emerging non-invasive diagnostic modalities for hair diseases are also discussed.

Gas hydrate is one of the crucial flow assurance problems in the petroleum industry. Inaccurate predictions of gas-hydrate conditions could lead to severe technical and economic defects especially when results in plugging the gas streamline. The prediction techniques could be classified into six approaches relevant to pure and complex hydrocarbon mixtures. Up till now, many correlations have been developed to cover the hydrate formation conditions point; however, most of these correlations are presented graphically that which makes them incompatible with computer simulation packages. Furthermore, by reviewing the literature, the previous artificial neural networks have a deficiency in providing the corresponding mathematical model to be employed to reproduce results using other data points or to be compared to other developed artificial neural network (ANN) models. The main purpose of this paper is to develop a comprehensive ANN model with its corresponding mathematical model to facilitate the prediction of gas-hydrate formation temperature in the case of natural gas-pure water systems. In addition to the mathematical model, a MATLAB code and a standalone computer program are also established to provide a high degree of compatibility with simulation and design software packages. The ANN model was built based on (1469) datasets of gas-hydrate formation pressure, temperature, and specific gravity, and the data points are captured from Katz's (Transactions of the AIME 160:140–9, 1945) gravity chart. The proposed model achieves a coefficient of determination equal to (0.999), with an average relative error of (− 0.0005%), the average absolute relative error of (0.1145%), root mean square error (0.073), and standard deviation of (0.00398).

Background: The COVID-19 pandemic has increased the need for remote healthcare options among patients with Huntington’s disease (HD). However, since not every HD patient is suitable for telehealth, it is important to differentiate who can be seen virtually from who should remain as in-person. Unfortunately, there are no clinical guidelines on how to evaluate HD patients for telehealth eligibility. Objective: To standardize the teleneurology selection process in HD by implementing a screening tool that accounts for patient-specific factors. Methods: We organized various indications and contraindications to teleneurology into a flowchart. If any indications or contraindications were met, patients were assigned to telehealth or maintained as in-person, respectively. If no indications or contraindications were met, patients were given the option of telehealth or in-person for their upcoming appointments. In two implementation cycles, we tested this screening tool among all HD patients scheduled for clinic visits, aided by chart review and phone interview. Results: In a cohort of 81 patients, telehealth acceptance among eligible patients increased from 45.0%to 83.3%. Frequency of telehealth visits increased from a pre-intervention baseline of 12.8%to 28.2%. Conclusion: Teleneurology utilization among HD patients more than doubled across our study. Our intervention promotes consistency and patient-centeredness in HD clinical care and streamlines the overall telehealth selection process. Future studies can seek to reduce telehealth no-shows and also evaluate the utility of the motor and psychiatric criteria included in our screening tool.

Early diagnosis of the harmful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with clinical expertise, allows governments to break the transition chain and flatten the epidemic curve. Although reverse transcription-polymerase chain reaction (RT-PCR) offers quick results, chest X-ray (CXR) imaging is a more reliable method for disease classification and assessment. The rapid spread of the coronavirus disease 2019 (COVID-19) has triggered extensive research towards developing a COVID-19 detection toolkit. Recent studies have confirmed that the deep learning-based approach, such as convolutional neural networks (CNNs), provides an optimized solution for COVID-19 classification; however, they require substantial training data for learning features. Gathering this training data in a short period has been challenging during the pandemic. Therefore, this study proposes a new model of CNN and deep convolutional generative adversarial networks (DCGANs) that classify CXR images into normal, pneumonia, and COVID-19. The proposed model contains eight convolutional layers, four max-pooling layers, and two fully connected layers, which provide better results than the existing pretrained methods (AlexNet and GoogLeNet). DCGAN performs two tasks: (1) generating synthetic/fake images to overcome the challenges of an imbalanced dataset and (2) extracting deep features of all images in the dataset. In addition, it enlarges the dataset and represents the characteristics of diversity to provide a good generalization effect. In the experimental analysis, we used four distinct publicly accessible datasets of chest X-ray images (COVID-19 X-ray, COVID Chest X-ray, COVID-19 Radiography, and CoronaHack-Chest X-Ray) to train and test the proposed CNN and the existing pretrained methods. Thereafter, the proposed CNN method was trained with the four datasets based on the DCGAN synthetic images, resulting in higher accuracy (94.8%, 96.6%, 98.5%, and 98.6%) than the existing pretrained models. The overall results suggest that the proposed DCGAN-CNN approach is a promising solution for efficient COVID-19 diagnosis.

Delivering increasingly complex software-reliant systems demands better ways to manage the long-term effects of short-term expedients. The technical debt (TD) metaphor has gained significant traction as a way to understand and communicate such issues. Almost 25 years after the term was coined in 1992 by Ward Cunningham, and more than 10 years after the first edition of the TechDebt workshop/conference series, we take a brief look at the past, present, and future of TD.

Software and IT usage are continuously growing to keep our society active and manage our individual lives. But as they grow, their energy demand is exploding. By 2030, data centers alone will already consume some 10% of the global electricity.1 Including the Internet, telecommunications, and embedded devices, the energy consumption will be one-third of the global demand. Understanding that end users only consume what we offer, it is the community of software developers who must become active in ecologic behaviors. Green IT is the call of today. Each single line of code that we develop today may still be running years from now on zillions of processors, eating energy and contributing to global climate change.

Software systems quality is strongly influenced by the presence of design problems and wrong constructs introduced during software evolution. Design smells are recognized to be indicators of such problems since their presence can indicate violations of the fundamental software design principles. This explains the interest of software researchers and developers in new approaches performing design smell early detection. In this paper, the adoption of a just-in-time design smell prediction approach is proposed. The approach uses a variant of Temporal Convolutional Networks (TCN) to predict the presence of design smells before they are introduced in the model. In comparison to other studies, in the proposed approach we focus on commit-level fine-grained product metrics and process metrics (i.e., ownership and seniority) to make deep neural networks training suitable. The validation is performed on a large dataset composed of six open source systems. The results show good prediction performance for some design smells and for all the considered design smells categories. The experiment performed shows that, with respect to baseline prediction models (i.e., LSTM,CNN and RF), the proposed TCN variant trained on the identified metrics, gives better results.

Software design is about making decisions. The quality of design decisions influences the quality of software design. This article describes nine decision-making principles to give software designers a systematic approach for decision making.

Background: Analysis of operative data with convolutional neural networks (CNNs) is expected to improve the knowledge and professional skills of surgeons. Identification of objects in videos recorded during surgery can be used for surgical skill assessment and surgical navigation. The objectives of this study were to recognize objects and types of forceps in surgical videos acquired during colorectal surgeries and evaluate detection accuracy.Methods: Images (n=1818) were extracted from 11 surgical videos for model training, and another 500 images were extracted from 6 additional videos for validation. The following 5 types of forceps were selected for annotation: ultrasonic scalpel, grasping, clip, angled (Maryland and right-angled), and spatula. IBM Visual Insights software was used, which incorporates the most popular open-source deep-learning CNN frameworks.Results: In total, 1039/1062 (97.8%) forceps were correctly identified among 500 test images. Calculated recall and precision values were as follows: grasping forceps, 98.1% and 98.0%; ultrasonic scalpel, 99.4% and 93.9%; clip forceps, 96.2% and 92.7%; angled forceps, 94.9% and 100%; and spatula forceps, 98.1% and 94.5%, respectively.Conclusions: Forceps recognition can be achieved with high accuracy using deep-learning models, providing the opportunity to evaluate how forceps are used in various operations.

Data compression is the science or art of representing information in a more compact form. Various algorithms have been developed for data compression purposes. One of the algorithms used is Arithmetic Coding. The working principle of this algorithm is to replace a series of input symbols with floating point numbers, where the longer and more complex the encoded message, the more bits required for file compression. This software is designed using the Visual Basic 6.0 programming language and is able to compress text files with a compression rate of about 75 percent. This system can also be used to compress two or more text files at once.

Aiming at the problems of low user satisfaction and long response time in traditional online English education platforms, this paper designs a web-based online English education platform and carries out relevant application tests. With the support of the web, the hardware and software of the platform are designed. The hardware of the platform is composed of student module, teacher module, administrator module, and database module. On this basis, the K-means clustering method is used to cluster the learner data in the web, to determine their English level, and the collaborative filtering algorithm is combined to recommend relevant course materials for learners so as to complete the platform software design. The application test results show that the platform has the advantages of superior function and security, high user satisfaction, and short response time and has certain practical application value.

Statistical body shape models (SBSM) provide compact, flexible representations of body shape that can be implemented in design software. However, few SBSMs have been created to represent adults in supported seated postures that are relevant for the design of seated environments, and none has incorporated the effects of age. This paper presents an SBSM based on surface laser-scan data from 155 U.S. adults. The data were processed to obtain homologous mesh structure and symmetric geometry, and the processed data were statistically analysed using principal component analysis to obtain a compact representation of the data variance. Regression analysis was conducted to predict body size and shape from stature, body mass index, ratio of sitting height to stature, sex, and age. The resulting model allows rapid generation of realistic body models for applications, including product design, accommodation assessment, and safety system optimisation. The model is publicly accessible at HumanShape.org. Practitioner summary: This paper presents a statistical model that represents adult body shapes in a supported seated posture based on 3 D anthropometric measurements. This model is the first whole-body parametric model known to incorporate age effects based on data extending beyond 65 years of age.

Android is a mobile device on the operating system for mobile phones based on Linux, android provides an open platform for developers to create applications. Android-based application developers make applications so that they can be useful for the community ranging from game genre applications, education, entertainment and simulation applications that are very likely to be created and run on this android operating system. Simulation is the process of designing a matemastis or logical model of a real system, conducting experiments on models using computers to describe, explain and predict system behavior. For a simulation application company can be used as a medium of product promotion, while for smartphone users can help to channel the existing imagination into a real picture. The application of simulation applications many functions, one example of its application to two-wheeled motor vehicles. By utilizing android application and the growth of vehicle users that lead to the hobby of modification, it can be utilized by creating an android-based coloring simulation application to help modifiers and modification lovers in order to channel their imagination and make it easier to design colors as desired. Without having to bother using design software that requires special skills

This research aimsto produce a village web-based information system design in Manulondo Village. This application contains the design of a village information system including village profiles, population information, and village potential information. This research uses the method waterfall. The research stages include data collection, analysis, design, coding, implementation testing. The trial using BlackBox Testing. That is by testing the user interface system to find out whether the user interface contained in this application can function properly as expected. That results of the software design in the form of a web-based Manulondo Village information system, consist of, serach for population information an village potential information.

Nowday, the development of technology is growing rapidly, this has an impact on practical life. One of them is coconut grating. in grating coconut people usually used coconut grating. but still rely on human power in grated procces. People use grated coconut from coconut plants. The result of grated coconut that can be used as an ingredient in food making, in order to get grated coconut, commonly people still used traditional way to take it.to overcome the situation, this thesis is designed to make it easier without washing energy and save time. by using sensor tcs3200 to detect color and hcsr 04 to respond distance. beside this project there I a buzzer makes sound when the coconut is finished being grated. the design of this project uses experimental method that consist of hardware and software design. this tool uses microcontroller atmega8535 as control centre of the tool . Dc motor permanent megnet as arm drive motor induction for main grater

The paper further develops the mathematical model of the software design technology (SDT) and the criteria for evaluating the SDT, which allow experts to evaluate each considered software design technology more accurately, taking into account all its components. The method and production rules of the selection of the software design technology proposed by the authors give the organization the opportunity to make a motivated and reasonable choice of the design technology for its further implementation

Mitochondria are dynamic organelles that differ significantly in their morphologies across cell types, reflecting specific cellular needs and stages in development. Despite the wide biological significance in disease and in health, delineating mitochondrial morphologies in complex systems remains challenging. Here, we present the Mitochondrial Cellular Phenotype (MitoCellPhe) tool developed for quantifying mitochondrial morphologies and demonstrate its utility in delineating differences in mitochondrial morphologies in a human fibroblast and human induced pluripotent stem cell (hiPSC) line. MitoCellPhe generates 24 parameters, allowing for a comprehensive analysis of mitochondrial structures and importantly allows for quantification to be performed on mitochondria in images containing single cells or clusters of cells. With this tool, we were able to validate previous findings that show networks of mitochondria in healthy fibroblast cell lines and a more fragmented morphology in hiPSCs. Using images generated from control and diseased fibroblasts and hiPSCs, we also demonstrate the efficacy of the toolset in delineating differences in morphologies between healthy and the diseased state in both stem cell (hiPSC) and differentiated fibroblast cells. Our results demonstrate that MitoCellPhe enables high-throughput, sensitive, detailed, and quantitative mitochondrial morphological assessment and thus enables better biological insights into mitochondrial dynamics in health and disease.

This article presents a GPU-accelerated software design of the recently proposed model of Slanted Stixels, which represents the geometric and semantic information of a scene in a compact and accurate way. We reformulate the measurement depth model to reduce the computational complexity of the algorithm, relying on the confidence of the depth estimation and the identification of invalid values to handle outliers. The proposed massively parallel scheme and data layout for the irregular computation pattern that corresponds to a Dynamic Programming paradigm is described and carefully analyzed in performance terms. Performance is shown to scale gracefully on current generation embedded GPUs. We assess the proposed methods in terms of semantic and geometric accuracy as well as run-time performance on three publicly available benchmark datasets. Our approach achieves real-time performance with high accuracy for 2048 × 1024 image sizes and 4 × 4 Stixel resolution on the low-power embedded GPU of an NVIDIA Tegra Xavier.