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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.

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)

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

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.

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.

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.

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

With the increasingly severe problems of garbage contamination, automatic garbage identification has significant value in actual application. However, the real implementation is still confronted with the limitations of equipment performance and the need for more computer resources. To effectively tackle the challenges, this paper proposes a model that uses the MobileNet as a base network to improve the performance of Mask R-CNN, which could be applied to the low-cost embedded devices. For a numerical evaluation on real images, we construct a garbage image data set containing multiple morphological types based on the complex background. Specifically, when pre-processing the data set, we use the methods of data expansion and transfer learning. Consequently, high-precision detection of garbage could be achieved under the condition of a few samples. The experimental results present that the approach can achieve a detection accuracy up to 93%. Furthermore, the proposed approach is a complete system combining both hardware and software design, which dramatically promotes the automation of production and provides a reference for the intelligent garbage sorting machines.

The problem of designing advanced computing systems in the class of structures of integrated modular avionics is considered. The unified topology of the internal network of the computer on the basis of Space Wire exchange channels and variants of its execution for various onboard applications is offered. Equivalent reliability schemes of each of the specific structures are introduced and the probabilities of trouble-free operation of each structure are analyzed. Families of graphic dependencies are given. The analysis of the existing principles and algorithms for testing multiprocessor multimodal onboard digital computer systems is given; the new testing algorithm for the multiprocessor systems which follows the software design standards for products of integrated modular avionics is offered. The structure of the unified automated workplace for checking the functional modules of integrated modular avionics is considered. Specific requirements inherent in the workplaces for testing integrated avionics are identified: an increased level of control of the hardware component of products; the ability to simulate the failure state of individual components of avionics to check the mode of reconfiguration of the computer system; modular construction of software with the division of verification tests into components performed at the level of each CPM and the computer as a whole in single-task and multitasking modes; openness of architecture of a workplace, which provides an ability to change the level of control complexity of a product and control of one class of complexity; intra-project unification of both hardware and software of the workstation of the inspection.

This research was conducted at SMK N 2 Padang which aims to implement the e-commerce system as a learning medium for entrepreneurship education in order to increase the potential of students in entrepreneurship according to the chosen field of expertise. As well as preparing students to compete in the world of work and be able to open their own jobs by utilizing technology in the form of internet networks. The method used in this study is a Research & Development (Development Method) with a waterfall development model. This model consists of several stages including the planning stage, the software needs analysis stage, the software design stage, the implementation stage, and the maintenance stage. With the existence of an e-commerce system as a medium of learning in entrepreneurship education, students can understand business processes that utilize the internet network, be it the production process, the marketing process, or the transaction.

Early in 2020, the pandemic resulted in an enormous demand for personal protective equipment (PPE), which consists of face masks, face shields, respirators, and gowns. At our institution, at the request of hospital administration, the Lifespan 3D Printing Laboratory spearheaded an initiative to produce reusable N95 masks for use in the hospital setting. Through this article, we seek to detail our experience designing and 3D printing an N95 mask, highlighting the most important lessons learned throughout the process. Foremost among these, we were successful in producing a non-commercial N95 alternative mask which could be used in an era when N95 materials were extremely limited in supply. We identified five key lessons related to design software, 3D printed material airtightness, breathability and humidity dispersal, and ability for communication. By sharing our experience and the most valuable lessons we learned through this process, we hope to provide a helpful foundation for future 3D-printed N95 endeavors.

Posyandu ialah salah satu wujud upaya kesehatan bersumber daya penduduk yang dikelola serta diselenggarakan dari, oleh, untuk, serta bersama penduduk dalam penyelenggaraan pembangunan kesehatan, guna memberdayakan penduduk serta berikan kemudahan penduduk dalam mendapatkan pelayanan kesehatan dasar. Namun itu semua tidak didukung dengan sarana yang baik dalam menyampaikan informasi. Proses kelola dan pendaftaran cukup merepotkan untuk kader dan bidan dengan adanya kendala-kendala yang sering di temui. Prosesnya yang manual dan tidak efesien hal ini dapat memakan waktu yang cukup lama. Pada penelitian ini, akan dirancang sebuah aplikasi posyandu android. Pembuatan aplikasi ini menggunakan cara analisis permasalahan dan perancangan perangkat lunak disertai pengujian terhadap perangkat lunaknya. Hasil dari penelitian ini berupa memantau serta mengelola informasi Kesehatan Ibu dan Anak( KIA).

In the blade computer numerical control (CNC) machining process, the machining fixture design for each type of blade will be a very difficult task due to the large variety and quantity of blades. This study proposes a computer-aided machining fixture design algorithm and software based on case learning. The three-dimensional model of the case learning blade and the target blade is firstly projected into a cylinder surface based on the projection algorithm, and the cylinder surface is expanded into a two-dimensional plane based on expansion algorithm. Then, the matching machining fixture layout of the target blade relative to the case learning blade is obtained in two-dimensional space by the mesh affine transformation algorithm according to the geometric edge constraints and the matching relationship of the case learning blade and target blade. At last, the inverse transformations of projection and expansion are used to obtain the final fixture layout of the target blade three-dimensional model based on the previous projection and expansion relationship. The special computer-aided fixture design software of near-net shaped blade is developed, and the machining fixture design and CNC machining experiment based on two typical near-net-shaped blades proved that the computer-aided fixture design algorithm based on case learning is feasible and can be used to realize the intelligent design of near-net-shaped blade machining fixture.

Software is rapidly evolving and operates in a changing environment; therefore, in addition to software design and testing, it is essential to observe and understand the software execution behavior by modeling data recorded during the execution of the software to improve its reliability. The nested call relationship between methods during the execution of software is common, but most process-mining methods are unable to discover them, only generating flat models with low fitness. Meanwhile, it is easy to generate “spaghetti-like” models with low comprehensibility when dealing with complex software execution data. This paper proposes a component-based hierarchical software behavior model discovery method that can discover hierarchical nested call structures during software runtime, improving the fitness of the model; meanwhile, the proposed method partitions the discovery model into several parts by component information to improve the comprehensibility of the model, which can also reflect the interaction behavior within and between components. The proposed approach was implemented in a process mining toolkit. Using real-life software event logs and public datasets, we demonstrated that compared with other advanced process mining techniques, our approach can visualize actual software execution behavior in a more accurate and easy-to-understand way while balancing time performance.

The construction of a dispersive optical spectrometer using three-dimensional (3D) design software and printing, without applying any optical adjustments, its validation, and application to quantification of ethanol in multiproduct liquids, is the objective of this work. A 3D design software was used to design a near-infrared (NIR) spectrometer in the region from 800 to 1600 nm from the dimensions of commercially available optical components. The project was printed on a polymer filament 3D printer, and the components were fitted to the printed part. Software calculations using the model design parameters were applied to attribute the wavelength values to the abscissa axis in the spectra and estimate errors due to 3D printing limitations. The alignment of the spectrum was proven using the chloroform absorbance spectrum, which presented a maximum mispositioning of 4.1 nm concerning the literature data and effective bandwidths equivalent to commercial instruments. The 3D-printed instrument was applied to quantify ethanol in samples of cachaça, rum, beer, brandy, whiskey, vodka, mouth wash, alcohol gel, and commercial alcohol solutions. Partial least-squares regression models were built for the 3D-printed instrument and two commercial NIR instruments, the MPA II (Bruker) and the NIR DLP NIRscan (Texas Instruments), using a group of 180 standards. The three instruments reached excellent predictive capability with similar root mean square error of cross-validation (2.36-2.68) and prediction (2.31-2.87). The correlation coefficient of cross-validation and prediction for all models were between 0.97 and 0.98. The results show the feasibility of building a 3D-printed dispersive spectrometer ready for application with the simple docking of the optics, presenting acceptable accuracy to the project design concerning the printing limitations.

Objective Three‐dimensional (3D) printing has been rapidly adopted by different surgical disciplines. It has shown itself to have improved outcomes in education, pre‐operative planning, and reconstruction. However, using 3D printing to create surgical instruments is a niche within the literature that has not yet been fully explored. The authors present a study in which it is hypothesized that 3D printing surgical instruments can be utilized successfully within ENT surgery. Methods As one of the most common ENT operations worldwide, a septoplasty was chosen as the procedure to provide proof of concept. For the septoplasty, five instruments were printed: a scalpel handle, needle holders, toothed forceps, a Cottle/Freer elevator, and a Killian's speculum. The entire set took 224 minutes on average to print, weighed 36 g, and only used approximately 86 pence ($1.20 USD) worth of polylactic acid plastic to create. Results All steps in performing a septoplasty on a human cadaver with the 3D printed tools were possible and were undertaken successfully. This yielded a similar outcome to using stainless steel with the added benefit of there being a large reduction in cost and the ability for rapid customization according to the surgeon's preferences. Conclusion As technology and mainstream interest in 3D printing develops, the availability of more precise Computer‐Aided Design software will allow for more complex designs of tools to be created. Currently, 3D printing has been shown to be a promising method from which future surgical tools can be fashioned to meet the complex, dynamic demands of surgery. Level of Evidence N/A.

IoT systems are required to manage themselves to changes regarding their internal and external contexts. So, adaptability is a very important aspect in IoT software systems. The MAPE (Monitoring, Analysis, Planning, Execution) control loop model, inspired from the autonomic nervous system, has been identified as a crucial element for realizing self-adaptation in software systems. In fact, software design patterns provide architects and developers with reusable software elements helping them to master building complex software systems including several interconnected components. Complex self-adaptive systems require several architectural patterns in their design which leads to the need of architectural pattern composition. In this paper, we focus in modeling adaptability in IoT systems through a set of MAPE design patterns for decentralized control in self-adaptive systems and we propose an approach for composing them using a UML profile. Then, we propose formalizing the composition process using the Event-B method. In addition, we propose verifying adaptation properties based on the resulting formal specification. We illustrate our approach by modeling structural and behavioral features of the hybrid pattern resulting from the composition of two MAPE patterns and applied to the fall-detection ambient assisting living system for elderly people.

Today’s industry has gradually realized the importance of lifting efficiency and saving costs during the life-cycle of an application. In particular, we see that most of the cloud-based applications and services often consist of hundreds of micro-services; however, the traditional monolithic pattern is no longer suitable for today’s development life-cycle. This is due to the difficulties of maintenance, scale, load balance, and many other factors associated with it. Consequently, people switch their focus on containerization—a lightweight virtualization technology. The saving grace is that it can use machine resources more efficiently than the virtual machine (VM). In VM, a guest OS is required to simulate on the host machine, whereas containerization enables applications to share a common OS. Furthermore, containerization facilitates users to create, delete, or deploy containers effortlessly. In order to manipulate and manage the multiple containers, the leading Cloud providers introduced the container orchestration platforms, such as Kubernetes, Docker Swarm, Nomad, and many others. In this paper, a rigorous study on Kubernetes from an administrator’s perspective is conducted. In a later stage, serverless computing paradigm was redefined and integrated with Kubernetes to accelerate the development of software applications. Theoretical knowledge and experimental evaluation show that this novel approach can be accommodated by the developers to design software architecture and development more efficiently and effectively by minimizing the cost charged by public cloud providers (such as AWS, GCP, Azure). However, serverless functions are attached with several issues, such as security threats, cold start problem, inadequacy of function debugging, and many other. Consequently, the challenge is to find ways to address these issues. However, there are difficulties and hardships in addressing all the issues altogether. Respectively, in this paper, we simply narrow down our analysis toward the security aspects of serverless. In particular, we quantitatively measure the success probability of attack in serverless (using Attack Tree and Attack–Defense Tree) with the possible attack scenarios and the related countermeasures. Thereafter, we show how the quantification can reflect toward the end-to-end security enhancement. In fine, this study concludes with research challenges such as the burdensome and error-prone steps of setting the platform, and investigating the existing security vulnerabilities of serverless computing, and possible future directions.

Digital media have become increasingly important in recent years and can offer new possibilities for mathematics education in elementary schools. From our point of view, geometry and geometric objects seem to be suitable for the use of computer-aided design software in mathematics classes. Based on the example of Tinkercad, the use of CAD software — a new and challenging context in elementary schools — is discussed within the approach of domains of subjective experience and the Toulmin model. An empirical study examined the influence of Tinkercad on fourth-graders’ development of a model of a geometric solid and related reasoning processes in mathematics classes.

Microservices and Big Data are renowned hot topics in computer science that have gained a lot of hype. While the use of microservices is an approach that is used in modern software development to increase flexibility, Big Data allows organizations to turn today’s information deluge into valuable insights. Many of those Big Data architectures have rather monolithic elements. However, a new trend arises in which monolithic architectures are replaced with more modularized ones, such as microservices. This transformation provides the benefits from microservices such as modularity, evolutionary design and extensibility while maintaining the old monolithic product’s functionality. This is also valid for Big Data architectures. To facilitate the success of this transformation, there are certain beneficial factors. In this paper, those aspects will be presented and the transformation of an exemplary Big Data architecture with somewhat monolithic elements into a microservice favoured one is outlined.

The removal of plastic contamination from cotton lint is an issue of top priority to the U.S. cotton industry. One of the main sources of plastic contamination showing up in marketable cotton bales is plastic used to wrap cotton modules produced by John Deere round module harvesters. Despite diligent efforts by cotton ginning personnel to remove all plastic encountered during module unwrapping, plastic still finds a way into the cotton gin’s processing system. To help mitigate plastic contamination at the gin, a machine-vision detection and removal system was developed that utilizes low-cost color cameras to see plastic coming down the gin-stand feeder apron, which upon detection, blows plastic out of the cotton stream to prevent contamination. This paper presents the software design of this inspection and removal system. The system was tested throughout the entire 2019 cotton ginning season at two commercial cotton gins and at one gin in the 2018 ginning season. The focus of this report is to describe the software design and discuss relevant issues that influenced the design of the software.

There are many methods of detecting unknown malware, each of which has its advantages, disadvantages and features of use. However, at present there is no methodology that would fully solve all the problems of detecting malware with acceptable efficiency. In the field of modern computer security, most solutions are implemented as a set of several technologies. In this regard, the study of modern methods of detecting malware is an important area. The purpose of this work is to analyze and systematize the main methods of detecting malicious software and study the features of their use. The research of signature and heuristic methods of malware detection is performed in the work. A separate analysis is devoted to the application of machine learning methods (static, dynamic, visual representation of elements and their combinations) for the classification of malicious programs. Various machine learning techniques have been studied: the machine method of reference vector, random forest, decision trees, naive bayes, k-nearest neighbor and gradient amplification to classify and detect malware samples and their respective classes, their filtering. The usefulness of graphical byte visualization for detecting software design templates using Dotplot technique for further automation of virus detection is shown. The comparative characteristic of modern, mainly, heuristic methods of detection of malware is executed and systematized on values of accuracy of search. As there is currently no effective method of detecting unknown malware, so to effectively search and destroy malware you need to combine all modern methods, techniques and tools, taking into account all the features of their use. The results can be used to develop software to effectively detect and destroy malware using artificial neural networks and their training.

A Quantitative Critical Thinking (QCT) software tool was developed in this study to facilitate students’ learning of quantitative critical thinking via repeated practice by chemical engineering students reading a core module called fluid-solid systems. The software tool generated detailed calculation steps to typical engineering design problems encountered in this module that contained weaknesses, flaws or even errors. Students utilized the software tool to practice identifying these weaknesses, flaws or errors in the design solutions and then present a better or correct design by applying the concepts and knowledge acquired in the module. Since the QCT software tool was built upon an existing design software tool that was able to generate the correct, detailed design calculation steps to design problems, students were able to check their own design calculations against those presented by the software tool during this second learning step, thereby engaging in and learning quantitative critical thinking via a repeated practice approach. The software tool was successful in enhancing the performance of second-year undergraduate students in solving a question that required quantitative critical thinking in the final examination of the module. The average percentage scores achieved by students for the question who reported higher frequencies of usage of the software were generally higher than those who reported lower frequencies of usage or did not utilize the software tool throughout the semester.

This article presents how young adults with intellectual disability (ID) from Sri Lanka, who had not previously used the Internet, interacted with Google search while enhancing their web search abilities throughout three web search workshops. Considering the little attention paid to the learning needs of people with ID in the current offering of web search learning tools, we iteratively developed a suite of learning tools to support our participants when they need help in the web search workshops. We employed an iterative participatory approach, with observations and semi-structured interviews, to reflect on how to design eLearning tools that enhance the participants’ interactions with web search. The qualitative thematic analysis resulted in five distinct themes on strategies to support, build on, and develop the abilities of young adults with IDs as they engage with Google search in their native language: application of existing abilities, basic skills to match learning needs, conceptual understanding, animations to facilitate visual memory, and promoting active engagement. These themes will be a starting point for understanding participants’ learning needs and behavior on web search, which would be important for future research on learning support as well as on software design.

Finding information from a large collection of documents is a complicated task; therefore, we need a method called an information retrieval system. Several models that have been used in information retrieval systems include the Vector Space Model (VSM), DICE Similarity, Latent Semantic Indexing (LSI), Generalized Vector Space Model (GVSM), and semantic-based information retrieval systems. The purpose of this study was to develop a semantic network-based search system that will find information based on keywords and the semantic relationship of keywords provided by users. This cannot be done by most search systems that only work based on keyword matching or similarities. The Waterfall development model was used, which divides the development stages into five steps, namely: (1) requirements analysis and definition; (2) system and software design; (3) implementation and unit testing; (4) integration and system testing; and (5) operation and maintenance. The developed system/application was tested by trying to find information based on various combinations of keywords provided by the user. The results showed that the system can find information that matches the keyword, and other relevant information based on the semantic relationships of these keywords. Keywords: information retrieval, search system, semantic network, web-based application

Based on reverse engineering, product innovation design and 3D-printing technology, a technical route for the rapid design and development of helmet products has been constructed, and optimize the product production process, reduce the product development cycle, improve production efficiency, and complete the personalized design of helmets to improve the comfort of personnel wearing. The point cloud data of the physical model were collected by the 3D scanner, and imported to the reverse processing software (Geomagic Studio) for data processing, surface reconstruction and surface fitting. In this way, the section curve of the repaired head surface was extracted. Then, the extracted information was sent to the forward design software (SolidWorks) through the parameter exchange command for the forward design of the helmet, and the 3D model was subsequently printed. Effectively solve the traditional design method for complex surface modelling design and manufacturing, forward software measurement deviation, modelling design process is complex and tedious, time-consuming and labor-intensive, cannot well meet the product design, processing and manufacturing process is relatively cumbersome, long production cycle and other design defects, speed up enterprise product development, shorten the cycle and reduce costs. Furthermore, the adoption of 3D printing technology to output the designed 3D digital model effectively shortens the product development cycle.

Despite the great creators’ efforts of e-learning educational materials, it is not possible to define the content of these materials specifically for all students. Based on this, it is necessary to provide in e-learning education the possibility of a more detailed interpretation of specific parts of the educational material that may be unclear to specific students. Based on this fact, we decided to take the first step in the form of software design, which will represent a virtual assistant in teaching computer science. The role of this assistant will be the ability to answer technical questions related to the presented curriculum. From an architectural point of view, it will be a set of micro-services, each of which will serve a specific task. The prerequisite is the use of decision trees to determine a specific micro-service, which will be implemented in the form of a neural network. Main aim of this paper is to provide detail description of global software architecture for such a virtual assistant

Batik is an original Indonesian heritage, that must be preserved and it is the mainstay for implementation of “Making Indonesia 4.0”. It was published many research about stamp batik manufacturing, but still have limitation of motive variation and slackly design process. This research aim to develop the way to making CNC program to be quickly, besides of high variation of batik motive. Source code in python programming language, was built for extension tool in inscape software. Extentension is used to create stamp point, depend on various motive size. Source code created in .py and .inx format will be save in “ekstensi” folder. Based on coordinate point of stamping in stamp batik, CNC program will be created, using “G-code tools” extension that was available in inscape software menubar. CNC programming used extension was built is measured about time of process and compared to manual CNC programming. CNC program that has been built then tested in CAMotics software. The results of this research show that CNC program could maked with this extension for various dimension od batik stamp and in 2, 4, 6, 8, 10, and 12 meters of cloth length. CNC programming with extension developed, can reduce time up to 96.05%.

International audience; The rise of enterprise resource planning (ERP) systems has been a major event in the software industry and it became a solution for most enterprises to manage their data and business processes. Successful ERP implementations can reduce costs by improving efficiency then lead to improved company performance and better competitive edge. Despite these benefits and the age of ERP existing for several decades still high percentage of implementation failures are documented. ERP is packaged software designed by following the best practice from the specific industry to support typical business processes in the entire industrial field, it was designed by ERP vendors and used by the organization which implement it. Since the designer and user are two independent entity misalignments between user’s needs and the software design are often happen. The misalignment define new specific requirements must be embedded into selected ERP. Requirement engineering (RE) is a main part and initial activity of software engineering concern about defines stakeholder requirements, needs and desire. Requirements engineering is the basis for efficient software implementation and quality management. Tools and theories which support RE in general are numerous nowadays; however, the task of providing a tools and theories that specializes in Requirements engineering for Enterprise resource planning systems has been addressed rarely. For that; this paper discusses modelling and verification of ERP functional requirements based on colored Petri nets (CPN) after evaluation of different Business process modelling techniques by using analytical hierarchy process (AHP). CPN considered one of powerful business process modelling techniques and using it help in stakeholder involvement and appropriate organization’s business process representation. The nature of colored Petri nets that help in verification of internal completeness and consistency of ERP functional requirements model.

One of the well-known ways to produce porous scaffolds or special three-dimensional (3D) micro-nanostructures is using the 3D printing technique. This technique requires a suitable computerized model of the scaffold using computer-aided design software or the computed tomography. The 3D printer fabricates a product by using a digital file and creates a layer-by-layer physical sample. Integrating different technologies and materials into one operational procedure can produce 3D tissue engineering scaffolds with enhanced properties. There are different tissue engineering strategies, including cell-based, factor-based, and scaffold-based strategies. In scaffold-based tissue engineering, 3D scaffolds are one of the most important applications of 3D printers, especially in medical science. In this article, a review of 3D printers, suitable for the production of soft and hard tissue engineering with different technologies is performed and several 3D printing techniques are described. Moreover, the pros and cons, and limitations of the 3D printing technique are discussed.

Due to massive thread-level parallelism, GPUs have become an attractive platform for accelerating large-scale data parallel computations, such as graph processing. However, achieving high performance for graph processing with GPUs is non-trivial. Processing graphs on GPUs introduces several problems, such as load imbalance, low utilization of hardware unit, and memory divergence. Although previous work has proposed several software strategies to optimize graph processing on GPUs, there are several issues beyond the capability of software techniques to address. In this article, we present GraphPEG, a graph processing engine for efficient graph processing on GPUs. Inspired by the observation that many graph algorithms have a common pattern on graph traversal, GraphPEG improves the performance of graph processing by coupling automatic edge gathering with fine-grain work distribution. GraphPEG can also adapt to various input graph datasets and simplify the software design of graph processing with hardware-assisted graph traversal. Simulation results show that, in comparison with two representative highly efficient GPU graph processing software framework Gunrock and SEP-Graph, GraphPEG improves graph processing throughput by 2.8× and 2.5× on average, and up to 7.3× and 7.0× for six graph algorithm benchmarks on six graph datasets, with marginal hardware cost.

RNA origami is a framework for the modular design of nanoscaffolds that can be folded from a single strand of RNA and used to organize molecular components with nanoscale precision. The design of genetically expressible RNA origami, which must fold cotranscriptionally, requires modelling and design tools that simultaneously consider thermodynamics, the folding pathway, sequence constraints and pseudoknot optimization. Here, we describe RNA Origami Automated Design software (ROAD), which builds origami models from a library of structural modules, identifies potential folding barriers and designs optimized sequences. Using ROAD, we extend the scale and functional diversity of RNA scaffolds, creating 32 designs of up to 2,360 nucleotides, five that scaffold two proteins, and seven that scaffold two small molecules at precise distances. Micrographic and chromatographic comparisons of optimized and non-optimized structures validate that our principles for strand routing and sequence design substantially improve yield. By providing efficient design of RNA origami, ROAD may simplify the construction of custom RNA scaffolds for nanomedicine and synthetic biology.

Energy efficiency in mobile computing is really an important issue these days. Owing to the popularity and prevalence of Android operating system among the people, a great number of Android smartphone applications have been developed and proliferated by the software developers. While developing these applications, developers have to keep energy consumption factor in mind, as the efficiency of an application is largely affected by it. Thus, designers and programmers endeavour to choose the best designing approaches to develop energy-efficient applications. It is imperative to assist the programmers in choosing appropriate techniques and strategies to manage power consumption. In the present research, we have investigated the effect of Android application design on its energy utilisation. For this purpose, we have practically implemented design patterns on two Android applications and evaluated their energy consumption before and after implementing these patterns. We have modelled the high-level design of these two Android applications by using software design patterns in such a way as to abate their energy requirement. We have also checked how the quality, maintainability, and efficiency of code are affected by these design patterns. The outcomes of the research can facilitate programmers to utilise these details while developing energy efficient solutions.

Using simulations for scientific discovery requires that the software used in the simulations undergoes a rigorous design and development process similar to that of the lab instruments in the experimental sciences. To devise a good design methodology, it is critical to understand the requirements, constraints, and challenges. This article describes insights from the long-term stewardship of a multiphysics multicomponent software, FLASH, that was designed more than 20 years ago for astrophysics, now serves multiple communities, and has been successful in adapting to the changing world of high-performance computing.

Your software projects never quite go as smoothly as the textbooks promise, do they? The article explains where the fog of design comes from and tells you how to handle it.

The article discusses the use of AutoCAD Civil 3D engineering system with the software for rice fields designing. The article contains the examples of graphic materials (rice field model) created in AutoCAD Civil 3D based on the design results in RIS software.

Spatial ability is considered a major factor of intelligence and is increasingly important in times of digitization. This article explores the fostering of spatial ability through computer-aided design software. Different notions of spatial ability will be discussed, and, finally, a concept consisting of five aspects will be described. In addition, literature reviews on the connection between the use of computers and the fostering of spatial ability, as well as on the use of 3D printing technology in mathematics education, are given. Building on this, a case study is presented which examines the work of two middle-school students using computer-aided design software within a workshop at the University of Siegen. From the data material, basic possible actions within such software are derived. These are, based on theory, connected with the five aspects of the specific concept of spatial ability used. The results show various perspectives for the fostering of spatial ability with computer-aided design software.