Software testing is an integral yet time-consuming component of software development, and even with capable software testing resources (technology and people), it is still regarded as problematic. Test automation is widely being utilised within the software industry to provide increased testing capacity to ensure high product quality and reliability. In this paper, we are specifically addressing automated testing whereby test cases are manually written. Test automation has its benefits, drawbacks, and impacts on different stages of development. Furthermore, there is often a disconnect between non-technical and technical roles, where non-technical roles (e.g., management) predominantly strive to reduce costs and delivery time whereas technical roles are often driven by quality and completeness. Although it is widely understood that there are challenges with adopting and using automated testing, there is a lack of evidence to understand the different attitudes toward automated testing (AtAT), focusing specifically on why it is not adopted. In this paper, the authors have surveyed practitioners within the software industry from different roles to determine common trends and draw conclusions. A two-stage approach is presented, comprising a comprehensive descriptive analysis and the use of Principle Component Analysis. In total, 81 participants were provided with a series of 22 questions and their responses are compared against job role types and experience levels. In summary, 6 key findings are presented covering expertise, time, cost, tools and techniques, utilisation, organisation, and capacity

This manuscript explores the significance of continuous improvement in software development and digital product management, highlighting its role in addressing challenges and achieving superior results in today's dynamic business landscape. Drawing on Lean Thinking and Agile principles, the paper emphasizes the importance of customer focus, multidisciplinary collaboration, respect for people, inspection and adaptation, experimentation, incremental delivery, and continuous feedback in driving continuous improvement. It identifies key problems associated with the lack of continuous improvement, including quality issues, stagnation, inefficiency, lack of learning, lack of participation and collaboration, and lack of adaptability. To overcome these challenges, the paper proposes a solution that leverages agile events and interactions, such as sprint retrospectives and daily scrum meetings, and introduces a "kaizen board" as a visual tool to manage and monitor the continuous improvement process. The manuscript highlights the benefits of continuous improvement in terms of quality enhancement, efficiency gains, improved customer satisfaction, and adaptability to change. It stresses the need for fostering a culture of learning and constant adaptation, and provides insights on applying continuous improvement across different dimensions: product, process, team, and culture. The paper concludes by emphasizing the significance of commitment and active participation from all team members for achieving sustainable progress and success in digital transformation efforts.

Agricultural production needs technologies that assist the management of natural resources, for example, the collection of real-time data on soil, water, weather, crops, and biodiversity conditions. Sensor technology solutions and open-source software are appropriate for promoting more sustainable agricultural production. Among the advantages of using open-source technologies and software is its potential for extension, collaboration, customization, flexibility, maintenance cost, transparency, speed, and better security. Given the above, the objective of this research was to find, in different electronic databases, exclusively open-source software for precision agriculture, offering a systematic review, and addressing considerations and challenges. This survey considers up-to-date open-source software available in repositories such as GitHub and GitLab, to understand its characteristics and application formats.

In this paper we present AWEbox, a Python toolbox for modeling and optimal control of multi-aircraft systems for airborne wind energy (AWE). AWEbox provides an implementation of optimization-friendly multi-aircraft AWE dynamics for a wide range of system architectures and modeling options. It automatically formulates typical AWE optimal control problems based on these models, and finds a numerical solution in a reliable and efficient fashion. To obtain a high level of reliability and efficiency, the toolbox implements different homotopy methods for initial guess refinement. The first type of methods produces a feasible initial guess from an analytic initial guess based on user-provided parameters. The second type implements a warmstart procedure for parametric sweeps. We investigate the software performance in two different case studies. In the first case study we solve a single-aircraft reference problem for a large number of different initial guesses. The homotopy methods reduce the expected computation time by a factor of 1.7 and and the peak computation time by a factor of 8, compared to when no homotopy is applied. Overall, the CPU timings are competitive to timings reported in the literature. When the user initialization draws on expert a priori knowledge, homotopies do not increase expected performance, but the peak CPU time is still reduced by a factor of 5.5. In the second case study, a power curve for a dual-aircraft lift-mode AWE system is computed using the two different homotopy types for initial guess refinement. On average, the second homotopy type, which is tailored for parametric sweeps, outperforms the first type in terms of CPU time by a factor of 3. In conclusion, AWEbox provides an open-source implementation of efficient and reliable optimal control methods that both control experts and non-expert AWE developers can benefit from.

The development of quantum computers seeks software developers' attention regarding security in the era of Information Technology, software security is the primary goal for our quantitative assessment of software security in the development cycle of software. Security assessment of software is identifying the key security factors of the software. A security elective provides ex-tensive strategies and calculations to ensure product safety. The security assessment is the key factor in surveying, administering, and controlling security to further enhance the nature of safety. It should be acknowledged that assessing security early on in the development process is beneficial in identifying worms, hazards, flaws, and threats. The definition and portrayal of Quantum Computing (QC) in software security will be discussed in this study. Researchers use cryptography calculations to secure our financial institutions, medical devices, military weaponry, planes, ships, vehicles, and pilots. Here authors of this study use the Fuzzy Technique for Order Preference by Similarity to Ideal Situation (FTOPSIS) to quantitatively assess the weight/rank of the quantum enable security alternatives like (Diffie-Hellman key-exchange algorithm, Quantum key distribution algorithm, Deutsch-Jozsa Algorithm, Special Deutsch-Jozsa Algorithm, Grover’s Algorithm and Quantum key distribution algorithm in GHZ state) with security factors like (Confidentiality, Integrity, Authenti-cation, Privacy, Reliability, Maintainability, Authorization, Integrity, Possessions, and Availability). Additionally, they critically analyze and select the six alternatives of quantum-based security algorithms. The nature of safety infers the ability to execute a thing on time in this exploration study, specifically 'software security'.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal man-agement. This study aims to develop and to validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule formula implemented in GEE and is capable to determine the coastline retreat of a profile through the creation of a simple vector line from topo-bathymetric data. The model shows a very high cor-relation (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE platform seems to be an important tool for coastal management. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific commu-nity.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence 15 of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal 16 management. This study aims to develop and to validate two different models to predict the 17 sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for plan-18 etary-scale environmental data analysis. The first model is a Bathtub Model based on the uncer-19 tainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and 20 Modeling System software. The validation process performed in the Rio Grande do Sul coastal 21 plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule for-22 mula implemented in GEE and is capable to determine the coastline retreat of a profile through the 23 creation of a simple vector line from topo-bathymetric data. The model shows a very high correla-24 tion (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE 25 platform seems to be an important tool for coastal management. The models developed have been 26 openly shared, enabling the continuous improvement of the code by the scientific community.

Security patterns encompass security-related issues in secure software system development and operations that often appear in certain contexts. Since the late 1990s about 500 security patterns have been proposed. Although the technical components are well investigated, the direction, overall picture, and barriers to implementation are not. Here, a systematic literature review of 240 papers is used to devise a taxonomy for security pattern research. Our taxonomy and the survey results should improve communications among practitioners and researchers, standardize the terminology, and increase the effectiveness of security patterns.

DevOps is an emerging practice to be followed in the Software Development life cycle. The name DevOps indicates that it’s an integration of the Development and Operation team. It is followed to integrate the various stages of the development cycle. DevOps is an extended version of the existing Agile method. DevOps aims at continuous integration, Continuous Delivery, Continuous Improvement, faster feedback and security. This paper reviews the building blocks of DevOps, challenges in adopting DevOps, Models to improve DevOps practices and Future works on DevOps

We present software on total energy calculation by quantum mechanics first principle method with a graphic user interface (GUI). Total energy calculation in this software is based on numerical analysis of time-dependent density functional (the used numerical method is finite difference time domain). QUMEC package has been equipped by common exchange-correlation energy terms with electron spin polarization calculation. With this package, users can calculate the total energy of the free particle, bulk materials, and materials with free surfaces at the atomic scale. The package is tested by several physical subjects, i.e., the surface energy of nano-LiCoO₂ and diffusion constant of lithium atoms in LiNi­_0.5Mn_1.5O₄.

This code provides computational facilities to simulate current versus time during the charging of Li-ion cells at desire constant voltage by considering multiscale physical phenomena. This code only considers a powder of active materials (at microscale or nanoscale) and a small part of electrolyte around it as a half cell. Then it is extended to a complete cell by applying correct boundary conditions. This code is very useful by modifying code parameters to understand the effect of the complex shape of active materials powder (surface area and powder size), kind of electrolyte, and the applied voltages on the charging response of Li-ion cell. As a summary, a microscale approach to the design of Li-ion cells has been provided via this code.

The crux of the paper is to present a detailed analysis of COVID-19 data which is available on global basis. This analysis is performed using some specific package of R software. It provides various insights from the data and help to understand the current status of this pandemic in India so that effective measures can be formulated by policymakers. These insights include global summary of this disease, growth rate of this pandemic and performance of SIR model for the given global data. The analysis has been presented in different tables and graphs to understand the outputs of the problem in a more detailed point of view.

A novel software engineering platform called the Dynamic Nuchwezi Architecture Platform (DNAP) is introduced, specified and its novelties explained. The unique features of this platform are explained and several new concepts and abstractions upon which its implementation, usage, and analysis are hinged also elaborately discussed. The motivations for this new approach to building especially tools used in data engineering are spelled out and the platform is contrasted against other existing technologies of a similar kind. Finally, it is shown what known limitations DNAP suffers, as well as what room for further research and improvement there is in this field.

Molecular visualisation is fundamental in the current scientific literature, textbooks and dissemination materials, forming an essential support for presenting results, reasoning on and formulating hypotheses related to molecular structure. Visual exploration has become easily accessible on a broad variety of platforms thanks to advanced software tools that render a great service to the scientific community. These tools are often developed across disciplines bridging computer science, biology and chemistry. Here we first describe a few Swiss Army knives geared towards protein visualisation for everyday use with an existing large user base, then focus on more specialised tools for peculiar needs that are not yet as broadly known. Our selection is by no means exhaustive, but reflects a diverse snapshot of scenarios that we consider informative for the reader. We end with an account of future trends and perspectives.

A university canteen is a queueing system characterised by non-stationary time of arrival with limited resources where the arrival rate is time dependent and has different pattern of arrival for different time interval. This means at certain time of the day, the arrival rate is much higher than other time and for a university canteen, the arrival rate of customer during the lunchtime is much higher and the food (resources) is limited. Non-stationary time dependent queueing system is not easily modelled mathematically hence such queueing systems are modelled using simulation tools such as ARENA. In order to model a non-stationary time dependent queueing system with limited resources and solve queueing problems using ARENA, researchers have to depend on their knowledge and experience in identifying the appropriate and relevant parameters for the system and make modifications to these parameters of the system to solve queueing problems by means of trial and error. Hence, this research work explores the potentials of applying a systematic problem solving tool, TRIZ to help users to make better decisions in deriving solutions to improve a non-stationary time dependent queueing system with limited resources. A case study was carried out to minimize the waiting time of the customers at the cafeteria of the Faculty of Engineering, Universiti Putra Malaysia (UPM), which has queueing problems for years during lunchtime. TRIZ was applied in this case study and the results showed that TRIZ can assist researchers to derive a solution model that lead to shorter waiting time without incurring additional cost and resources.

Context: Code smells are associated with poor design and programming style that often degrades code quality and hampers code comprehensibility and maintainability. Goal: Identify reports from the literature that provide evidence of the influence of code smells on the occurrence of software bugs. Method: We conducted a Systematic Literature Review (SLR) to reach the~stated goal. Results: The SLR includes selected studies from July 2007 to September 2017 which analyzed the source code for open source and proprietary projects, as well, as several code smells and anti-patterns. The results of this SLR show that 24 code smells are more influential in the occurrence of bugs according to 16 studies. In contrast, three studies reported that at least 6 code smells are less influential in such occurrences. Evidence from the selected studies also point out tools, techniques and procedures applied to analyze the influence. Conclusion: To the best of our knowledge, this is the first SLR to target this goal. This study provides an up-to-date and structured understanding of the influence of code smells on the occurrence of software bugs based on findings systematically collected from a list of relevant references in the latest decade.

This paper present a platform for airborne sensor applications using low-cost, open-source components carried by an easy-to-fly unmanned aircraft vehicle (UAV). The system, available in open-source [1], is designed for researchers, students and makers for a broad range of their exploration and data-collection needs. The main contribution is the extensible architecture for modularized airborne sensor deployment and real-time data visualisation. Our open-source Android application provides data collection, flight path definition and map tools. Total cost of the system is below 800 dollars. The flexibility of the system are illustrated by mapping the location of Bluetooth beacons (iBeacons) on a ground field and by measuring water temperatures in a lake.

This article advances the discourse on sustainable and energy-efficient software by examining the performance and energy efficiency of intelligent algorithms within the framework of green and sustainable computing. Building on previous research, it explores the theoretical implications of Bremermann's Limit on efforts to enhance computer performance through more extensive methods. The study presents an empirical investigation into heuristic methods for search and optimisation, demonstrating the energy efficiency of various algorithms in both simple and complex tasks. It also identifies key factors influencing the energy consumption of algorithms and their potential impact on computational processes. Furthermore, the article discusses cognitive concepts and their interplay with computational intelligence, highlighting the role of cognition in the evolution of intelligent algorithms. The conclusion offers insights into the future directions of research in this area, emphasising the need for continued exploration of energy-efficient computing methodologies.

The increasing adoption of distributed recycling via additive manufacturing (DRAM) has facilitated the revalorization of materials derived from waste streams for additive manufacturing. Recycled materials frequently contain impurities and mixed polymers, which can degrade their properties over multiple cycles. This degradation, particularly in rheological properties, limits their applicability in 3D printing. Consequently, there is a critical need for a tool that enables rapid assessment of the flowability of these recycled materials. This study presents the design, development, and manufacturing of an open-source melt flow index (MFI) apparatus. The proposed MFI tool offers a user-friendly and cost-effective solution for evaluating the flow properties of materials from waste streams, thereby enhancing their viability for additive manufacturing applications.

Modular inversion on large operands is a time-consuming calculation used in elliptic curve cryptography. Its hardware implementation requires extensive hardware resources such as lookup tables and registers. We investigate state-of-the-art modular inversion algorithms and evaluate the performance and cost of the algorithms and their hardware implementations. We then propose a high-radix modular inversion algorithm aimed at short execution time and low hardware cost. We present a detailed radix-8 hardware implementation based on 256-bit primes in Verilog HDL and compare its cost performance with other implementations. Our implementation on the Altera Cyclone V FPGA chip uses 1227 ALMs (Adaptive logic modules) and 1037 registers. The modular inversion calculation takes 3.67 microseconds. The AT (Area time) factor is 8.30, outperforming other implementations. We also present an implementation of elliptic curve cryptography using the proposed radix-8 modular inversion algorithm. The implementation results also show that our modular inversion algorithm is more efficient in area time than other algorithms.

The installation of electric vehicle supply equipment (EVSE) increases demand and peak loads, potentially straining existing energy distribution infrastructure. Dispersed and inadequately planned placement of charging points (CPs) can disrupt the electrical grid, surpass contracted demand thresholds, and require infrastructure upgrades, thereby incurring unfeasible costs for Distribution System Operators (DSOs). In this context, it is necessary to recognize the role of business models in enabling effective electrification of the transportation sector. In response to these challenges, this paper introduces a novel e-mobility hub management strategy, tailored for implementation in the Brazilian context. The proposed strategy revolves around a microgrid configuration encompassing dispatchable and photovoltaic generation, a battery energy storage system (BESS), EVSE infrastructure, and local loads. Moreover, this centralized controller facilitates the implementation of dynamic pricing and demand-response mechanisms, integral to business models seeking to integrate EVSE into the distribution grid. To validate the efficacy of the proposed solution, hardware-in-the-loop (HIL) simulations of the microgrid system are conducted. These simulations, incorporating the centralized controller, serve as a tool for assessing system performance and viability before on-site equipment deployment. Finally, this paper concludes with the insights gleaned from test analysis and its discussion through a selection of the most expressive scenarios, including islanded and connected operation modes.

Sense element engagement theory explains how neural networks produce cortical prosthetic vision. A major prediction of the theory can be tested by developing a device which is expected to enable perception of continuous forms in altered visual geometries. The research reported here completes several essential steps in developing this device: (1) replication of simulations that are consistent with the theory using the NEST simulator, which can also be used for full-scale network emulation by a neuromorphic computer; (2) testing whether results consistent with the theory survive increasing the scale and duration of simulations; (3) establishing a method that uses numbers of spikes produced by network neurons to report the number of phosphenes produced by cortical stimulation; and (4) simulating essential functions of the prosthetic device. NEST simulations replicated early results and increasing their scale and duration produced results consistent with the theory. A decision function created using multinomial logistic regression correctly classified the expected number of phosphenes for 2080 spike number distributions for each of three sets of data, half of which arise from simulations expected to yield continuous visual forms on an altered visual geometry. A process for modulating electrical stimulation amplitude based on intermittent population recordings that is predicted to produce continuous visual forms was successfully simulated. The classification function developed using logistic regression will be used to tune this process as the scale of simulations is further increased.

There is an intense need to optimize agrivoltaic systems. This article describes the invention of a new testing system: the parametric open source cold-frame agrivoltaic system (POSCAS). POSCAS is an adapted gardening cold-frame used in cold climates as it acts as a small greenhouse for agricultural production. POSCAS is designed to test partially transparent solar photovoltaic (PV) modules targeting the agrivoltaic market. It can both function as a traditional cold frame, but it can also be automated to function as a full-service greenhouse. The integrated PV module roof can be used to power the controls or it can be attached to a microinverter to produce power. POSCAS can be placed in an experimental array for testing, agricultural and power production. It can be easily adapted for any type of partially transparent PV module. An array of POSCAS systems al-lows for testing agrivoltaic impacts from the percent transparency of the modules by varying the thickness of a thin film PV material or the density of silicon-based cells, and various forms of optical enhancement, anti-reflection coatings and solar light spectral shifting materials in the back sheet. All agrivoltaic variables can be customized to identify ideal PV designs for a given agricultural crop.

Grid energy storage system for PV Applications is connected with three different power sources i.e. PV Array, Battery and the Grid. It is advisable to have Isolation between these three different sources to provide safety for the equipment. The configuration proposed in this paper provides the complete isolation between the three sources. A Power Balancing Control (PBC) for this configuration is proposed to operate the system in three different modes of operation. Control of a dual active bridge (DAB) based battery charger which provides a galvanic isolation between batteries and other sources is explained briefly. Various modes of operation of a Grid energy storage system are also presented in this paper. Hardware-In-Loop (HIL) Simulation is carried out to check the performance of the system and the PBC algorithm. Power circuit (comprises of inverter, dual active bridge based battery charger, grid, PV cell, batteries, contactors and switches) is simulated and the controller hardware and user interface panel are connected as HIL with the simulated power circuit through Real Time Digital Simulator (RTDS). HIL simulation results are presented to explain the control operation, steady state performance in different modes of operation and the dynamic response of the system.

To stabilize the Earth’s climate a large-scale transition is needed to non-carbon emitting renewable energy technologies like wind and solar energy. Although these renewable energy sources are now lower-cost than fossil fuels, their inherent intermittency make them unable to supply a constant load without storage. To address these challenges rechargeable electric batteries are currently the most promising option; however, their high capital costs limit current deployment velocities. To both reduce the cost as well as improve performance 3D printing technology has emerged as a promising solution. This literature review provides the state-of-the-art enhancements of battery properties with 3D printing including efficiency, mechanical stability, energy and power density, customizability and sizing, production process efficiency, material conservation and environmental sustainability as well as the progress in solid-state batteries. The principles, advantages, limitations, and recent advancements associated with the most common types of 3D printing are reviewed focusing on their contributions to the battery field. 3D printing batteries offer design flexibility, material flexibility, reduces pack weight, minimizes material waste, increases the range of applications and has the potential to reduce costs. As 3D printing technologies become more accessible, the prospect of cost-effective production for customized batteries is extremely promising.

Exposure of experimental rodents to controlled cycles of light, food, and temperature is important when investigating alterations in circadian cycles that profoundly influence health and disease. However, applying such stimuli simultaneously is difficult in practice. Our aim was to design, build, test, and open-source describe a simple device that subjects a conventional mouse cage to independent cycles of physiologically relevant environmental variables. The device is based on a box enclosing the rodent cage to modify the light, feeding, and temperature environments. The device provides temperature-controlled air conditioning (heating or cooling) by a Peltier module and includes programmable feeding and illumination. All functions are set by a user-friendly front panel for independent cycle programming. Bench testing with a model simulating the CO2 production of mice in the cage showed: a) suitable air renewal (by measuring actual ambient CO2), b) controlled realistic illumination at the mouse enclosure (measured by a photometer), c) stable temperature control, and d) correct cycling of light, feeding, and temperature. The cost of all the supplies (retail purchased by e-commerce) was < 300 US$). Detailed technical information is open-source provided, allowing for any user to reliably reproduce or modify the device. This approach can considerably facilitate circadian research since using one of the described low-cost devices for any mouse group with a given light-food-temperature paradigm allows for all the experiments to be performed simultaneously, thereby requiring no changes in the light/temperature of a general-use laboratory.

The proliferation of devices for the Internet of Things (IoT) and their implication in many activities of our lives have led to a considerable increase in concern about the security of these devices, posing a double challenge for designers and developers of products. On the one hand, the design of new security primitives, suitable for resource-limited devices, that can facilitate the inclusion of mechanisms and protocols to ensure the integrity and privacy of the data exchanged over the Internet. On the other hand, the development of techniques and tools to evaluate the quality of the proposed solutions as a step prior to their deployment, as well as to monitor their behavior once in operation against possible changes in operating conditions arising naturally or as a consequence of a stress situation forced by an attacker. To address these challenges, this paper first describes the design of a security primitive that plays an important role as a component of a hardware-based root of trust, as it can act as a source of entropy for true random number generation (TRNG) or as a physical unclonable function (PUF) to facilitate the generation of identifiers linked to the device on which it is implemented. The work also illustrates different software components that allow carrying out a self-assessment strategy to characterize and validate the performance of this primitive in its dual functionality, as well as to monitor possible changes in security levels that may occur during operation as a result of device aging and variations in power supply or operating temperature. The designed PUF/TRNG is provided as a configurable IP module, which takes advantage of the internal architecture of the Xilinx Series-7 and Zynq-7000 programmable devices and incorporates an AXI4-based standard interface to facilitate its interaction with soft- and hard-core processing systems. Several test systems that contain different instances of the IP have been implemented and subjected to an exhaustive set of on-line tests to obtain the metrics that determine its quality in terms of uniqueness, reliability, and entropy characteristics. The results obtained prove that the proposed module is a suitable candidate for various security applications. As an example, an implementation that uses less than 5% of the resources of a low-cost programmable device is capable of obfuscating and recovering 512-bit cryptographic keys with virtually zero error rate.

Having access to a unique quantum circuit that one wants to protect against use by others is a very likely scenario. However currently users rely on classical computer security schemes, which have known shortcomings. In this chapter we introduce a novel protection scheme along with a survey of other known methods to protect quantum information. In particular we review physically unclonable functions (PUFs), obfuscation, and introduce quantum logic locking.

Finding out an available car parking space has been one of the most crucial problems for the crowded cities, which causes such issues as traffic congestions, air pollutions, and stressful drivers. To mitigate the issues here, smart car parking management system (SPMS) solutions can be used which digitalise the parking processes and maximise the drivers’ productiveness. However, the existing SPMS solutions are either domain-specific or address the applications of particular techniques and technologies. None of the solutions in the literature propose any generic approach that can be re-used for the analysis and design of any SPMSs. In this paper, we propose a reference architecture (RA) for the SPMS product family. Performing comprehensive domain analysis, we provide a feature model of the common and varying features for SPMSs. We designed 4 architectural viewpoints, which are context, module, component&connector, and allocation. Each viewpoint addresses a different concern and proposes a generic solution that can be re-used for any specific SPMSs. To validate our RA design, we used the commercial 4Park SPMS application and specified its architecture using our RA viewpoints. We strongly believe that our RA design for SPMSs can be useful for the SPMS domain who develop SPMS solutions.

This review article explores the innovative application of software sensors for monitoring and controlling biosystems, emphasizing their advantage in managing the complexities of various biological processes. Biosystems—from cellular interactions to ecological dynamics—are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors bridge this gap by combining hardware sensor data with advanced computational modelling techniques to indirectly infer hard-to-measure target variables, such as stress level, animal and human health indicators, and chemical soil properties. The article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of Kalman filter in greenhouse control, remote detection of soil organic matter, and sound recognition algorithms for early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This article positions software sensor as remarkable tool for advancing biosystem management, driving forward the potential for sustainable practices in various sectors such as agriculture, healthcare, and environmental monitoring.

In the field of music education, the incorporation of technology originally designed for professionals presents both significant opportunities and challenges. These technologies, although advanced and powerful, are often not adapted to meet the specific needs of the educational envi-ronment. Therefore, this study details the design and implementation process of a system con-sisting of a hardware device called "Play Box" an associated software, "Imaginary Play Box". The Design Sciences Research Methodology (DSRM) specifically adapted to software development was used to structure the project. The three phases shown in this study ranged from the conception of an initial prototype to the realisation of a working software. During the design phase, a questionnaire was developed to evaluate various aspects of the soft-ware, such as the visual interface, the programming of components and the sound interactivity provided by the Play Box. The technique of panels of experts in music pedagogy and program-ming in MAX-MSP was used to obtain critical feedback. This expert evaluation was crucial to it-erate and polish the software, culminating in a beta version optimised for the creation of electro-acoustic music for music education.

Currently, health tourists primarily rely on independent facilitators to manage and book their medical appointments and vacation plans. There is a notable absence of dedicated booking applications for health tourism packages. This paper proposes HealthTourismHub, an application designed to provide personalized packages that include medical appointments, accommodation options and recommended tourism activities. It also serves as a platform for medical experts and accommodation providers, allowing health tourists to discover and connect with them. To incorporate personalization, HealthTourismHub uses an ontology that organizes medical and tourism data, along with a reasoner that generates new knowledge. This approach enables the application to offer customized packages and identify the most suitable providers for each user. Providers are strategically paired and located in close proximity, with the package also including personalized tourism recommendations. A survey was conducted to assess the usability of the application and general perspectives towards health tourism, including motivations, concerns, and preferences. The results revealed an above-average SUS score, indicating that users found the application user-friendly and effective. Some areas for improvement were identified, such as error handling and additional functionalities. Nonetheless, HealthTourismHub shows great potential as a pioneer in the field of health tourism applications.

Ghana Tamale's current waste management system is overwhelmed by the growing waste crisis, necessitating the exploration of Waste-to-Energy technologies as an innovative solution. This study explores challenges and opportunities for integrating Waste-to-Energy (WtE) technologies into Tamale's existing waste management system. Qualitative research identified policy hurdles, financial constraints, social concerns, and institutional weaknesses as key barriers. Conversely, stakeholder collaboration, public education, and capacity building emerged as some success fac-tors. A proposed implementation plan emphasizes feasibility studies, public engagement, pub-lic-private partnerships (PPPs), and technology selection based on waste composition. Addition-ally, a novel framework for WtE integration within a broader Integrated Solid Waste Manage-ment (ISWM) system is presented. This framework incorporates waste sorting, recycling, WtE alternatives, and responsible management of residuals. Validation by key stakeholders con-firmed the framework's potential to improve waste management in Tamale. By overcoming identified challenges and implementing the proposed framework, Tamale can achieve environ-mental sustainability, energy generation, and progress towards UN SDGs 7, 11, and 12. This re-search offers valuable insights for policymakers and paves the way for a cleaner and more sus-tainable future for Tamale, potentially serving as a model for other developing cities.

Multiple sequence alignments and phylogenetic trees are rich in biological information and are fundamental to research in biology. PhyKIT is a tool for processing and analyzing the information content of multiple sequence alignments and phylogenetic trees. Here, we describe how to use PhyKIT for diverse analyses, including (i) constructing a phylogenomic supermatrix, (ii) detecting errors in orthology inference, (iii) quantifying biases in phylogenomic data sets, (iv) identifying radiation events or lack of resolution using gene support frequencies, and (v) conducting evolution-based screens to facilitate gene function prediction. Several PhyKIT functions that streamline multiple sequence alignment and phylogenetic processing—such as renaming FASTA entries or tree tips—are also discussed. These protocols demonstrate how simple command-line operations in the unified framework of PhyKIT facilitate diverse phylogenomic data analysis and processing, from supermatrix construction and diagnosis to gene function prediction.

Artificial intelligence (AI) has witnessed an exponential increase in its use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software engineering problems. However, a comprehensive and holistic understanding of the current status is missing. To close the above gap, synthetic knowledge synthesis was used to induce a research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and five themes, namely natural language processing in software engineering, use of artificial intelligence in the management of software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n=2042), followed by the United States (n=1193), India (n=934), Germany (n=445), and Canada (n=381). A high percentage (n=47.4%) of papers were funded, showing a strong interest in this research topic. The convergence of AI and software engineering can significantly reduce needed resources, improve quality, increase user experience, and improve the well-being of software developers.

The Distributed eLearning Center (DeLC) is a portal, providing extensive support in the day-to-day work when it comes to e-learning content – it helps students and teachers organize their learning materials, fill the gaps in knowledge (for students) and educational approaches (for teachers), organize and conduct exams, and overall, with providing proactive and personalized e-learning environment. The scope of DeLC as a project involves many extensions, covering the aspects of learning, teaching, exams, and collecting statistical information. Such extension is an agent-oriented environment, which enriches the functionalities with intelligent components, that are reactive and proactive, referred to as agents or assistants. This paper is focused on presenting the latest step in the evolution of FraudDetector software agent, which started with base functionality for fraud detection, and now it aims at usage of AI to accomplish its tasks, taking advantage not only from its knowledgebase, but from a much larger one, used by ChatGPT – through integration with it, which is the main contribution of this research, although the real results from production environment are still pending. In this process, agent’s architecture should stay open for collaboration with another external AI provider if necessary, trying to decouple the components, responsible for integration. As of now, the experiments show that involving ChatGPT in FraudDetector’s functionality enriches it and the agent’s precision could be improved this way.

The growing of Brain-Computer Interface (BCI) applications is closely related to the increasing accessibility of Electroencephalography (EEG) hardware (EEG headsets), which are noninvasive, portable, wireless and often with open software. However, there is a limited number of BCI software platforms tailored to help inexperienced programmers in the development of BCI applications. Only few of them integrate BCI applications with IoT devices and services. To address these challenges, a model for visual node-based programming has been designed utilizing BrainFlow library within the Node-RED platform, which can be applied to more than 20 biosensors. New Nodes for Node-RED Library within OpenBCI Category (openBCI toolkit for Node-RED) have been developed for design of EEG-Based Brain-Machine Interface and Integration in IoT. The proposed toolkit have been implemented and validated through a case study for controlling a robotic arm by OpenBCI headset. The results from the pilot experiment demonstrated that through concentration levels classified by BrainFlow performance metrics, the control of a TinkerKit Braccio robot arm is possible.

The protection of first responders from radioactive contamination of alpha emitters that may result from a radiological accident is of great complexity due to the short range of the alpha particle in air of a few cm. To overcome this issue, for the first time a system for near real-time measuring alpha particles remotely mounted on a UAS has been developed, tested and calibrated. The new system, based on an optical system adapted to be installed on the UAS in order to measure the UV-C fluorescent emitting by the alpha particle in air, has been tested and calibrated carried out by laboratory and in field experiments using UV-C leds and ²⁴¹Am sources. In experimental flights, the probability of detecting a point source was determined to be approximately 60%. In case of a surface extended source, a detection efficiency per unit surface activity of ten counts per second per MBq cm^-2 are calculated. A background count rate of UV-C around 26 ± 28 s^-1 for an integration time of 0.1 s was measured during flights, which leads to a decision threshold surface activity of 5 MBq cm^-2.

Open-source devices are nowadays used in a vast number of research fields like medicine, education, agriculture, and sports among others. In this work, an open-source, portable, low cost pH logger, appropriate for in situ meas-urements was designed and developed to assist in experiments on agricultural produce manufacturing. Τhe device was calibrated manually using pH buffers for values of 4.01 and 7.01. Then it was tested by manually measuring pH from the juice of citrus fruits. A waterproof temperature sensor was added to the device for temperature compen-sation when measuring pH. A formal method comparison process between the open-source device and a Hanna HI9024 Waterproof pH Meter was designed to assess their agreement. We derived indices of agreement and graph-ical assessment tools using mixed-effects models. Advantages and disadvantages for interpreting agreement through the proposed procedure are discussed. In our illustration, indices reported mediocre agreement and the subsequent similarity analysis revealed a fixed bias of 0.22 pH units. After recalibration, agreement between the devices im-proved to excellent levels. The process can be followed in general to avoid misleading or over-simplistic results of studies reporting solely correlation coefficients for formal comparison purposes.

Acceleration rates of vehicles are crucially important statistics needed in two main aspects: first, they are essential to incident reconstructor professionals as having a verified database on where basing the cinematic reconstructions is definitely difficult and moreover, they are useful in order to understand how to create the safest possible environment on the road. This key topic gives es-sential information for road design like intersection schemes, acceleration and deceleration lane length as well as traffic simulation modelling and furthermore, to educate vehicle drivers when entering the traffic flow and to provide limit acceleration values needed to develop autonomous vehicle’s software in order to guarantee a comfort drive. Literature reports tons of linear acceleration stats mainly referred to vehicle performances claimed by car manufacturers, but these data don’t recreate real drivers’ behaviour when entering the road by turning left or right or crossing an intersection. This work aims to integrate the most reliable data from literature with a hybrid and an electric vehicles statistic, adding also two contemporary thermal engine vehicles, extracted conducting probes that recreates real road environment. The entirety of these experiments reconstructs the scenario in which a car is entering the traffic of a two-lanes road, from a stationary position at a stop sign and the acceleration rates are collected using a M.E.M.S. (Microelectromechanical system). Finally, the space needed between the car that is about to enter or crossing the road and the car that is approaching the intersection is calculated, basing the computations on the acceleration values extracted from the probes. These spaces are pictured, as a way to give a concrete idea to readers.

Evaluating mutation testing behavior can help decide whether refactoring successfully maintains the expected initial test results. Moreover, manually performing this analytical work is both time-consuming and prone to errors. This paper extends an approach to assess test code behavior and proposes a tool called MeteoR. This tool comprises an IDE plugin to detect issues that may arise during test code refactoring, reducing the effort required to perform evaluations. A preliminary assessment was conducted to validate the tool and ensure the proposed test code refactoring approach is adequate. By analyzing not only the mutation score but also the generated mutants in the pre- and post-refactoring process, results show that the approach is capable of checking whether the behavior of the mutants remains unchanged throughout the refactoring process. This proposal represents one more step toward the practice of test code refactoring. It can improve overall software quality, allowing developers and testers to safely refactor the test code in a scalable and automated way.

River monitoring has the potential to grow substantially with present-day available affordable and locally sourced hardware. Yet river monitoring networks are still under pressure due to lack of resources, difficulties with maintenance, and rapidly changing conditions which in part may be due to climate change. We advocate that to turn around this trend, monitoring stations must rely on local people, work with locally available devices, work without contact with water and operate through openly available knowledge. River observations with camera videos have this potential. IP cameras, drones or smartphones are widely available as observation platforms. The scientific methods are well established in literature. Yet current attempts to establish scalable open-source software solutions that can be operated anywhere are lacking. In fact, currently available software is either research-oriented, more aimed at incidental observations, is restricted to single-use licenses, entirely proprietary, or restricted to operations through a third-party Software as a Service. To overcome this obstacle, we present OpenRiverCam, a free and open-source software ecosystem for river observations that can fulfill a wide variety of use cases and business cases through a well-documented and simple to use Application Programming Interface, service workflows, cloud scalability and interoperability, and options to extend to several applications within the hydrological, hydrodynamic, environmental and geospatial domains. We demonstrate its current technical abilities through three different case studies that all originate from a user perspective. We discuss the future developments to meet further requirements, which include documentation, widely available training materials and embedding in curricula, and further hardware and software developments.

Flexible implementations of Monte Carlo Tree Search (MCTS), combined with domain specific knowledge and hybridization with other search algorithms, can be a very powerful for the solution of problems in complex planning. We introduce mctreesearch4j, a standard MCTS implementation written as a standard JVM library following key design principles of object oriented programming. We define key class abstractions allowing the MCTS library to flexibly adapt to any well defined Markov Decision Process or turn-based adversarial game. Furthermore, our library is designed to be modular and extensible, utilizing class inheritance and generic typing to standardize custom algorithm definitions. We demon- strate that the design of the MCTS implementation provides ease of adaptation for unique heuristics and customization across varying Markov Decision Process (MDP) domains. In addition, the implementation is reasonably performant and accurate for standard MDP’s. In addition, via the implementation of mctreesearch4j, the nuances of different types of MCTS algorithms are discussed.

The success of a software product depends on several factors. Given that different organizations and institutions use software products, the need to have a quality and desirable software according to the goals and needs of the organization makes measuring the quality of software products an important issue for most organizations and institutions. To be sure of having the right software. It is necessary to use a standard quality model to examine the features and sub-features for a detailed and principled study in the quality discussion. In this study, the quality of Word software was measured. Considering the importance of software quality and to have a good and usable software in terms of quality and measuring the quality of software during the study, experts and skilled in this field were used and the impact of each factor and quality characteristics. It was applied at different levels according to their opinion to make the result of measuring the quality of Word software more accurate and closer to reality. In this research, the quality of the software product is measured based on the fuzzy inference system in ISO standard. According to the results obtained in this study, it is understood that quality is a continuous and hierarchical concept and the quality of each part of the software at any stage of production can lead to high quality products.

In the software development process, the decision-maker (DM) has a range of problems inherent to its function. Wrong choices during software planning can bring great risk to the project. Therefore, the planning of software releases to be delivered to the customer should be well done. This is not an easy task because releases are made up of many requirements that contain complex variables that must be considered, such as precedence, cost, requirement stability, among other features that make the requirements-selection process challenging. To make this process less exhaustive, DM can use tools that facilitate this work. In software engineering, we can find fields of research specialists in this context, such as Search-Based Software Engineering (SBSE). The SBSE makes use of advanced metaheuristics to search for optimal solutions or the closest to it. In this work, we try to use another field of research to solve this same problem type, the Verbal Decision Analysis (VDA). To do this, we elaborate a workflow that will use the same source data, execute two solutions using the two search fields (SBSE and VDA) and compare the results. In the end, we evaluated and commented on the results.

During software development requirement gathering is an important phase. Requirements are the basis of software development. The success or failure of any software depends upon level of understanding developed in requirements. During software development requirements keeps on changing due to different reasons. Hence requirements are such a critical phase that leads to the total project failure. So, to understand the impacts and to identify the conflicts with existing requirements, it is important to manage and analyze the requirements well. Requirement change management is the interest of this paper. Different requirement change management techniques has been discussed in this paper and analyzed them well and finally conclude the results accordingly.

It is a common practice to detect outliers in a financial time series in order to avoid the adverse effect of additive outliers. This paper investigated the performance of GARCH family models (sGARCH; gjrGARCH; iGARCH; TGARCH and NGARCH) in the presence of different sizes of outliers (small, medium and large) for different time series lengths (250, 500, 750, 1000, 1250 and 1500) using root mean square error (RMSE) and mean absolute error (MAE) to adjudge the models. In a simulation iteration of 1000 times in R environment using rugarch package, results revealed that for small size of outliers, irrespective of the length of time series, iGARCH dominated, for medium size of outliers, it was sGARCH and gjrGARCH that dominated irrespective of time series length, while for large size of outliers, irrespective of time series length, gjrGARCH dominated. The study further leveled that in the presence of additive outliers on time series analysis, both RMSE and MAE increased as the time series length increased.

This study compared two object-oriented land use change detection methods—detection after classification (DAC) and classification after detection (CAD) —based on a digital elevation model, slope data, and multi-temporal Landsat images (TM image for 2000 and ETM image for 2010). We noted that the overall accuracy of the DAC (86.42%) was much higher than that of the CAD (71.71%). However, a slight difference between the accuracies of the two methods exists for deciduous broadleaf forest, evergreen coniferous forest, mixed wood, upland, paddy, reserved land, and settlement. Owing to substantial spectrum differences, these land use types can be extracted using spectral indexes. The accuracy of DAC was much higher than that of CAD for industrial land, traffic land, green shrub, reservoir, lake, river, and channel, all of which share similar spectrums. The discrepancy was mainly because DAC can completely utilize various forms of information apart from spectrum information during a two-stage classification. In addition, the change-area boundary was not limited at first, but was adjustable in the process of classification. DAC can overcome smoothing effects to a great extent using multi-scale segmentations and multi-characters in detection. Although DAC yielded better results, it was more time-consuming (28 days) because it uses a two-stage classification approach. Conversely, CAD consumed less time (15 days). Thus, a hybrid of the two methods is recommended for application in land use change detection.

This paper investigates the reconfigurable intelligent surface (RIS)-aided uplink multicell massive multiple-input multiple-output (mMIMO) communication system with transceiver hardware impairments (THWIs), and the practical and feasible two-timescale scheme is used to design the phase shifts of RIS. We consider the Rician channel model and use maximal-ratio combining (MRC) technology to process the received signal at BS. The expression of the uplink achievable rate is derived and analyzed. Besides, genetic algorithm (GA) is used to optimize the phase shifts of RIS to maximize the data rate. Finally, the accuracy of the derived results is verified. The simulation results show that appropriately increasing the number of RIS’s reflecting elements can compensate for the performance loss caused by inter-cell interference and THWIs, and reduce the demand for the number of BS antennas, which can significantly reduce the hardware costs at the BS.

This work studies a novel low voltage (<60 V) hybrid system that supports engine boosting and downsizing in addition to start-stop, regenerative braking, and constrained torque assist/regeneration. The hybrid power split supercharger (PSS) shares a 9 kW motor between supercharging the engine or providing hybrid functionalities through a planetary gear set, a brake and a bypass valve. The PSS operation is limited to only one of the parallel hybrid or boosting modes at a time, necessitating a highly optimized decision making algorithm to select the device mode and power split ratio. In this work an adaptive equivalent consumption minimization strategy (A-ECMS) is developed for energy management of the PSS. The A-ECMS effectiveness is compared against a dynamic programming (DP) solution with full drive cycle preview through hardware-in-the-loop experiments on an engine dynamometer testbed. The experiments show that the PSS with A-ECMS reduces a vehicle fuel consumption by 18.4% over standard FTP75 cycle compared to a baseline turbocharged engine, while global optimal DP solution decreases the fuel consumption by 22.8% compared to baseline.

The majority of E-commerce transactions reveal private information such as customers' identities, order contents, and payment information during the transaction. Other personal information such as health conditions, religion, and even ethnicity may be also deduced. Even when deploying electronic cryptocurrencies such as Bitcoin, anonymity cannot be fully guaranteed. Also, many anonymous payment schemes suffer from possible double spending circumstances. E-commerce privacy is basically a difficult problem as it involves parties with concurring interests. Three major e-commerce requirements are highly difficult to resolve: anonymous purchase, anonymous delivery, and anonymous payment. This work presents a possible e-commerce system addressing all three anonymity requirements for electronic-items business on open networks. The system offers anonymous entities authentication mechanisms up to completing a fair anonymous e-commerce transaction. The system is based on deploying a physically clone-resistant hardware token for each relevant involved party. The tokens are made clone-resistant by accommodating a Secret Unknown Cipher (SUC) in each hardware-token as a digital PUF-like identity. A set of novel generic system-setups for units, protocols and e-commerce schemes is introduced. The proposed anonymization is basically attained by virtually-replacing relevant e-commerce entities by low-cost, unique and clone-resistant tokens/units using SUCs. The units act as trustable anonymous, authenticated and non-replaceable entities monitored by their acting users.

In this study, we focus on the vibration-damping devices within the renowned physical experimental apparatus—STM. Utilizing GeoGebra software and Python, we meticulously elucidate the principle by which the vibrations of the damper are damped in a magnetic field, thereby achieving the vibration-damping functionality of the entire system. This approach not only fosters a clear physical conceptualization but also ensures the acquisition of accurate and comprehensive knowledge. Furthermore, the inquiry demonstrates the process of problem clarification facilitated by the use of software tools and then we compare the power of the widlely programming language--Python and the GeoGebra.

Users with Autism Spectrum Disorder may find human-computer interaction (HCI) challenging due to a number of symptoms experienced, one of which is difficulty coping with change. Through capturing, comparing and statistically analysing the reactions of autistic and neurotypical users to seven individual design changes, it has been possible to identify the changes that cause significant difficulty in autistic users. These difficulty points were then used to form heuristics that can followed by HCI practitioners in the industry to reduce the negative impact of software interface design change on autistic users, hence making software updates more accessible.

A novel investigation into the structure-function relationship was undertaken to enhance understanding of the mechanism of action of the vasoactive peptide angiotensin II (AngII) using unconventional substitutions such as AIB (α-aminobutyric acid), CHA (cyclohexylalanine acid), IAP (isonipecotic acid), TOAC (2,2,6,6-tetramethylpiperidine-1-oxyl-4-amine-4-carboxylic acid) at position 3, and DMT (2,6-dimethyl-Tyrosine) at position 4. Biological potency assays were conducted on guinea pig ileum, and structural features were analyzed using circular dichroism (CD) and molecular dynamics (MD). CHA3-AngII and AIB3-AngII analogs retained some agonistic activity, whereas IAP3-AngII and TOAC3-AngII derivatives were inactive. CD results indicated low conformational flexibility for AIB3-AngII and CHA3-AngII, and greater conformational restrictions for IAP3-AngII and TOAC3-AngII, consistent with computational modeling analyses. DMT4-AngII exhibited significant agonistic activity compared to the inactive Phe4AngII. Molecular modeling suggested a potential interaction between the hydroxyl group of Tyr4 and the amide bond between positions 7 and 8. AngII showed greater conformational restriction compared to DMT4-AngII and Phe4-AngII, underscoring the importance of tyrosine at position 4.

Software systems are often maintained by a group of experienced software developers, in order to ensure that faults which may bring the system down are less likely. Large turnover in organizations such as CERN makes it important to think of ways of onboarding newcomers on a technical project rapidly. This paper focuses on optimizing the way that people get up-to-speed on the business logic and technologies used on the project, using a knowledge-imbued large language model that is enhanced using domain-specific knowledge from the group or team’s internal documentation.

Construction using earth materials demonstrates ecological sustainability using locally sourced natural materials and environmentally friendly demolition methods. In this study, the environmental impact of adding cement to soil materials for rammed-earth farmhouse construction in rural China was investigated and comparatively simulated using the One Click LCA database, focusing on the conflict between sustainability objectives and the practical aspects of cement addition. By analyzing how the addition of cement aligns with local construction practices and by addressing the debate surrounding the inclusion of cement in rammed earth construction, our objective is to provide insights into achieving a balance between the environmental impact and the pragmatic considerations of using cement in earthen building practices. Three local structure scenarios are evaluated via simulations: a cement-stabilized rammed earth wall, a fired brick wall, and a localized reinforced concrete frame structure. The quantitative environmental impacts are assessed, and the qualitative differences in adaptation, economic sustainability, and other factors are examined in the context of present-day development in rural China. The results show that the use of cement-stabilized rammed earth wall-supported structures is associated with higher embodied carbon emissions compared to structures supported by reinforced concrete frames and enclosed by brick walls; however, these emissions are lower than those for brick wall-supported structures, while effectively meeting the structural requirements. In addition, the use of cement-stabilized earth for perimeter walls simplifies material management and disposal throughout the building's life cycle, and the cost-effectiveness of cement has been found to be substantially greater than that of reinforced concrete frames and brick structures, improving economic viability and social acceptability, especially among low-income communities in rural areas.

The paper concerns with the identification of current communication technologies and software functionalities from the field of digitalization and Industry 4.0, from the point of view of benefits and applicability for the machine manufacturer. In order to identify the relevant technologies and functionalities, both a descriptive and statistical literature research was carried out. As a result, both the currently most widespread technologies and eleven IT functionalities relevant to mechanical engineers were identified and described in terms of their application and implementation. In addition, a knowledge gap in the area of industry transfer in the field of Industry 4.0 / Digitalization was identified

Software-In-The-Loop (SITL) simulation tools have been extensively used in development of safety-critical software. Utilizing these tools substantially accelerates software development eliminating potential risks and resource costs of physical experiments. This paper investigates the effects of model specific parameters on the development and testing of custom module in PX4 SITL environment. Models of a fixed-wing unmanned aerial vehicle with vertical takeoff and landing capabilities and steerable sensor platform/gimbal are used as case study for this investigation. The effects of parameters of these aerial vehicle and sensor platform models are taken into consideration in the development of custom module that controls the dynamics of steerable sensor platform mounted on the vehicle model. The work also presents steps necessary to customize PX4 SITL and Gazebo simulation environment to incorporate parameters of the vehicle and sensor platform models in the development and testing process of custom module. Based on these instances, simulation results are obtained and discussed. The results show that reliability of PX4 SITL simulation framework is justified by proper customization and integration of PX4 SITL and Gazebo simulator.

The reliance on cloud computing services and related technologies has increased due to their many benefits, including resource sharing and the need for organizations to maintain their competitive advantage in the digital market. Also, the pace of cloud growth has significantly increased because of COVID-19. Many companies and businesses have shifted their core processes to the cloud for seamless operations during the pandemic. This migration of legacy systems to the cloud creates many opportunities for businesses. However, on the other hand, it may also bring substantial challenges. Hence, a careful decision is needed to align with company goals. In this paper, we demonstrate the need and advantages of cloud computing, followed by highlighting AWS's characteristics as one of the leading cloud service providers. We also discuss the merger of AWS with SDN to improve cloud data security and performance. The outcomes of this study can provide a data migration strategy and guide decision-makers toward a secure and sustainable migration to a cloud computing environment.

OpenToolFlux is an open-source software to estimate soil gas fluxes from gas concentration time-series data generated by automatic chamber systems. This paper describes the physical equipment used as well as software design and workflow. The software is a command-line application that imports tabular time-series data from the analyzer following the instructions specified in a configuration file by the user, performs configurable data-cleaning operations, and outputs a data file with volumetric flux estimates as well as diagnostic plots. The software can be configured according to the specifics of physical equipment and experimental setup and is therefore applicable in a wide range of studies.

Introduction: Due to the great development of digital technology, through CAD (Computer - Aided Design) and CAM (Computer - Aided Manufacturing) systems, digital models could be used in orthodontic treatment planning decision-making, as there are numerous studies in the literature that support the validity of measurements of digital models of anterior teeth and the total coefficient of Bolton analysis. The aim of the study was to compare the average length value of the current upper and lower arches with that of a hypothetical nickel-titanium wire and the accuracy of the Bolton index measurement. In the present study, 138 dental casts were analyzed using Ortho3Shape software. Using the Ortho3Shape software, it was possible to measure the real and ideal lower arch lengths and, with regard to Bolton analysis, the values of the anterior and total Bolton coefficients. By comparing the values obtained with those of the study by A. Anand Kumar et al. using CBCT and plaster casts, the reliability of the measurements obtained with such CAD/CAM orthodontic software was evaluated. Considering the validity of digital measurements demonstrated by the studies and systematic reviews in the literature, it can be stated that CAD/CAM digital models can be a viable alternative to plaster models in the coming years, as they can facilitate model preservation and retrieval. For future studies and research, it would be preferable to use intra-oral scanners (IOS) in order to ensure greater accuracy, requiring only one step and ensuring a better outcome for the patient.

Unique analytical challenges arise when drivers, who face a route choice between a toll lane and a set of free lanes, have different values of time. The most complex situation is one in which multiple sub-populations of drivers exist, each with their own unique mean and coefficient of variation of value of time. This situation, when imbedded within a larger network cannot be tackled using existing planning models, and consequently is usually only approximated. This paper examines these different approximations, the resulting numerical solutions and the implications of these approximations on the estimate of the number of expected toll lane users. The paper also shows how this problem can be solved using a combined traffic assignment/simulation model. The first part of this paper develops an analytical formulation for solving the toll lane scenario using the “value of time” representations range from the simplest to the most complex. It is shown that one of the most critical issues is a determination of who the marginal users are of the toll lane, at each level of usage, as the perceived disutility of the last marginal toll lane user depends dynamically upon that driver’s value of time. Analytical formulations based on these different approximations are then solved numerically in the second part of the paper. These numerical solutions show that significant different lane use estimates result, depending upon the representation of value of time. Consequently, it is clear that solving this problem with the fewest approximations is both of theoretical and practical importance. The third part of the paper illustrates the solution to the toll lane problem, with each level of approximation, using a combined traffic assignment/simulation model. The simulated resulting estimates of the toll lane usage for each case matches both the relative and absolute trends found in analytical solutions. However, the solution using the assignment/simulation model is not only much faster and simpler to obtain, but is also scalable both in size and complexity. The additional complexities, that are associated with a less approximate representation of value of time, should therefore be incorporated in all future assessments of toll lane facilities, be they analyzed analytically or through simulation.

In Model-Driven Development (MDD), the models, their generation, and imposing changes on them (model transformation) are used for the development of software. Models provide a framework to start from the imagination and abstraction to create and accomplish the final system. Models create a slow and steady transition from whatness to howness, i.e. from the natural path of the generation of software. For supporting this path, the Logic and Functionality of software must be changeable during its evolution. Here we provide a brief introduction to the concept of Model Driven Development.

The spread of the global COVID-19 pandemic, the lack of specific treatment and the urgent situation requires use of all resources to remedy this scourge. In the present study, using molecular docking, we identify new probable inhibitors of COVID-19 by molecules from Nigella sativa L, which is highly reputed healing herb in North African societies and both Islamic and Christian traditions. The discovery of the M^pro protease structure in COVID-19 provides a great opportunity to identify potential drug candidates for treatment. Focusing on the main proteases in CoVs (3CL^pro/M^pro) (PDB ID 6LU7 and 2GTB); docking of compounds from Nigella Sativa and drugs under clinical test was performed using Molecular Operating Environment software (MOE). Nigelledine docked into 6LU7 active site gives energy complex about -6.29734373 Kcal/mol which is close to the energy score given by chloroquine (-6.2930522 Kcal/mol) and better than energy score given by hydroxychloroquine (-5.57386112 Kcal/mol) and favipiravir (-4.23310471 kcal/mol). Docking into 2GTB active site showed that α- Hederin gives energy score about-6.50204802 kcal/mol whcih is better energy score given by chloroquine (-6.20844936 kcal/mol), hydroxychloroquine (-5.51465893 kcal/mol)) and favipiravir (-4.12183571kcal/mol). Nigellidine and α- Hederin appeared to have the best potential to act as COVID-19 treatment. Further, researches are necessary to testify medicinal use of identified and to encourage preventive use of Nigella Sativa against coronavirus infection.

The main moto of this study is to examine and study on behavior of Software Quality Assurance (SQA) issues of project stakeholders in a Scrum environment and their consequences. This inductive case study identifies SQA principles relevant to Meeting User Expectations. The Stakeholders in the Scrum project having lack of Concrete Guidance on Scrum’s SQA approaches, methods, and techniques. The insufficiency of concrete guidelines in Scrum needs a management squad to develop concepts that can include implementing practices from other methodologies and wisely modifying the system structure to incorporate the practices adopted, ensuring improvement in the processes, and creating a shared ownership environment. Through explaining the incompleteness of Agile approaches with special attention to the lack of concrete instructions in Scrum, the study uses techniques to customize literature and advocate for Scrum’s versatility. The study uses strategies to adapt literature and argue for Scrum’s simplicity by illustrating the incompleteness of Agile approaches with special attention to the lack of concrete instructions in Scrum methodology.

Accurate prediction of defects in software components plays a vital role in administrating the quality of the quality and efficiency of the system to be developed. So we have written a systematic literature review in order to evaluate the four main defect prediction techniques. Defect prediction paves way for the testers to find bugs and modify them in order to achieve input to output conformance. In this paper we have discussed the open issues in predicting software defects and have provided with a detailed analyzation of different methods including Machine Learning, Integrated Approach, Cross-Project and Deep Forest algorithm in order to prevent these flaws. However, it is almost impossible to rule which method is better than the other so every technique can be analyzed separately and the best technique according to the problem at hand can be used or can be altered to create hybrid technique suitable for the cause.

Cloud infrastructure provides computing services where computing resources can be adjusted on-demand. However, the adoption of cloud infrastructures brings concerns like reliance on the service provider network, reliability, compliance for service level agreements (SLAs), etc. Software-defined networking (SDN) is a networking concept that suggests the segregation of a network’s data plane from the control plane. This concept improves networking behavior. In this paper, we present an SDN-enabled resource-aware topology framework. The proposed framework employs SLA compliance, Path Computation Element (PCE) and shares fair loading to achieve better topology features. We also present an evaluation, showcasing the potential of our framework.

Titanium’s accelerating usage in global markets is attributable to its distinctive combination of physical and metallurgical properties. The key to best utilizing titanium is to exploit these characteristics, especially as they complement one another in a given application, rather than to just directly substitute titanium for another metal. Titanium alloy are extensively used in aerospace applications such as components in aero-engines and space shuttles, mainly due to their superior strength to weight ratio. For these demanding applications functionality and reliability of components are of great importance. To increase flight safety, higher sensitivity inspections are sought for rotating parts. Increased sensitivity can be applied at the billet stage, the forging stage, or both. Inspection of the forging geometry affords the opportunity to apply the highest sensitivity due to the shorter material paths when compared to those required for billet inspections. Forging inspection is typically performed for titanium (Ti) rotating parts with immersion inspection and fixed-focus, single-element transducers. Increased gain is required with depth because the ultrasonic beam attenuates with distance and diverges beyond the focus position that is placed near the surface. The higher gain that is applied with depth has the effect of increasing the UT noise with depth. The relationships between the UT noise, selection of the examination technique and the smallest detectable defect are presented in this material.

Requirements for cloud services include security and privacy. Although many security patterns, privacy patterns, and non-pattern-based knowledge have been reported, knowing which pattern or combination of patterns to use in a specific scenario is challenging due to the sheer volume of options and the layered cloud stack. To deal with security and privacy in cloud services, this study proposes the Cloud Security and Privacy Metamodel (CSPM). CSPM uses a consistent approach to classify and support existing security and privacy patterns. In addition, CSPM is used to develop a security and privacy awareness process to develop cloud systems. The effectiveness and practicality of CSPM is demonstrated via several case studies.

The authors introduce the GRASS GIS add-on module g.citation as an initial implementation of a fine-grained software citation concept. The module extends the existing citation capabilities of GRASS GIS, which until now only provide for automated citation of the software project as a whole, authored by the GRASS Development Team, without reference to individual persons. The functionalities of the new module enable individual code citation for each of the over 500 implemented functionalities, including add-on modules. Three different classes of citation output are provided in a variety human- and machine-readable formats. The implications of this reference implementation of scientific software citation for both for the GRASS GIS project and the OSGeo foundation are outlined.

Regarding number of vehicles, forest roads are characterized by low traffic intensity, but on the other hand great values of ground pressure between wheels of timber truck units and forest road surface occur, often with pressures values above 80 kN which additionally causes damage of the upper and lower forest road layer. There are currently several methods for assessing condition of a forest road surface which are mainly used for assessing state of public roads, but can be used in forestry as well. Assessing condition of forest road surface was done by measuring vibrations with a specially developed software for Android OS installed on a Huawei MediaPad 7 Lite. Software measured vibrations in all three axes, coordinates of device, speed of the vehicle and time. Aim of this research was to determine accuracy of collected data so that this method can be used for scientific and practical purposes. Research was carried out on the segment of a forest road during driving a vehicle equipped with a measuring device. Tests were performed in both driving direction of the forest road segment with different measuring frequencies, tyre inflation pressures and driving speeds. Values of vibrations were classified and translated on a map of forest road together with devices’ measured coordinates. Vibration values were compared with places of recorded forest road surface damages. Research results show no significant difference in vibration values between 1 Hz and 10 Hz of measurement frequencies. Based on the analysis of collected data and obtained results, it is clear that it is possible to assess the condition of a forest road surface by measuring vibrations. The greatest values of vibrations were recorded on the most damaged parts of the forest road. Vibrations do not depend on tyre inflation pressure, but ranges of vibrations are decreasing with decreasing driving speed. Accuracy of collected data depends on GPS signal quality, so it is recommended that each segment of forest road is recorded twice so that location of damages on forest road can be confirmed with certainty.

The use of Physical Unclonable Functions (PUFs) linked to the manufacturing process of the electronic devices supporting applications that exchange critical data over the Internet has made these elements essential to guarantee the authenticity of said devices, as well as the confidentiality and integrity of the information they process or transmit. This paper describes the development of a configurable PUF/TRNG module based on Ring Oscillators (ROs) that takes full advantage of the structure of modern programmable devices offered by Xilinx 7 Series families. In this work, the increase in hardware efficiency of the proposed architecture is used with a double objective. On the one hand, perform an exhaustive statistical characterization of the results derived from the exploitation of RO configurability. On the other hand, undertake the development of a new version of the module that requires a smaller amount of resources while considerably increasing the number of output bits compared to other proposals previously reported in the literature. The design as a highly parameterized Intellectual Property (IP) module connectable through a standard interface to a soft- or hard-core general-purpose processor greatly facilitates its integration into embedded solutions, while accelerating the validation and characterization of this element on the same electronic device that implements it. The studies carried out reveal adequate values of reliability, uniqueness and unpredictability when the module acts as a PUF, as well as acceptable levels of randomness and entropy when it acts as a True Random Number Generator (TRNG). They also illustrate the ability to obfuscate and recover identifiers or cryptographic keys of up to 4 096 bits using an implementation of the PUF/TRNG module that requires only a 4×4 CLB array to accommodate the RO bank.

Low-frequency electromagnetic induction (EMI) is a non-invasive geophysical method that is based on the induction of electromagnetic (EM) waves into the subsurface to quantify changes in electrical conductivity. In this study we present an open (design details and software are accessible) and modular system for the collection of EMI data. The instrument proposed allows to adjust the separations between the transmitter and up to four receiving antennas as well as the acquisition frequency (in the range between 3 and 50 kHz) to permit measurements with variable depth of investigation. The sensor provides access to raw data and the software described in this study allows control of the signal processing chain. The design specifications permit apparent conductivity measurements in the range of between 1 mS/m and 1000 mS/m, with a resolution of 1.0 mS/m and with a sampling rate of up to 10 samples per second. The sensor allows for a synchronous acquisition of a time stamp and a location stamp for each data sample. The sensor has a mass of less than 5 kg, is portable and suitable for 1-person operation, provides 4 hours of operation time on one battery charge, and provides sufficient rigidity for practical field operations.

The paper discusses the design and modeling of a simple and illustrative hardware kit for teaching the basics of quantum cryptography to engineering students. The novel solution differs from those already on the market in that it is focused on familiarising trainees with the physical principles of quantum key distribution, as well as with the basics of mathematical formalism in quantum mechanics. This is achieved by using a minimally sufficient set of optical elements with a simple mathematical description in the Jones formalism. This composition of the kit is targeted mostly at engineering students and does not require advanced training in physics as a prerequisite. The configurable architecture of the hardware educational kit contributes to the deeper involvement of students. By independently changing the modular configuration of the system, students can conduct experiments that were not directly provided by the developers. For instance, students can assess the impact of choosing the basic settings of phase retarders on the course of a man-in-the-middle attack. The proposed amount of mathematical and informational support, as well as ready-made formal models, is sufficient to reasonably put forward hypotheses for experimental verification and interpret the obtained empirical data. At this moment, the hardware kit is being replicated, distributed and successfully applied at universities and companies in the communications industry. The accumulated experience of educational use testifies to the high efficiency of the kit as a tool for basic QKD training for people without prior knowledge of quantum mechanics. A discrete event model simulating the operation of the hardware kit is implemented in the non-commercial modeling software CPN Tools and is openly distributed.

A morphological feature estimation method is presented, that can be used to monitor fish from low contrast underwater images and videos. The fish orientation is classified in order to apply a shape alignment technique that is based on the Ensemble of Regression Trees machine learning method. Shape alignment allows the estimation of fish dimensions (length, height, mass) and the location of fish body parts of particular interest such as the eyes and gills. The developed system can be exploited both for fish monitoring in open sea as well as aquacultures where non-invasive, low cost morphological feature extraction is essential. Fish health can be assessed from malformations in body shape, the color of the eyes or the gills as well as the fish behavior. The proposed method can estimate the position of 18 landmarks with an accuracy of about 95% from low resolution and low contrast underwater images where the fish can be hardly distinguished from its background. Hardware acceleration can be applied at various stages of the proposed method for real time video processing. As a case study, the developed system has been trained and tested with several Mediterranean fish species in the category of seabream or similar: diplodous sargus annularis (white seabream), diplodus annularis seabream (spawn), oblada melanura (saddled seabream), pagrus pagrus (common seabream), etc. A large dataset with low resolution underwater videos and images has been created in the context of this work.

This report summarizes the findings of a deep retrofitting modelling study of four government buildings across the UAE. Buildings envelope assessment, HVAC, and electrical systems design, installed conditions, maintenance practices and operation methods, and rigorous engineering analysis has been performed for the four buildings to produce calibrated energy models that confirm a good correlation between actual annual energy consumption and advanced simulated energy modeling results. IES-VE software, ASHRAE Guideline 14, and IPMVP were the basis of the energy model calibration based on actual data. Energy retrofitting options were applied to the calibrated energy models of the four government buildings where advanced computational modeling studies have been utilized to analyze different thermal envelope components and external fabric energy efficiency measures in conjunction with the actual HVAC systems. The results have shown that it may be financially challenging to implement a single envelope retrofitting measure on any of the four buildings due to high payback periods. Nevertheless, when conducting an optimization through a holistic building approach targeting the overall envelope enhancements, the financial modeling results provide significantly improved and provide reduced payback periods of the solutions, making them more attractive to investors. Based on the energy modeling results and the cost of the various proposed building envelope solutions provided by Saint-Gobain, the payback period in all cases varies between 15 to 20 years. However, huge savings in terms of carbon emissions are achieved by implementing the deep retrofitting measures, crucial to achieving carbon neutrality within the UAE built environment. Deep retrofitting is a promising segment of the execution roadmap as part of the UAE's decarbonization journey; even though it is capital-intensive upfront, it has a major impact on long-term operational savings. Moreover, it is often more cost-effective than building new and energy-efficient structures from scratch. The findings indicate that deep retrofitting can be a techno-financially viable solution if it is well-planned during major refurbishment or as part of building maintenance. It has significant potential to reduce carbon emissions while also providing a range of benefits such as operational cost savings, improved thermal comfort – a feature which has a direct impact on perceived efficiency and productivity at work, indoor air quality, improved asset value, reputational enhancement, and better choice for environmentally-cautious tenants or buyers.

Purpose – This paper aims to investigate the role of forensic accounting skills in enhancing auditor self-efficacy towards fraud detection in Indonesia. In addition, explores the moderating effect of the implementation of Generalized Audit Software (GAS) and whistleblowing system on the relationship between accounting and auditing skills and auditor self-efficacy, as well as its role in enhancing fraud detection. Methodology– A cross-sectional survey was developed and distributed to 537 external auditors working in Indonesia. This study uses a multiple linear regression analysis method with multiple linear moderation models using WarpPLS 8.0 software. Findings – The study results indicate a significant direct relationship between practical communication skills, psychosocial skills, accounting and auditing skills, and auditor self-efficacy. However, technical and analytical factors do not affect auditor self-efficacy. The study's results indicate a significant direct relationship between auditor self-efficacy and fraud detection. It is revealed that implementing GAS moderates the relationship between auditor self-efficacy and fraud detection. In contrast, the study's results do not show a significant relationship between technical and analytical skills and auditor self-efficacy. Novelty– This research paper's originality lies in exploring the role of forensic accounting skills by using GAS and Whistleblowing systems to enhance auditor self-efficacy toward fraud detection in Indonesia.

Global initiatives such as the Paris Agreement and Agenda 2030 have united nations around common sustainability goals, prompting critical discussions about the true nature of sustainability. A significant concern highlighted by the United Nations is greenwashing—a practice where companies or organisations mislead the public about the environmental benefits of their products or services. Tactics employed in greenwashing include using unclear or misleading labels like “green” and “eco-friendly” or emphasising a single aspect of the product’s lifecycle that appears sustainable while ignoring others. Environmental Product Declarations (EPDs) have gained prominence in response to these challenges, particularly in New Zealand, where government climate declaration regulations have fuelled their demand. This study aims to comprehensively analyse existing EPDs for construction products by identifying and evaluating EPD programs, categorising product types, assessing the completeness of Life Cycle Assessment (LCA) information modules, and verifying the accuracy of declared impact values. The research methodology involves a systematic literature review, drawing data from reputable databases, including Scopus, Elsevier, Taylor & Francis, MDPI, and Emerald Insight, with data analysis conducted using ATLAS.ti 9 software. Examining the current state of EPDs in New Zealand’s construction sector, this study aims to highlight best practices, identify areas for improvement, and provide a foundation for enhancing the transparency and reliability of environmental impact assessments. The findings will contribute to ongoing efforts to combat greenwashing and promote genuine sustainability in construction projects, ultimately supporting the development of more environmentally responsible building practices.

The purpose of this paper is to construct an evaluation system for AI painting software based on generative adversarial network technology, which optimizes the performance of the related software in terms of functionality, ease of use, system performance and safety. The results of the questionnaires are statistically analyzed. In addition, exploratory factor analysis was supported to extract the data of the study, which was ultimately used to calculate the weight and importance of each index through fuzzy hierarchical analysis method. The study constructed an evaluation system for AI painting software based on generative adversarial network technology, including 16 indicators of functionality, 16 indicators of ease of use, 7 indicators of system performance, and 8 indicators of safety respectively that their alpha coefficients were 0.882, 0.962, 0.932, 0.932, and 0.932, respectively. In addition, the accumulated explanatory variances of their coefficients were 84.405%, 84.897%, 84.013%, and 72.606%, respectively, 73.013%, and 72.606%, respectively. It is clear that the items included in each of the indicators are homogeneous, with a high degree of internal consistency. This paper suggests that the development of AI painting software focusing on functionality, ease of use, system performance and safety can enhance the market competitiveness of the software.

Real time condition monitoring and precision health assessment system is a mandatory need for effective maintenance program in industrial sector. Rapid advancement in the information technology and other engineering technologies have invited more proactive attention from research and development in industrial sectors and particularly in condition monitoring of machines and related Industrial processes. In this work, drill bit condition monitoring techniques have been developed based on wavelet analysis and artificial neural networks (ANN) for automatic drill bit fault detection and classification. An experimental work has been conducted to capture the vibration signals for analysis. In this experiment, the CNC drill machine is used with high carbon steel drill bit and mild steel material as work piece. The cutting condition parameters are kept constant and the wear level is varied from 0.2 to 0.6 mm. The Data Acquisition system (DAQ) with Lab VIEW software is used to capture the vibration signals for drill bit with different wear conditions using accelerometer. The captured vibrations data are analyzed using continuous wavelet transform (CWT) with Morlet wavelet and Daubechies wavelet as a prime function. In general, the CWT coefficient is used to generate the inputs features to ANN for automatic tool condition classification, with two outputs (0, 1) for healthy and (1, 0) for faulty. The outcome of ANN showed 98% of accuracy in the wear prediction and this results show the effectiveness of the combed WT and ANN for automatic classification of tool wear conditions with high success rate.

The traditional receiver employs scalar tracking loops, resulting in degraded navigation performance in weak signal and high dynamic scenarios. An innovative design of a vector tracking receiver based on nonlinear Kalman filter (KF) tracking loops is proposed in this paper, which combines the strengths of both vector tracking and KF tracking loops. Firstly, a comprehensive description of the vector tracking receiver model is presented, and unscented Kalman filter (UKF) is applied to nonlinear tracking loop. Secondly, to enhance the stability and robustness of the KF tracking loop, we introduce square root filtering and an adaptive mechanism. The tracking loop based on square root UKF (SRUKF) can dynamically adjust its filtering parameters based on signal noise and feedback Doppler error. Finally, the proposed method is implemented on a software-defined receiver (SDR), and the field vehicle experiment demonstrates the superiority of this method over other receivers in complex dynamic environments.

Collaboration among individuals with diverse skills and personalities is crucial in producing high-quality software. The success of any software project depends on the team’s cohesive functionality and mutual complementation. This study introduces a data-centric methodology for forming Software Engineering (SE) teams centred around personality traits. Our study analyzed data from an SE course where 157 students in 31 teams worked through four project phases and were evaluated based on deliverables and instructor feedback. Using the Five Factor Model (FFM) and a variety of statistical tests, we determined that teams with higher levels of extraversion and conscientiousness and lower neuroticism consistently performed better. We examined team member interactions and developed a predictive model using extreme gradient boosting. The model achieved a 74% accuracy rate in predicting inter-member satisfaction rankings. Through graphical explainability, it underscored incompatibilities among members, notably those with differing levels of extraversion. Based on our findings, we introduce a team formation algorithm using Simulated Annealing (SA), built upon the insights from our predictive model and additional heuristics.

Ensemble fuzzing in parallel with heterogeneous fuzzers has been proposed to leverage the advantages of diverse fuzzers and improve testing efficiency. However, in current ensemble fuzzing methods, the collaboration among different fuzzers is achieved solely by synchronizing the seeds discovered by each fuzzer. This results in a high likelihood of different fuzzers choosing the same seeds and creating a large number of equivalent testcases, thus reducing overall fuzzing efficiency. Meanwhile, the existing task division method proposed by AFLTeam is highly coupled with the fuzzer specially designed for it, making it challenging to apply to ensemble fuzzing directly. So, in this paper, we proposed a callgraph-based task division method suitable for ensemble fuzzing. Firstly, we divided the target program’s callgraph into subgraphs (subtasks) balancing expected workloads. Then, we divided the global seed corpus into sub-corpora, each corresponding to a subtask, making fuzzers easily accept the subtasks. Finally, we designed synchronization mechanisms for coverage bitmaps and seeds to realize the collaborative fuzzing among different fuzzers, and a cyclic subtask scheduling strategy to fully leverage the benefits of ensemble fuzzing. We have implemented a prototype called TAEF. The evaluation results show that in the best-case scenario, our method has up to 24% more branch coverage than previous work.

Communication infrastructures may be partially or completely destroyed due to the natural disasters, especially the disruption of a few critical network devices, leading to the collapse of communication services.To address this issue, we constructed a layered, multi-domain data transmission architecture based on a UAV-assisted resilient network. Initially, the UAV controllers perceived the network status and learning the temporal- spatial characteristics of air-to-ground networks links. Subsequently, we developed a multi-domain routing algorithm based on federated reinforcement distillation, called FedRDR, enhancing the generalization capabilities of routing decision model through the augmentation of training data samples.We conducted simulation experiments, and the results indicate that the FedRDR algorithm has an average communication data volume of approximately 45.3KB between each domain controller and server. Compared to parameter transmission through the federated reinforcement learning algorithm, FedRDR can reduce the amount of transmitted parameters by approximately 29\%. Therefore, the FedRDR routing algorithm promotes knowledge transfer, accelerates the training process of intra-domain intelligent agents, and decreases the communication data volume between domain controllers and servers.

Self-adaptive systems are capable of reconfiguring themselves while in use to reduce the risks forced by environments for which they may not have been specifically designed. Run-time validation techniques are required because complex Self-adaptive systems must consistently offer acceptable behavior for important services. The run-time testing can offer further confidence that a Self-adaptive system will continue to act as intended even when operating in unknowable circumstances. This article introduces an evolutionary framework that supports adaptive testing for Self-adaptive systems. To ensure that the adaptive systems continue to operate following its requirements and that both test plans and test cases continuously stay relevant to shifting operational conditions. The proposed approach using the SPEA2 algorithm facilitates both the execution and adaptation of run-time testing operations.

With the daily increasing demands for higher-quality services, mobile Internet of Things (MIoT) are facing various challenges, such as communication security, availability, scalability, etc. Its changing topology features bring a lot of trouble when solving the above problems. Though the evolved cellular network is expected to bring promising solutions, some inherent problems in traditional MIoTs may keep hindering its development. Thus, to overcome above problems, we propose a software-defined MIoTs-based model providing communication security and privacy protection under emergencies. In our scheme, the control plane is responsible to compute routes for online learning devices (OLDs), and forward entries for switches. Taking use of information that OLDs and facilities collect, controller is able to coordinate the overall situation. To ensure the authenticity and reliability of messages sent by OLDs, signature and authentication should be provided. We also introduce an emergency-dealing system. It transplants the multicast technology into software-defined MIoTs to generate a Steiner Tree among impacted nodes, so that OLDs will be informed as soon as emergency happening. The security analysis proves that our scheme is able to ensure the communication security in software-defined MIoTs. The evaluation of performance indicates that our scheme outperforms other existing schemes.

With the rise of Internet of Things (IoT) devices and Cyber-Physical Systems, the demand for more functional and high-quality software has increased tremendously. To meet this need, we must reengineer and reuse existing software, as well as develop new software formal verification methods. One such method is based on physical quantities defined by the System International, which have physical dimensions. The homogeneity of physical dimensions in software code enables us to check the software code in the space of base units, making it the first basis of the new software verification method. However, this method cannot check expressions with angles, angle speed, and other similar features. To address this, a transformation for physical value orientation introduced by Siano allows us to check software code for orientational, stabilization, and other related branches. This makes the orientational homogeneity the second basis of the new software verification method. To assess the effectiveness of the proposed method, we developed special software defect models based on the statistical characteristics of software code. We used a special statistical analysis tool to define the statistical characteristics of modern software and analyzed over 2 GB of C++ GITHUB code for drones. Based on the actual distribution of software characteristics, the proposed method can detect over 60% of latent software defects. Implementing this method can significantly reduce testing time, improve reliability, and enhance overall software quality.

Computer extended Descriptive Geometry (CeDG) is a new approach to solve and build computer models of three dimensional (3D) geometrical systems through descriptive geometry procedures that have demonstrated reliability and accuracy. CeDG may calculate a parametric algebraic exact form for the spatial curves generated in the intersection of two surfaces, as well as of the flattened pattern of any developable surface involved in those encounters. This study presents first the theoretical foundations and methodology to calculate those parametric algebraic curves. Secondly, a compound hopper is defined and modelled through CeDG (implemented in GeoGebra) and CAD (Solid Edge 2023) approaches to evaluate the advantages of CeDG against CAD. The results demonstrate the robustness and accuracy of the CeDG technique for surfaces intersection and flattening and the advantages of CeDG against Solid Edge 2023 in solving the hopper case study.

this article presents the results of implementing existing software tools in the accounting field. The teaching processes regarding the use of Learning and Knowledge Technologies (TAC) in the university environment, and very specifically, in what has to do with the pedagogical integration of the same in the curriculum is deficient and disjointed with the current technological development. Thus, in the different curricula of professional programs of Public Accounting and related, the courses related to computer automation processes are oriented only towards the management of computer science and accounting software and the great diversity of existing applications that would undoubtedly directly benefit the student to know the potential of these in their future accounting practice is not taken into account. Strengthen the various skills of a Public Accountant in training through Learning and Knowledge Technologies (TAC), under the hypothesis of developing skills in the accounting field through TACs. The research was developed using a quantitative approach, an empirical-analytical method, with a type of descriptive research under an experimental design based on G1 x O1 and G2–O2, three phases were defined. In the first phase, the characterization of the existing software tools in the accounting field was carried out. In the second phase, their relevance was determined. In the third phase, performance was evaluated and then classified in a repository. The data obtained were analyzed with parametric analysis techniques through which it was found that the difference in quantitative results in the academic process between the experimental and control groups was statistically significant, which concluded the success of the experimental treatment. The use of CT scans in the classroom by the experimental group resulted in an improvement in their academic performance compared to the control group, strengthening their self-learning processes and facilitating the development of cognitive functions and skills necessary in the exercise of their accounting profession. This research supports and strengthens the work skills developed in the classroom for public accounting students. The tools TAC is developed from the request made by the radiometry laboratory as a first phase and a second phase is necessary for validation with experts.

Agaricus bisporus is belonging to family agaricaceae, which is widely acceptable and mostly cultivated among the all mushrooms. It has great nutritional values and it is rich in proteins, vitamins, carbohydrates, fibers, minerals and amino acids. It is effective in antimicrobial, anticancer, antidiabetic, antihypercholesterolemic, antihypertensive, hepatoprotective and antioxidant activities. As it is effective in anticancer property, we check the effects of chemical constituents of Agaricus Bisporus on DNA damaging protein which results its activity PARP inhibiting or vise-versa. We choose the molecular docking technique to check the effects of different chemical constituents of Agaricus Bisporus on DNA damaging protein. For that different PARP inhibitory drugs taken as the standard. We perform the molecular docking of the chemical constituents of Agaricus Bisporus, using 4UND protein with the help of PyRx software and BIOVIA Discovery studio software. Along with that PARP inhibitor drugs also run against the same protein. The results of molecular docking shows the some of the constituents of Agaricus Bisporus has better binding affinity than the standard taken PARP inhibitor drugs. The ergosterol shows the better binding affinity than the niraparib and rucaparib on the same proteins. On other hands the naringenin, quercetin, anthocyanin, folate and myricetin shows the better results than the rucaparib. That means the ergosterol shows the better results as PARP inhibitor than the niraparib and rucaparib.

Computer systems are involved in many critical human applications today, so that a small error can lead to serious and dangerous problems. These errors can be from an error in the incorrect design of the user interface to an error in the program code. The success of a software product depends on several factors. Given that different organizations and institutions use software products, the need to have a quality and desirable Software according to the goals and needs of the organization makes measuring the quality of software products. an important issue for most organizations and institutions, To be sure of having the right software. It is necessary to use a standard quality model to examine the features and sub-features for a detailed and principled study in the quality discussion. In this study, the quality of Word software was measured by Adaptive Neural Fuzzy Inference System. In recent years, powerful systems called fuzzy inference systems on The basis of adaptive neural network (ANFIS) has been used in various sciences. Using the power of neural network training and the linguistic advantage of fuzzy systems, these types of systems have been able to realize the advantages of the two in terms of analyzing very powerful complex processes. Considering the importance of software quality and to have a good and usable software in terms of quality and measuring the quality of software during the study. It was applied at different levels to make the result of measuring the quality of Word software more accurate and closer to reality. In this research, the quality of the software product is measured based on the adaptive neural-fuzzy inference system in ISO standard. According to the results obtained in this study, it is understood that quality is a continuous and hierarchical concept and the quality of each part of the software at any stage of production can lead to high quality products.

In this article, a technology for producing wire and rod solder from 52In-48Sn alloy has been developed and investigated in the conditions of small-scale production. The use of direct extrusion of wire and rods instead of the traditional technology for producing solder, which includes pressing, rolling and drawing, can significantly reduce the fleet of required equipment. Using only a melting furnace and a hydraulic press, solder wires and rods can be produced in various sizes. Shortening the production cycle allows you to quickly fulfill small orders and be competitive in sales. The article developed a mathematical model of direct extrusion, which allows you to calculate: extrusion ratio, extrusion speed and pressing force. The results of modeling the process of extrusion of wire ∅2.00 mm and rods ∅8.0 mm made of 52In-48Sn alloy are presented. The temperature of the solder and the tool is simulation in software QForm based on the finite element method. Experimental results of manufacturing ∅2.0 mm solder wire and ∅8.0 mm rods are presented. The microstructure of the direct extruded solder is a eutectic of phases γ and β. Energy-dispersive X-ray spectroscopy (EDS) mapping of the 52In-48Sn alloy showed that the solder obtained by direct extrusion has a uniform distribution of structural phases. The developed technology can be used in the manufacture of wires and rods from other low-melting alloys.

The transportation system has evolved into a complex cyber-physical system with the introduction of wireless communication and the emergence of connected travelers and connected automated vehicles. Such applications create an urgent need to develop high-fidelity transportation modeling tools that capture the mutual interaction of the communication and transportation systems. This paper addresses this need by developing a high-fidelity, large-scale dynamic and integrated traffic and direct cellullar vehicle-to-vehicle and vehicle-to-infrastructure (collectively known as V2X) modeling tool. The unique contributions of this work are (1) we developed a scalable analytical communication model that captures packet movement at the millisecond level; (2) we coupled the communication and traffic simulation models in real-time to develop a fully integrated dynamic connected vehicle modeling tool; and (3) we developed scalable approaches that adjust the frequency of model coupling depending on the number of concurrent vehicles in the network. The proposed scalable modeling framework is demonstrated by running on the Los Angeles downtown network considering the morning peak hour traffic demand (145,000 vehicles), running faster than real-time on a regular personal computer (1.5 hours to run 1.86 hours of simulation time). Spatiotemporal estimates of packet delivery ratios for downtown Los Angeles are presented. This novel modeling framework provides a breakthrough in the development of urgently needed tools for large-scale testing of Direct C-V2X enabled applications.

TThe XPRIZE Foundation designs and operates multi-million-dollar, global competitions to incentivize the development of technological breakthroughs that accelerate humanity toward a better future. To combat the COVID-19 pandemic, the Foundation coordinated with several organizations to make available data sets about different facets of the disease and to provide the computational resources needed to analyze those data sets. This is paper is a case study of the requirements, design, and implementation of the XPRIZE Data Collaborative, a cloud-based infrastructure that enables the XPRIZE to meet its COVID-19 mission and host future data-centric competitions. We examine how a Cloud Native Application can use an unexpected variety of Cloud technologies, ranging from containers, serverless computing, to even older ones like Virtual Machines. We also search and document the effects that the pandemic had on application development in the Cloud. We include our experiences of having users successfully exercise the Data Collaborative, detailing the challenges encountered and areas for improvement and future work.

Studies have found critical software malfunctions responsible for some of the worst accidents in recent times. These malfunctions are often only minor defects that snowball into large problems; a few lines of code is all it takes. Complexity, safety, quality, and resilience are among the key attributes defining a software’s operational success. There are many leading factors for complexity, such as increases in the product size, the rate of requirement changes, and the number and type of stakeholders, and failure to manage these issues efficiently always has the same consequence, i.e., massive failure and sometimes technological catastrophe. This work analyzes some of the architecture, design, and implementation guidelines used as detection and mitigation techniques. It also discusses the safety considerations, as considering how the steam industry has handled safety issues could offer some guidance for ensuring safety. Complexity in such systems also causes some of the worst side effects from the quality auditor's perspective. While failures in the software are hard to predict, one of the most significant ways of showing preparedness is practicing software resilience. New mitigation areas, such as the fragility spectrum and failure obviation, and their usage for building a safer system are analyzed. Also discussed are various architecture styles in practice and the dramatic effect human factors can have on the success of the software being developed.

Software project complexity increases day by day because the software engineering products is being used in the solution of more technically difficult problem and the size of project continuous to grow. The increase complexity causes to high numbers of software project failures in term of time, cost and quality. The main question regarding to this problem is how to handle or cope with this complexity. There is no single way to handle this, software engineer uses different perspective to handle complexity without affecting the overall project performance. A management perspective recognizes that the success of complex project requires good project management. A technically perspective reveals new paradigms for software development i.e.; object oriented and formal methods etc. and software engineer also look for automation perspective in order to reduce the complexity issues. In this paper we will find out the main software project complexity factors by focusing on the management aspects of software project development and also the problems of managing complexity in software engineering products from these different perspectives. The paper is divided in three main sections; paradigms of software development, project management in term of time, cost and quality and third one is automated support that includes methods and tools used to manage the complexity.

Toby Walsh in “The Singularity May Never Be Near” gives six arguments to support his point of view that technological singularity may happen but that it is unlikely. In this paper, we provide analysis of each one of his arguments and arrive at similar conclusions, but with more weight given to the “likely to happen” probability.

Trends such as Industrial Internet of Things (IIoT) and Industry 4.0 have increased the need to use powerfull network technologies in industrial automation. The growing communication in industrial automation is harnessing the productivity and efficiency of manufacturing and process automation with minimum human intervention. Due to the ongoing evolution of industrial networks from Fieldbus technologies to Ethernet, the new opportunity has emerged to integrate the Software Defined Networking (SDN) technique. In this paper, we provide a brief overview of SDN in the domain of industrial automation. We propose a network architecture called Software Defined Industrial Automation Network (SDIAN), with the objective of improving network scalability and efficiency. To match the specific considerations and requirements of having a deterministic system in an industrial network, we propose two solutions for flow creation: Pro-active Flow Installation Scheme (PFIS) and Hybrid Flow-Installation Scheme (HFIS). We analytically quantify the proposed solutions in alleviating the overhead incurred from the flow setup cost. The analytical model is verified through monte carlo simulations. We also evaluate the SDIAN architecture and analyze the network performance of the modified topology using an emulator called Mininet. We further list and motivate SDIAN features and in particular report on an experimental food processing plant demonstration featuring Raspberry PIs (RPIs) instead of traditional Programmable Logic Controllers (PLCs). Our demonstration exemplifies the characteristics of SDIAN.

Clinical rehabilitation and sports performance analysis both require the objectification of movement. Kinovea^© is a free 2D motion analysis software that enables the establishment of kinematics parameters. This low-cost technology has been used in sports sciences, as well as clinical field and research work. Although it has been validated as a tool with which to assess time-related variables, this is not yet the case regarding angular and distance variables. The main objective of this study was to determine the validity and reliability of the Kinovea software in obtaining angular and distance data at different perspectives of 90°, 75°, 60° and 45°. For this purpose, a figure with 29 points was designed (in AutoCAD) and 24 frames analysed. Each frame was examined by three observers who each made two attempts. For each export data item, 20 angles and 20 distance variables were calculated, with intra- and inter-observer reliability also analysed. To evaluate Kinovea reliability and validity a multiple approach was applied involving the following analysis: -systematic error with a two-way ANOVA 2x4; -relative reliability with ICC and CV (95% confidence interval); -absolute reliability with Standard Error. The results thus obtained indicate that the Kinovea software is a valid and reliable tool that is able to measure accurately at distances up to 5 m from the object and at an angle range of 90°–45°. Nevertheless, for optimum results an angle of 90° is suggested.

The main focus when developing software is to improve the reliability and stability of a software system. When software systems are introduced, these systems are used in field environments that are the same as or close to those used in the development-testing environment; however, they may also be used in many different locations that may differ from the environment in which they were developed and tested. In this paper, we propose a new software reliability model that takes into account the uncertainty of operating environments. The explicit mean value function solution for the proposed model is presented. Examples are presented to illustrate the goodness-of-fit of the proposed model and several existing non-homogeneous Poisson process (NHPP) models and confidence intervals of all models based on two sets of failure data collected from software applications. The results show that the proposed model fits the data more closely than other existing NHPP models to a significant extent.