Ultra-large-scale low earth orbit satellite internet, as a core component of space-air-ground integrated networks, is poised to become the cornerstone of next-generation wireless communication system architectures, thanks to its advantages such as high transmission bandwidth, global seamless coverage, and low transmission latency. However, despite their wide application prospects, ultra-large-scale low earth orbit satellite internet still faces various technical bottlenecks in practical deployment, and its network service performance is severely challenged. For example, the high-speed orbital motion of satellites leads to continuously dynamic network topology; complex space environment effects cause fluctuations in node availability; and frequent satellite-to-ground link switches result in significant transmission performance degradation. These characteristics mean that traditional terrestrial network transport layer protocols may encounter performance bottlenecks and struggle to adapt to the service performance requirements of ultra-large-scale low earth orbit satellite Internet. Therefore, it is of great significance to evaluate and analyze the performance of various transport layer protocols in the context of ultra-large-scale low earth orbit satellite internet scenarios. This paper, focusing on ultra-large-scale low earth orbit satellite internet and based on typical constellation configurations and diverse service demands, utilizes the independently developed lightweight satellite internet simulation platform UltraStar to conduct a multi-dimensional performance evaluation and comparative analysis of mainstream transport layer protocols, including UDP, TCP, QUIC, and SCPS-TP. The results indicate that the SCPS-TP protocol demonstrates significant advantages in video service transmission; however, its delay-based congestion control algorithm still requires further refinement. In contrast, TCP and QUIC exhibit comparatively inferior performance in terms of transmission quality and user experience. Although UDP achieves high transmission rates and low latency in this scenario, its inherent lack of reliability results in elevated packet and frame loss rates, thereby constraining its applicability in service scenarios with stringent reliability requirements.
Software engineering research benefited for decades from openly available tools, accessible systems, and problems that could be studied at modest scale. Today, many of the most relevant software systems are large, proprietary, and embedded in industrial contexts that are difficult to access or replicate in academia. We review how the field reached this point, identify structural challenges facing contemporary research, and argue that incremental methodological refinement is insufficient. We discuss practical directions forward, including industrial PhDs, long-term industry-academia collaborations, larger research teams, moonshot projects, and changes to funding and evaluation practices.
Lijia Elena Mendoza Leon, Jorge Nicolás A. Papanicolau Denegri
Esta investigación fue efectuada en una empresa de tecnología, en donde se analiza el Net Promoter Score (NPS) para conocer el nivel de satisfacción y la lealtad de los clientes tras recibir un servicio posventa. El interés por realizar esta investigación surgió a raíz de los cambios que afectaron a los procesos de la empresa como consecuencia de la pandemia y la posterior adaptación a la nueva normalidad. El muestro se realizó durante quince días, periodo en el cual se obtuvieron 12 datos para conocer la situación anterior; posteriormente, se recogió la misma cantidad de datos para evaluar la eficacia de las mejoras introducidas para atender los reclamos, recibidos vía WhatsApp, y atendidos, incluido el transporte, con la posterior satisfacción del cliente.
As the 6G era approaches, wireless communication faces challenges such as massive user numbers, high mobility, and spectrum resource sharing. Radio maps are crucial for network design, optimization, and management, providing essential channel information. In this paper, we propose an innovative learning framework for Radio Map Estimation (RME) based on cycle-consistent generative adversarial networks. Traditional RME methods are often constrained by model complexity and interpolation accuracy, while learning-based methods require strictly paired datasets, making their practical application difficult. Our method overcomes these limitations by enabling training with unpaired data, efficiently converting local features into radio maps. Our experimental results demonstrate the effectiveness of the proposed method in two scenarios: accurate map data and map data with dynamic errors. To address dynamic interference, we designed a two-stage learning process that uses sparse observations to correct local details in the radio map, and the model's accuracy and practicality.
Large Language Models (LLMs) are increasingly integrated into various daily tasks in Software Engineering such as coding and requirement elicitation. Despite their various capabilities and constant use, some interactions can lead to unexpected challenges (e.g. hallucinations or verbose answers) and, in turn, cause emotions that develop into frustration. Frustration can negatively impact engineers' productivity and well-being if they escalate into stress and burnout. In this paper, we assess the impact of LLM interactions on software engineers' emotional responses, specifically strains, and identify common causes of frustration when interacting with LLMs at work. Based on 62 survey responses from software engineers in industry and academia across various companies and universities, we found that a majority of our respondents experience frustrations or other related emotions regardless of the nature of their work. Additionally, our results showed that frustration mainly stemmed from issues with correctness and less critical issues such as adaptability to context or specific format. While such issues may not cause frustration in general, artefacts that do not follow certain preferences, standards, or best practices can make the output unusable without extensive modification, causing frustration over time. In addition to the frustration triggers, our study offers guidelines to improve the software engineers' experience, aiming to minimise long-term consequences on mental health.
Stuart M. Allen, Neil Chue Hong, Stephan Druskat
et al.
Research Software Engineering (RSEng) is a key success factor in producing high-quality research software, which in turn enables and improves research outcomes. However, as a principal investigator or leader of a research group you may not know what RSEng is, where to get started with it, or how to use it to maximize its benefit for your research. RSEng also often comes with technical complexity, and therefore reduced accessibility to some researchers. The ten simple rules presented in this paper aim to improve the accessibility of RSEng, and provide practical and actionable advice to PIs and leaders for integrating RSEng into their research group. By following these rules, readers can improve the quality, reproducibility, and trustworthiness of their research software, ultimately leading to better, more reproducible and more trustworthy research outcomes.
The population growth, along with lifestyle changes, has resulted in unprecedented levels of food waste at all phases of the supply chain, including harvest, packing, transportation, and consumption. Conventional practices involve dumping of food waste with municipal garbage. However, these methods have serious environmental and health consequences. Food waste has a great recycling perspective due to its high biodegradability and water content, making it an ideal substrate for the production of biofuels and other industrially important chemicals including pigments, enzymes, organic acids, and essential oils. This review extensively covers conversion of food waste to generate bioenergy which will help to reduce environmental pollution and facilitate implementation of a circular bioeconomy. Moreover, review also highlights novel technologies like supercritical fluid extraction, ultra-sonication, pressurized liquid extraction, and microwave assisted extractions that are being employed in food waste management to increase the efficiency of value-added product recovery in an economically viable manner. Metabolic engineering of microorganisms for specificity of product would be a future breakthrough in food waste valorization/management.
Oleksandr Diachenko, Maksym Delembovskyi, Kateryna Levchuk
et al.
The production of concrete mixes, along with their use in the production of building materials and structures, is one of the key processes in the construction industry during the construction, restoration and repair of buildings and structures. Because of this, the need to create modern concrete mixing plants that will meet the requirements of minimum energy consumption and maximum productivity of concrete mixture production is an urgent task. Not only the main operations, which include the dosing of the components of the mixture and their mixing, but also the maintenance operations, namely operations that ensure the timely movement of the components of the concrete mixture from warehouses to the main technological equipment, affect the set rhythm of the concrete mixture production. Conveyors of various types and designs are used to move bulk materials, such as crushed stone and sand.
For the rational selection of such equipment in accordance with the characteristics of the cargo to be transported, knowledge of the types of conveyors, their structures and parameters, understanding of operation issues and methods of parameter calculation are required. In addition, it is worth paying attention to the following parameters: maximum cargo transportation productivity, low energy consumption per unit of moved products, low metal content of the structure.
The work reviewed the most common designs of conveyors used to move bulk materials in concrete mixing plants, analyzed the disadvantages and advantages of conveyors, as well as technical parameters. As a result, the predominant directions for the use of belt and plate conveyors at construction enterprises were determined. The advantages of belt conveyors, which contribute to their widespread distribution, are high productivity, simplicity of design, reliability, quiet operation, low specific power consumption.
When choosing a conveyor, it is recommended to choose the equipment with the highest productivity and the lowest power of the drive motors, however, the performance should be clearly related to other technological equipment.
Lucas Romao, Marcos Kalinowski, Clarissa Barbosa
et al.
[Context] Software Engineering (SE) education constantly seeks to bridge the gap between academic knowledge and industry demands, with active learning methods like Problem-Based Learning (PBL) gaining prominence. Despite these efforts, recent graduates struggle to align skills with industry needs. Recognizing the relevance of Industry-Academia Collaboration (IAC), Lean R&D has emerged as a successful agile-based research and development approach, emphasizing business and software development synergy. [Goal] This paper aims to extend Lean R&D with PBL principles, evaluating its application in an educational program designed by ExACTa PUC- Rio for Americanas S.A., a large Brazilian retail company. [Method] The educational program engaged 40 part-time students receiving lectures and mentoring while working on real problems, coordinators and mentors, and company stakeholders in industry projects. Empirical evaluation, through a case study approach, utilized structured questionnaires based on the Technology Acceptance Model (TAM). [Results] Stakeholders were satisfied with Lean R&D PBL for problem-solving. Students reported increased knowledge proficiency and perceived working on real problems as contributing the most to their learning. [Conclusion] This research contributes to academia by sharing Lean R&D PBL as an educational IAC approach. For industry, we discuss the implementation of this proposal in an IAC program that promotes workforce skill development and innovative solutions.
AbstractThe advent of non-pillar mining technology of self-formed roadway based on roof cutting theory (NPMTSFRRCT) has revolutionized the method of tackling the difficulties posed by hard suspended roofs in mining engineering. The design of roof cutting parameters plays a crucial role in determining the roof fracture characteristics. The principles of roof cutting parameter design have been analysed, with a focus on the key and challenging aspect of the collapse of single-layer thick hard rock under the condition of a thin immediate roof. A mechanical model of roof fracture has been established, and the impact of immediate roof thickness, roof cutting height, roof cutting angle, and main roof thickness on roof fracture has been analysed. A numerical model based on the UDEC software has been created, and the roof fracture, stress, and displacement variation characteristics have been studied using the failure criterion of polygonal blocks. The results of the theoretical analysis have been verified, and it was found that the roof fracture in partial roof cutting occurs in the form of hinge bite, while complete roof cutting results in step sinking. Engineering practice has shown that the deformation of the roadway surrounding rock has been effectively controlled.
Abstract: Cyber-Physical Production Systems (CPPS) stand as a synonym for the future factory environment (also Smart Factory) and are characterized by their scalable and modular structure. The CPPS-paradigm facilitates the integration, adaptation and replacement of production units, e.g. in case of scaling up or down the production capacity to satisfy unpredictable market demands or to respond flexibly to disruptions and failures. However, to reach the next generation of CPPS or Smart Factories, the concurrent combination of the physical world with its digital counterpart will be crucial. Given the fact that products are becoming even more complex and that the product life cycle decreases, the usage of simulation tools grow in importance for the optimization and acceleration of all phases of the production life-cycle. Simulation tools are intended to support (re-)engineering and decision-making processes, to evaluate the impact of external and internal changes and to react in a timely manner to critical influences on production management. This paper deals with upcoming challenges to exploit the full potential of modeling and simulation within Smart Factories. To address these challenges, an appropriate framework for modeling and simulation of CPS-based factories is presented. The framework is being applied in one of the most competitive, advanced and complex industrial sector, the automotive industry.
Stefania Bramanti, Matteo Carrabba, Alice Di Rocco
et al.
Introduction: Chimeric antigen receptor (CAR) T-cell therapies are novel immunotherapies for the treatment of hematologic malignancies. They are administered in specialized centers by a multidisciplinary team and require the careful coordination of all steps involved in manufacturing and using cellular therapies. The Maturity Model (MM) is a tool developed and used for assessing the effectiveness of a variety of activities. In healthcare, it may assist clinicians in the gradual improvement of patient management with CAR T-cell therapy and other complex treatments.
Methods: The START CAR-T project was initiated to investigate the potential of a MM in the setting of CAR T-cell therapy. Four Italian clinics participated in the creation of a dedicated MM. Following the development and test of this MM, its validity and generalizability were further tested with a questionnaire submitted to 18 Italian centers.
Results: The START CAR-T MM assessed the maturity level of clinical sites, with a focus on organization, process, and digital support. For each area, the model defined four maturity steps, and indicated the actions required to evolve from a basic to an advanced status. The application of the MM to 18 clinical sites provided a description of the maturity level of Italian centers with regard to the introduction of CAR T-cell therapy.
Conclusion: The START CAR-T MM appears to be a useful and widely applicable tool. It may help centers optimize many aspects of CAR T-cell therapy and improve patient access to this novel treatment option.
Battery management system plays an important role for modern battery-powered application such as Electric vehicles, portable electronic equipment and storage for renewable energy sources. It also increases the life-cycle of the battery, battery state and efficiency. Monitoring the state of charge of the battery is a crucial factor for battery management system. This paper deals with monitoring the state of charge of the battery along with temperature, current for Solar panel fitted with battery for residential application. Microcontroller is used for controlling purpose, analog sensors are used for sensing the parameters of voltage, current. The information of the battery is given with tabular form and shown in photograph. Battery parameters are displayed with the LCD screen.
Massimo Giovannozzi, Ewen Maclean, Carlo Emilio Montanari
et al.
A Machine Learning approach to scientific problems has been in use in Science and Engineering for decades. High-energy physics provided a natural domain of application of Machine Learning, profiting from these powerful tools for the advanced analysis of data from particle colliders. However, Machine Learning has been applied to Accelerator Physics only recently, with several laboratories worldwide deploying intense efforts in this domain. At CERN, Machine Learning techniques have been applied to beam dynamics studies related to the Large Hadron Collider and its luminosity upgrade, in domains including beam measurements and machine performance optimization. In this paper, the recent applications of Machine Learning to the analyses of numerical simulations of nonlinear beam dynamics are presented and discussed in detail. The key concept of dynamic aperture provides a number of topics that have been selected to probe Machine Learning. Indeed, the research presented here aims to devise efficient algorithms to identify outliers and to improve the quality of the fitted models expressing the time evolution of the dynamic aperture.
Background: A growing amount of software is available to children today. Children use both software that has been explicitly developed for them and software for general users. While they obtain clear benefits from software, such as access to creativity tools and learning resources, children are also exposed to several risks and disadvantages, such as privacy violation, inactivity, or safety risks that can even lead to death. The research and development community is addressing and investigating positive and negative impacts of software for children one by one, but no comprehensive model exists that relates software engineering and children as stakeholders in their own right. Aims: The final objective of this line of research is to propose effective ways in which children can be involved in Software Engineering activities as stakeholders. Specifically, in this paper, we investigate the quality aspects that are of interest for children, as quality is a crucial aspect in the development of any kind of software, especially for stakeholders like children. Method: Our contribution is based mainly on an analysis of studies at the intersection between Software Engineering (especially software quality) and Child Computer Interaction. Results: We identify a set of qualities and a preliminary set of guidelines that can be used by researchers and practitioners in understanding the complex interrelations between Software Engineering and children. Based on the qualities and the guidelines, researchers can design empirical investigations to obtain deeper insights into the phenomenon and propose new Software Engineering knowledge specific for this type of stakeholders. Conclusions: This conceptualization is a first step towards a framework to support children as stakeholders in software engineering.
The term behavior engineering (BE) encompasses a broad integration of behavioral and compositional requirements needed to model large-scale systems. BE forms a connection between systems-engineering processes and software-engineering processes. In software engineering, interpreting requirements can be perceived as specifying behavior, which is viewed in terms of chronology of events in the modeled system. In this paper, we adopt BE in its general and integrating sense to search for a unifying notion of an event as a fundamental behavior-modeling concept. We examine several bodies of research with various definitions of an event and its basic units and structure. We use the thinging machine (TM) model to analyze notions related to events, including Dromey s behavior trees, fluents (change over time), recurrent events, and Davidson s events. The results point to an underlying meaning that can lead to a unifying event concept.
This paper introduces the Simulated Jet Engine Bracket Dataset (SimJEB): a new, public collection of crowdsourced mechanical brackets and accompanying structural simulations. SimJEB is applicable to a wide range of geometry processing tasks; the complexity of the shapes in SimJEB offer a challenge to automated geometry cleaning and meshing, while categorical labels and structural simulations facilitate classification and regression (i.e. engineering surrogate modeling). In contrast to existing shape collections, SimJEB's models are all designed for the same engineering function and thus have consistent structural loads and support conditions. On the other hand, SimJEB models are more complex, diverse, and realistic than the synthetically generated datasets commonly used in parametric surrogate model evaluation. The designs in SimJEB were derived from submissions to the GrabCAD Jet Engine Bracket Challenge: an open engineering design competition with over 700 hand-designed CAD entries from 320 designers representing 56 countries. Each model has been cleaned, categorized, meshed, and simulated with finite element analysis according to the original competition specifications. The result is a collection of 381 diverse, high-quality and application-focused designs for advancing geometric deep learning, engineering surrogate modeling, automated cleaning and related geometry processing tasks.
Giuliano Lorenzoni, Paulo Alencar, Nathalia Nascimento
et al.
Data scientists often develop machine learning models to solve a variety of problems in the industry and academy but not without facing several challenges in terms of Model Development. The problems regarding Machine Learning Development involves the fact that such professionals do not realize that they usually perform ad-hoc practices that could be improved by the adoption of activities presented in the Software Engineering Development Lifecycle. Of course, since machine learning systems are different from traditional Software systems, some differences in their respective development processes are to be expected. In this context, this paper is an effort to investigate the challenges and practices that emerge during the development of ML models from the software engineering perspective by focusing on understanding how software developers could benefit from applying or adapting the traditional software engineering process to the Machine Learning workflow.