Tolgahan Bardakci, Andreas Faes, Mutlu Beyazit
et al.
Large Language Models (LLMs) are increasingly used to support software testing tasks, yet there is little evidence of their effectiveness for REST API testing in industrial settings. To address this gap, we replicate our earlier work on LLM-based REST API test amplification within an industrial context at one of the largest logistics companies in Belgium. We apply LLM-based test amplification to six representative endpoints of a production microservice embedded in a large-scale, security-sensitive system, where there is in-depth complexity in authentication, stateful behavior, and organizational constraints. Our experience shows that LLM-based test amplification remains practically useful in industry by increasing coverage and revealing various observations and anomalies.
Francesc Sabater, Ouns El Harzli, Geert-Jan Besjes
et al.
Optimization via decoded quantum interferometry (DQI) has recently gained a great deal of attention as a promising avenue for solving optimization problems using quantum computers. In this paper, we apply DQI to an industrial optimization problem in the automotive industry: the vehicle option-package pricing problem. Our main contributions are 1) formulating the industrial problem as an integer linear program (ILP), 2) converting the ILP into instances of max-XORSAT, and 3) developing a detailed quantum circuit implementation for belief propagation, a heuristic algorithm for decoding LDPC codes. Thus, we provide a full implementation of the DQI algorithm using Belief Propagation, which can be applied to any industrially relevant ILP by first transforming it into a max-XORSAT instance. We also evaluate the effectiveness of our implementation by benchmarking it against both Gurobi and a random sampling baseline.
Mehrdad Saadatmand, Abbas Khan, Beatriz Marin
et al.
The evolving landscape of software development demands that software testers continuously adapt to new tools, practices, and acquire new skills. This study investigates software testing competency needs in industry, identifies knowledge gaps in current testing education, and highlights competencies and gaps not addressed in academic literature. This is done by conducting two focus group sessions and interviews with professionals across diverse domains, including railway industry, healthcare, and software consulting and performing a curated small-scale scoping review. The study instrument, co-designed by members of the ENACTEST project consortium, was developed collaboratively and refined through multiple iterations to ensure comprehensive coverage of industry needs and educational gaps. In particular, by performing a thematic qualitative analysis, we report our findings and observations regarding: professional training methods, challenges in offering training in industry, different ways of evaluating the quality of training, identified knowledge gaps with respect to academic education and industry needs, future needs and trends in testing education, and knowledge transfer methods within companies. Finally, the scoping review results confirm knowledge gaps in areas such as AI testing, security testing and soft skills.
Abstract Economic growth is closely related to carbon emissions, and determining the appropriate emission reduction targets for various sectors under different economic models has always been a challenge. This paper utilizes an Energy-Economic-Environment CGE model to simulate two types of economic growth models: extensive and intensive. Four economic growth scenarios are defined, and initial carbon quota allocations for various sectors are obtained for China at two key points: the peak year (2029) and the post-peak year (2035). The ZSG-DEA model is applied, considering the principles of fairness and efficiency, to iterate carbon efficiency across 33 industries and obtain quota adjustment values. The results indicate that the innovation-driven scenario, representing intensive growth, achieves a win-win outcome compared to other scenarios by enhancing GDP and avoiding additional carbon reduction costs. The initial carbon emission efficiency in agriculture, chemicals, steel, electronics, water supply, and services all reached 1. Comparative analysis reveals that the sectors of electricity, chemicals, coal, and cement face higher emission reduction pressures, while agriculture and services experience relatively lower pressures.
Abstract The large quantity of steel slag generated from the iron and steel industry needs to be disposed. Using alkali–activated steel slag as a building material is a good method of comprehensive utilization. In this paper, the properties of steel slag activated by liquid sodium silicate with a silicate modulus of 1.5 as clinker–free cement were investigated. Portland cement with the same water–binder ratio of 0.45 was used as the reference sample. Detailed comparisons including the hydration properties, microstructure characteristics and mechanical strength of the two binders were conducted. The results show that alkali–activated steel slag and cement have similar hydration processes and products. However, the hydration of alkali–activated steel slag has a shorter dormant stage, an earlier and smaller second exothermic peak, a lower cumulative heat, less and poorer crystallization of Ca(OH)2, a lower Ca–Si ratio and a similar Al–Si ratio in gels. Compared to the morphology of cement paste, looser microstructure, unhydrated particles and Ca(OH)2 crystals in a single sheet can be seen in alkali–activated steel slag paste, forming a weak link in the matrix and damaging the strength development. The compressive strengths of alkali–activated steel slag hardened pastes are only 30–40% of the strengths of cement pastes due to poor hydration of former. Therefore, although its efficacy is lower than that of cement, steel slag could replace cement as a building material for certain engineering applications due to its more economic and environmental benefits.
Abstract In recent decades, a novel concept of accelerating hydration process of cement has been proposed by adding external nuclei into a hydrating cement system, which is called seeding technology. Nano-sized C-S-H particles are prepared and characterized by means of dynamic light scattering (DLS), TEM, XRD, FT-IR and TGA. It has been realized that polycarboxylate superplasticizer (PCE) often leads to lower early strength of hardened cement paste (hcp) due to its retardation action, which is undesired for pre-fabricated concrete and repair mortars industry. The mechanism of nano-C-S-H seeds accelerating cement hydration containing PCE is investigated by isothermal calorimetry. The mortar strength growth with inclusion of seeds is investigated at every curing age of 1 d, 3 d and 28 d. Results show that nano-C-S-H particles significantly promote the early strength development for mortar containing PCE without obvious reduction of 28 d strength. The threshold pore size of hcp is also significantly decreased. The addition of nano-sized seeds decreases [Ca] in the pore solution, which is needed by C-S-H nucleation. More importantly, this paper brings a direct evidence that nano-C-S-H seeds provide more nucleation sites for further generated C-S-H precipitate by separated hydration method. C-S-H gel is identified as the substance growing on nano-C-S-H seeds by FT-IR, XRD and TGA. The separated hydration result firmly indicates that homogeneous nucleation is irrelevant in the blank hydrating cement pastes and the C-S-H hydration product is able to grow on the surface of the added foreign C-S-H seeds.
Abstract The supplementary cementitious materials (SCMs) are used as a substitute for the cement to reduce the environmental issues backed by concrete industry for the past three decades. Additionally, the use of industrial waste as SCMs can mitigate the wastes dumped in lagoons and landfills sites. Ground granulated blast furnace slag (GGBS) is one of the potential candidates as it possesses strength, durability, economic, and environmental benefits. In this study, GGBS is used in Ultra high performance concrete (UHPC) up to 80% replacement level of cement. Various properties such as flowability, compressive strength, tensile strength, fracture, and durability of UHPC with a high volume GGBS are experimentally evaluated under two curing conditions (Standard water and elevated temperature curing). Uniaxial tensile (UAT) strength test is conducted to determine the tensile strength for UHPC. The test results inferred that the hardened properties of GGBS based UHPC are significant upto 40% cement replacement level under standard water curing. Elevated temperature curing, improves its performance upto 60% replacement level. Finally, microstructure properties are studied using scanning electron microscopy which confirms the dense microstructure of UHPC with a high volume GGBS.
Abstract With increasing population and rising demands for improved built environment, there is an expected increase in greenhouse gas emission from the construction industry. Carbon dioxide emission levels are fast approaching a tipping point which could lead to irreversible climate change. The earth's capability to neutralise the CO2 emissions through the natural carbon cycle has been overstretched. Therefore it is imperative to adopt technologies that are able to capture and sequester CO2 in order to cancel out their release from industrial activities such as the construction and building industry. This is important so that cement-based material productions' carbon footprint can be reduced drastically for a positive change to take place in the climate. Biochar holds great promise as an effective CO2 sorptive material in cement-based applications relatively similar to its conventional use for soil amendment. Actually, fragmented researches on biochar as an admixture in cementitious materials have been conducted. Based on this logic, this review placed enormous emphasis on collating information from recent studies on biochars from agro-sources used as an admixture in cement-based applications. Similarly, the review gave up-to-date knowledge about the sources of the biomass and the production processes. Conclusively, the positive effects of biochar for carbon sequestration on some properties of the various cementitious applications were highlighted.
With the increased attention toward nanotechnology and their innovative use for different industries including but not limited to food, biomedical, electronics, materials, etc, the application of nanotechnology or nanoparticles in the oil and gas industry is a subject undergoing intense study by major oil companies, which is reflected through the huge amount of funds invested on the research and development, with respect to the nanotechnology. Nanotechnology has been recently investigated extensively for different applications in the oil and gas industry such as drilling fluids and enhanced oil recovery in addition to other applications including cementing and well stimulation. In this paper, comprehensive literature was conducted to review the different applications of nanotechnology in the oil and gas industry. A summary of all nanoparticles used along with a detailed analysis of their performance in improving the targeted parameters is comprehensively presented. The main objective of this review was to provide a comprehensive summary of the different successful applications of nanotechnology and its associated challenges, which could be very helpful for future researches and applications.
Lahiba Imtiaz, Sardar Kashif Ur Rehman, Shazim Ali memon
et al.
The emission of CO2 and energy requirement in the production of Ordinary Portland Cement (OPC) causes the continuous depletion of ozone layer and global warming. The introduction of geopolymer concrete (GPC) technology in the construction industry leads to sustainable development and cleaner environment by reducing environmental pollution. In this article, constituents of GPC and their influence on properties of GPC has been reviewed critically. Fresh and hardened properties of GPC as well as the factors influencing these properties are discussed in detail. Flow charts have been proposed to show which factors have higher/lower impact on the fresh and hardened properties of GPC. A comprehensive review on the mix design of GPC, nanomaterial-based GPC, 3D printing using GPC, reinforced GPC and Global warming potential (GWP) assessment was conducted. Finally, the practical applications of GPC in the construction industry are provided.
Abstract Piezoelectric materials are capable of transforming mechanical strain and vibration energy into electrical energy. This property allows opportunities for implementing renewable and sustainable energy through power harvesting and self-sustained smart sensing in buildings. As the most common construction material, plain cement paste lacks satisfactory piezoelectricity and is not efficient at harvesting the electrical energy from the ambient vibrations of a building system. In recent years, many techniques have been proposed and applied to improve the piezoelectric capacity of cement-based composite, namely admixture incorporation (e.g. lead zirconate titanate, barium zirconate titanate, carbon particles, and steel fibers) and physical treatments (e.g. thermal heating and electrical field application). The successful application of piezoelectric materials for sustainable building development not only relies on understanding the mechanism of the piezoelectric properties of various building components, but also the latest developments and implementations in the building industry. Therefore, this review systematically illustrates research efforts to develop new construction materials with high piezoelectricity and energy storage capacity. In addition, this article discusses the latest techniques for utilizing the piezoelectric materials in energy harvesters, sensors, and actuators for various building systems. With advanced methods for improving the cementitious piezoelectricity and applying the material piezoelectricity for different building functions, more renewable and sustainable building systems are anticipated.
Industrial Internet of Things (IIoT) technologies have revolutionized industrial processes, enabling smart automation, real-time data analytics, and improved operational efficiency across diverse industry sectors. IIoT testbeds play a critical role in advancing IIoT research and development (R&D) to provide controlled environments for technology evaluation before their real-world deployment. In this article, we conduct a comprehensive literature review on existing IIoT testbeds, aiming to identify benchmark performance, research gaps and explore emerging trends in IIoT systems. We first review the state-of-the-art resource management solutions proposed for IIoT applications. We then categorize the reviewed testbeds according to their deployed communication protocols (including TSN, IEEE 802.15.4, IEEE 802.11 and 5G) and discuss the design and usage of each testbed. Driven by the knowledge gained during this study, we present suggestions and good practices for researchers and practitioners who are planning to design and develop IIoT testbeds for connectivity research.
Time series forecasting in the air cargo industry presents unique challenges due to volatile market dynamics and the significant impact of accurate forecasts on generated revenue. This paper explores a comprehensive approach to demand forecasting at the origin-destination (O\&D) level, focusing on the development and implementation of machine learning models in decision-making for the air cargo industry. We leverage a mixture of experts framework, combining statistical and advanced deep learning models to provide reliable forecasts for cargo demand over a six-month horizon. The results demonstrate that our approach outperforms industry benchmarks, offering actionable insights for cargo capacity allocation and strategic decision-making in the air cargo industry. While this work is applied in the airline industry, the methodology is broadly applicable to any field where forecast-based decision-making in a volatile environment is crucial.
Cyber attacks leveraging or targeting the software supply chain, such as the SolarWinds and the Log4j incidents, affected thousands of businesses and their customers, drawing attention from both industry and government stakeholders. To foster open dialogue, facilitate mutual sharing, and discuss shared challenges encountered by stakeholders in securing their software supply chain, researchers from the NSF-supported Secure Software Supply Chain Center (S3C2) organize Secure Supply Chain Summits with stakeholders. This paper summarizes the Industry Secure Supply Chain Summit held on November 16, 2023, which consisted of \panels{} panel discussions with a diverse set of \participants{} practitioners from the industry. The individual panels were framed with open-ended questions and included the topics of Software Bills of Materials (SBOMs), vulnerable dependencies, malicious commits, build and deploy infrastructure, reducing entire classes of vulnerabilities at scale, and supporting a company culture conductive to securing the software supply chain. The goal of this summit was to enable open discussions, mutual sharing, and shedding light on common challenges that industry practitioners with practical experience face when securing their software supply chain.
This paper explores the potential application of Deep Reinforcement Learning in the furniture industry. To offer a broad product portfolio, most furniture manufacturers are organized as a job shop, which ultimately results in the Job Shop Scheduling Problem (JSSP). The JSSP is addressed with a focus on extending traditional models to better represent the complexities of real-world production environments. Existing approaches frequently fail to consider critical factors such as machine setup times or varying batch sizes. A concept for a model is proposed that provides a higher level of information detail to enhance scheduling accuracy and efficiency. The concept introduces the integration of DRL for production planning, particularly suited to batch production industries such as the furniture industry. The model extends traditional approaches to JSSPs by including job volumes, buffer management, transportation times, and machine setup times. This enables more precise forecasting and analysis of production flows and processes, accommodating the variability and complexity inherent in real-world manufacturing processes. The RL agent learns to optimize scheduling decisions. It operates within a discrete action space, making decisions based on detailed observations. A reward function guides the agent's decision-making process, thereby promoting efficient scheduling and meeting production deadlines. Two integration strategies for implementing the RL agent are discussed: episodic planning, which is suitable for low-automation environments, and continuous planning, which is ideal for highly automated plants. While episodic planning can be employed as a standalone solution, the continuous planning approach necessitates the integration of the agent with ERP and Manufacturing Execution Systems. This integration enables real-time adjustments to production schedules based on dynamic changes.
Gurjot Singh Bhatia, Yoann Corre, Linus Thrybom
et al.
Appropriate channel models tailored to the specific needs of industrial environments are crucial for the 5G private industrial network design and guiding deployment strategies. This paper scrutinizes the applicability of 3GPP's channel model for industrial scenarios. The challenges in accurately modeling industrial channels are addressed, and a refinement strategy is proposed employing a ray-tracing (RT) based channel model calibrated with continuous-wave received power measurements collected in a manufacturing facility in Sweden. The calibration helps the RT model achieve a root mean square error (RMSE) and standard deviation of less than 7 dB. The 3GPP and the calibrated RT model are statistically compared with the measurements, and the coverage maps of both models are also analyzed. The calibrated RT model is used to simulate the network deployment in the factory to satisfy the reference signal received power (RSRP) requirement. The deployment performance is compared with the prediction from the 3GPP model in terms of the RSRP coverage map and coverage rate. Evaluation of deployment performance provides crucial insights into the efficacy of various channel modeling techniques for optimizing 5G industrial network planning.