In light of the increasing population leading to climatic changes across the globe, it has become imperative to dispose of waste materials, both organic and inorganic, bionic and non-bionic, to mitigate all aspects of environmental pollution, that is, land, water, and air. One method is to reuse such waste in the construction industry, which is also a major contributor to environmental pollution, in various forms, viz., admixture, additive, and reinforcement. In the present investigation, paddy stubble ash (PSA) was prepared by burning the stubble within an enclosure subjected to isothermal heating. The ash was found to comprise more than 60% silicon, aluminum, magnesium, and oxygen, confirming it as a pozzolanic material. The assay was followed by replacement of ordinary Portland cement (OPC) at 5%, 7.5%, and 10%. The structural properties of the lightweight concrete blocks, such as compressive strength and durability, were investigated following the IS 516:1959 (Reaffirmed 2018) and IS 456:2000 (Reaffirmed 2021) standards, respectively. At 7.5% replacement, the compressive strength had the highest value of 25.27 MPa, 30.61 MPa, and 34.28 MPa for 7 days, 28 days, and 56 days of curing, respectively, and that of the 10% replacement was the lowest. The scanning electron microscope (SEM) micrographs revealed uniformity in calcium silicate hydrate (C–S–H) gel formation during the hydration process for the concrete mix compositions. The energy-dispersive X-ray analysis (EDAX) and X-ray diffraction (XRD) elemental analysis showed dominance of calcium (Ca) and its compounds in PSA-incorporated concrete, as well as in the control concrete after 28 days and 56 days of curing. The thermogravimetric analysis (TGA) reveals significant mass loss for control concrete and 5% PSA-replaced concrete, whereas with the 7.5% and 10% PSA-incorporated concretes, the mass loss was found to be reduced because of better moisture absorption.
Engineering (General). Civil engineering (General), City planning
Automated semantic understanding of dense point clouds is a prerequisite for Scan-to-BIM pipelines, digital twin construction, and as-built verification--core tasks in the digital transformation of the construction industry. Yet for industrial mechanical, electrical, and plumbing (MEP) facilities, this challenge remains largely unsolved: TLS acquisitions of water treatment plants, chiller halls, and pumping stations exhibit extreme geometric ambiguity, severe occlusion, and extreme class imbalance that architectural benchmarks (e.g., S3DIS or ScanNet) cannot adequately represent. We present Industrial3D, a terrestrial LiDAR dataset comprising 612 million expertly labelled points at 6 mm resolution from 13 water treatment facilities. At 6.6x the scale of the closest comparable MEP dataset, Industrial3D provides the largest and most demanding testbed for industrial 3D scene understanding to date. We further establish the first industrial cross-paradigm benchmark, evaluating nine representative methods across fully supervised, weakly supervised, unsupervised, and foundation model settings under a unified benchmark protocol. The best supervised method achieves 55.74% mIoU, whereas zero-shot Point-SAM reaches only 15.79%--a 39.95 percentage-point gap that quantifies the unresolved domain-transfer challenge for industrial TLS data. Systematic analysis reveals that this gap originates from a dual crisis: statistical rarity (215:1 imbalance, 3.5x more severe than S3DIS) and geometric ambiguity (tail-class points share cylindrical primitives with head-class pipes) that frequency-based re-weighting alone cannot resolve. Industrial3D, along with benchmark code and pre-trained models, will be publicly available at https://github.com/pointcloudyc/Industrial3D.
Abstract This study examines the barriers to implementing sustainable construction practices in Nigeria, focusing on organizational culture, availability of sustainable technologies, stakeholder engagement, financial constraints, and government regulations. It identifies and contextualizes unique challenges specific to Nigeria’s socio-economic and regulatory landscape, offering insights that extend the global understanding of sustainable construction barriers. By employing a mixed-methods approach, this research incorporates a comprehensive literature review and a cross-sectional survey of 237 construction professionals, including project managers, engineers, and policymakers. Quantitative data analysis using SPSS and LISREL revealed that weak regulatory frameworks and high initial costs of sustainable materials and technologies are significant barriers to sustainability adoption. Organizational culture also emerged as a critical determinant, with firms prioritizing internal sustainability being more likely to adopt sustainable practices. The novel contributions of this study include practical, context-specific recommendations for overcoming these barriers, such as enhancing regulatory enforcement, promoting innovative financing mechanisms, and fostering collaborative stakeholder engagement. The findings offer targeted solutions that policymakers and construction firms can adopt to promote sustainability, providing a roadmap for advancing sustainable construction in developing economies.
The continuous integration of artificial intelligence (AI) technologies, represented by machine learning, deep learning, and foundation models, with biotechnology has brought numerous benefits to human health, but it has also inevitably given rise to a series of risks that have attracted significant attention from governments, businesses, think tanks, and research institutions. This paper analyzes the potential risks of integrating AI and biotechnology, focusing on knowledge acquisition, toxin design, and pathogen modification. We review the current status of AI governance in the global biotechnology field from four perspectives: international frameworks, national legislation, industry guidelines, and scientist initiatives. To mitigate relevant risks in China, we propose a series of recommendations, including developing risk assessment methods and tools, strengthening legislation, and actively participating in global governance, aiming to contribute to the construction of a responsible governance framework with Chinese characteristics.
Recommender systems have generated tremendous value for both users and businesses, drawing significant attention from academia and industry alike. However, due to practical constraints, academic research remains largely confined to offline dataset optimizations, lacking access to real user data and large-scale recommendation platforms. This limitation reduces practical relevance, slows technological progress, and hampers a full understanding of the key challenges in recommender systems. In this survey, we provide a systematic review of industrial recommender systems and contrast them with their academic counterparts. We highlight key differences in data scale, real-time requirements, and evaluation methodologies, and we summarize major real-world recommendation scenarios along with their associated challenges. We then examine how industry practitioners address these challenges in Transaction-Oriented Recommender Systems and Content-Oriented Recommender Systems, a new classification grounded in item characteristics and recommendation objectives. Finally, we outline promising research directions, including the often-overlooked role of user decision-making, the integration of economic and psychological theories, and concrete suggestions for advancing academic research. Our goal is to enhance academia's understanding of practical recommender systems, bridge the growing development gap, and foster stronger collaboration between industry and academia.
The application of artificial intelligence (AI) in industry is accelerating the shift from traditional automation to intelligent systems with perception and cognition. Vision language-action (VLA) models have been a key paradigm in AI to unify perception, reasoning, and control. Has the performance of the VLA models met the industrial requirements? In this paper, from the perspective of industrial deployment, we compare the performance of existing state-of-the-art VLA models in industrial scenarios and analyze the limitations of VLA models for real-world industrial deployment from the perspectives of data collection and model architecture. The results show that the VLA models retain their ability to perform simple grasping tasks even in industrial settings after fine-tuning. However, there is much room for performance improvement in complex industrial environments, diverse object categories, and high precision placing tasks. Our findings provide practical insight into the adaptability of VLA models for industrial use and highlight the need for task-specific enhancements to improve their robustness, generalization, and precision.
V. Sanchez Padilla, Albert Espinal, Jennifer M. Case
et al.
ABET accreditation is an increasingly prominent system of global accreditation of engineering programs, and the assessment requires programs to demonstrate that they meet the needs of the program's stakeholders, typically industrial potential employers of graduates. To obtain these inputs, programs are required to assemble an advisory committee board. The views of the advisory board on the relevance of the degree outcomes are an essential part of this process. The purpose of this qualitative research study is to explore the viewpoints that industry stakeholders have on this type of process. The context for the study was an Ecuadorian engineering program which had successfully achieved the ABET accreditation. The study drew on interviews undertaken with industry members who were part of the advisory board. This study focuses on how they perceive the process and the accreditation awarded, analyzing their views of its usefulness, especially in relation to the employability of graduates. Based on the findings, we offer critical insights into this accreditation process when it takes place in contexts beyond highly industrialized countries.
The construction industry has advanced to a great extent by using natural fibres and mineral admixtures in concrete taking sustainability into account. The addition of glass fibre at less proportion has shown some interesting values in terms of tensile behavior. Admixtures have mostly been used in concrete to support the development of work in the construction industry. Certain admixtures are required to get the intended effects out of the concrete. The paper revolves around the examination of the mechanical behavior of glass fibre-reinforced concrete (GFRC) with cement partially replaced by silica fume such as compression test and split tensile strength. Silica fume as an admixture is added to glass fibre-reinforced concrete in this paper in varying quantities to partially replace cement. Glass fibre 1% and silica fume at varied percentages such as 15, 20, and 25% have been used. The compressive strength increased by 13% and tensile strength by 40% in comparison with conventional concrete.
In this article, we investigate a growing trend in the worldwide Quantum Technology (QT) education landscape, that of the development of masters programs, intended to provide graduates with the knowledge and skills to take a job in the quantum industry, while serving a much shorter timeline than a doctoral degree. Through a global survey, we identified 86 masters programs, with substantial growth since 2021. Over time masters have become increasingly interdisciplinary, organised by multiple faculties or through joint degree programs, and offer more hands-on experiences such as internships in companies. Information from program organisers and websites suggests that the intended career destinations of their graduates are a diverse range of industries, and therefore masters programs may be a boon to the industrialisation of quantum technologies. Finally, we identify a range of national efforts to grow the quantum workforce of many countries, quantum program enhancements, which augment the content of existing study programs with quantum content. This may further contribute to the growth and viability of masters programs as a route to the quantum industry.
Wesley Hanwen Deng, Solon Barocas, Jennifer Wortman Vaughan
Recent years have witnessed increasing calls for computing researchers to grapple with the societal impacts of their work. Tools such as impact assessments have gained prominence as a method to uncover potential impacts, and a number of publication venues now encourage authors to include an impact statement in their submissions. Despite this push, little is known about the way researchers assess, articulate, and address the potential negative societal impact of their work -- especially in industry settings, where research outcomes are often quickly integrated into products. In addition, while there are nascent efforts to support researchers in this task, there remains a dearth of empirically-informed tools and processes. Through interviews with 25 industry computing researchers across different companies and research areas, we first identify four key factors that influence how they grapple with (or choose not to grapple with) the societal impact of their research. To develop an effective impact assessment template tailored to industry computing researchers' needs, we conduct an iterative co-design process with these 25 industry researchers and an additional 16 researchers and practitioners with prior experience and expertise in reviewing and developing impact assessments or broad responsible computing practices. Through the co-design process, we develop 10 design considerations to facilitate the effective design, development, and adaptation of an impact assessment template for use in industry research settings and beyond, as well as our own ``Societal Impact Assessment'' template with concrete scaffolds. We explore the effectiveness of this template through a user study with 15 industry research interns, revealing both its strengths and limitations. Finally, we discuss the implications for future researchers and organizations seeking to foster more responsible research practices.
Jimmy Xuekai Li, Tiancheng Zhang, Yiran Zhu
et al.
Artificial General Intelligence (AGI) is set to profoundly impact the oil and gas industry by introducing unprecedented efficiencies and innovations. This paper explores AGI's foundational principles and its transformative applications, particularly focusing on the advancements brought about by large language models (LLMs) and extensive computer vision systems in the upstream sectors of the industry. The integration of Artificial Intelligence (AI) has already begun reshaping the oil and gas landscape, offering enhancements in production optimization, downtime reduction, safety improvements, and advancements in exploration and drilling techniques. These technologies streamline logistics, minimize maintenance costs, automate monotonous tasks, refine decision-making processes, foster team collaboration, and amplify profitability through error reduction and actionable insights extraction. Despite these advancements, the deployment of AI technologies faces challenges, including the necessity for skilled professionals for implementation and the limitations of model training on constrained datasets, which affects the models' adaptability across different contexts. The advent of generative AI, exemplified by innovations like ChatGPT and the Segment Anything Model (SAM), heralds a new era of high-density innovation. These developments highlight a shift towards natural language interfaces and domain-knowledge-driven AI, promising more accessible and tailored solutions for the oil and gas industry. This review articulates the vast potential AGI holds for tackling complex operational challenges within the upstream oil and gas industry, requiring near-human levels of intelligence. We discussed the promising applications, the hurdles of large-scale AGI model deployment, and the necessity for domain-specific knowledge in maximizing the benefits of these technologies.
This document offers a critical overview of the emerging trends and significant advancements in artificial intelligence (AI) within the pharmaceutical industry. Detailing its application across key operational areas, including research and development, animal testing, clinical trials, hospital clinical stages, production, regulatory affairs, quality control and other supporting areas, the paper categorically examines AI's role in each sector. Special emphasis is placed on cutting-edge AI technologies like machine learning algorithms and their contributions to various aspects of pharmaceutical operations. Through this comprehensive analysis, the paper highlights the transformative potential of AI in reshaping the pharmaceutical industry's future.
Abstract The ever-increasing environmental complexity makes strategizing a difficult multidimensional task. In this paper, we conducted a corporate foresight case study in an SME in packaging industry in Iran. The case study offers a detailed procedure of implementing corporate foresight (CF) and how it can reshape traditional strategic planning. A multimethodological approach was taken in this case study. Once an intraorganizational team in studied company was formed, archival document analysis, PESTEL and weak signal analysis, importance/uncertainty matrix, cross-impact balanced (CIB) analysis, scenario construction, wind tunneling, robust decision-making, and premortem session were used to create foresight intelligence. This paper presents a detailed description of how CF can be linked to conventional strategizing and reshape it. Key variables, driving forces, critical uncertainties, and 4 plausible scenarios are presented. The case study illustrates that as alternative realities challenged the foresight teams ingrained presuppositions, they found the dialectic between “weight of history” and “pull of future” both revelatory and indigestible. The CF intervention illuminated the fragility of preexisting strategic objectives, the implicit optimism bias underlying them, and an overflowing-plate syndrome of formulating too many strategic objectives. Consequently, studied company decided to revisit their strategic objectives, prepare a contingency plan for worst-case scenarios, and begin developing a crisis-ready culture. The comprehensive case study demonstrates how CF can enhance and contradict traditional strategizing, presents a rich know-how of added value of scenarios, and provides some subtleties and complexities of CF interventions.
Nur Amira Bahrin, Mohd Khairul Kamarudin, Hazrina Mansor
et al.
Bamboo is a sustainable and cost-effective alternative to traditional construction materials. Despite the fact that three species are well known for structural applications, namely Dendrocalamus asper, Gigantochloa scortechinii, and Gigantochloa levis, the scientific data for their mechanical characterization is scarcely available and widely dispersed. In addition, a systematic literature review appraising the study advancement of mechanical characterization of bamboo had been unavailable. This paper bridges this gap by conducting a systematic literature review (SLR) of the available literature of mechanical characterization of bamboo pole. A total of 54 relevant articles were retrieved from Scopus and snowballing and then put forward through bibliometric analysis using VOSviewer. The results showed that the distribution of data for physical and mechanical characterization of aforementioned species was scattered due to the different location (origin), age, and initial moisture content recorded during empirical work among the researchers. This review's importance and distinctiveness lie in its synthesis of the existing literature on bamboo mechanical characterization. The findings provide a point of reference for both academia and industry by bridging the scarcity of current bamboo engineering data and outlining future possibilities for bamboo research in the building and construction domain.
Restoring historic buildings is a challenging task in an environment where any insensitive or unprofessional intervention can cause irreparable damage. Among the most important demands currently placed on the construction industry are the protection of structural details, materials and technologies, and the extension of the life of these historic buildings. In this context, we should mention the protection of the high number of tenement buildings in European cities from the second half of the 19th and early 20th centuries, whose structural quality is relatively high and where many other building details and elements have been preserved. The brick dwellings of the period, which are between 85 and 170 years old, do not fully comply with many of the requirements and provisions of the current regulations and standards. The serious shortcomings of brick tenement buildings include, among other things, the inadequate thermal resistance of the envelope and infill structures and the high energy consumption of the operation of these buildings. This paper focuses on analysing this situation and defining the requirements for renovation, while preserving the architectural and historical values of urban buildings; achieving acceptable compliance with the requirements and provisions of the currently applicable regulations and standards; and demonstrating cost-effectiveness.
Ali Kaan Kurbanzade, Ansaar M. Baig, Sanjay Mehrotra
Unmanned aerial vehicles, commonly known as drones, have emerged as a disruptive technology with the potential to revolutionize operations across various industries. Drones are the fast-growing internet-of-things technology and are estimated to have a $100 billion market value in the next decade. Exploring drone operations through research has the potential to yield innovative academic insights and create significant practical effects in diverse industries, offering a competitive edge. Drawing insights from both academic and industry literature, this article describes how technological advancements in UAVs may disrupt traditional operational practices in different industries (e.g., commercial last-mile delivery, commercial pickup and delivery, telecommunication, insurance, healthcare, humanitarian, environmental, urban planning, homeland security), identifies the value of this evolving disruptive technology from sustainability and operational innovation perspectives, argues the significance of this area for operations management by conceptualizing a research agenda. The current state of the art focuses on the computing aspect of analytical models to tackle a variety of synthetic drone-related problems, with mixed integer optimization being the primary tool. There is a very significant research gap that should focus on drone operations management with industry know-how by partnering with actual stakeholders and using a variety of tools (i.e., econometrics, field experiments, game theory, optimal control, utility functions). This article aims to promote research on UAVs from operations management and industry-specific point of view.
Philippe Carvalho, Alexandre Durupt, Yves Grandvalet
The field of industrial defect detection using machine learning and deep learning is a subject of active research. Datasets, also called benchmarks, are used to compare and assess research results. There is a number of datasets in industrial visual inspection, of varying quality. Thus, it is a difficult task to determine which dataset to use. Generally speaking, datasets which include a testing set, with precise labeling and made in real-world conditions should be preferred. We propose a study of existing benchmarks to compare and expose their characteristics and their use-cases. A study of industrial metrics requirements, as well as testing procedures, will be presented and applied to the studied benchmarks. We discuss our findings by examining the current state of benchmarks for industrial visual inspection, and by exposing guidelines on the usage of benchmarks.
Digitalization is being adopted in many public services to increase the efficiencies of the required operations. Regarding this, there is an important interest in digitalizing the current building permit procedures since most of the buildings are designed digitally and as three-dimensional (3D). In addition, several countries are making an effort to realize the transition from two-dimensional (2D) cadastre to 3D cadastre. This is because 2D delineation of the legal rights may remain incapable to reflect the reality with respect to property ownership in multipartite buildings. The 3D city models should also be kept updated to effectively manage the occasions (e.g., natural disasters) and services (e.g., waterworks) in the living areas. In this sense, the open data standards (e.g., CityGML and Industry Foundation Classes (IFC)) have a vital role to enable interoperability between different domains such as Architecture, Engineering, and Construction (AEC) and Land Administration (LA). In this context, this paper aims to show the current situation and opportunities on how to efficaciously benefit from open data standards for three significant issues. The issues can be listed as, 1) digitalizing the building permit procedures, 2) registering the condominium as 3D, and 3) updating the 3D city models. The examination in the paper concerns the cases for Turkey.
Andrew Trafford, Robert Ellwood, Loris Wacquier
et al.
Abstract The long-term sustainability of the offshore wind industry requires the development of appropriate investigative methods to enable less conservative and more cost-effective geotechnical engineering design. Here we describe the novel use of distributed acoustic sensing (DAS) as part of an integrated approach for the geophysical and geotechnical assessment of the shallow subsurface for offshore construction. DAS was used to acquire active Scholte-wave seismic data at several locations in the vicinity of a planned windfarm development near Dundalk Bay, Irish Sea. Complimentary additional datasets include high-resolution sparker seismic reflection, cone penetration test (CPT) data and gravity coring. In terms of fibre optic cable selection, a CST armoured cable provided a reasonable compromise between performance and reliability in the offshore environment. Also, when used as a seismic source, a gravity corer enabled the fundamental mode Scholte-wave to be better resolved than an airgun, and may be more suitable in environmentally sensitive areas. Overall, the DAS approach was found to be effective at rapidly determining shear wave velocity profiles in areas of differing geological context, with metre scale spatial sampling, over multi-kilometre scale distances. The application of this approach has the potential to considerably reduce design uncertainty and ultimately reduce levelised costs of offshore wind power generation.