Search Results (20)

The construction sector is a major contributor to environmental degradation due to high energy consumption and CO₂ emissions. This study presents a low-tech, sustainable construction system based on the manual fabrication of curved laminated bamboo beams, assembled with screws and steel straps, without adhesives or heavy machinery. The case study is part of a bamboo roof structure built within Jericoacoara National Park, Brazil, using Dendrocalamus asper for its mechanical strength and carbon storage capacity. The construction process of three vertical lower laminated curved beams (Vig.CLIV-1, CLIV-2, and CLIV-3) was systematized into two main phases—preparation and construction. Due to the level of detail involved, only Vig.CLIV-1 is fully presented, broken down into work items, processes, and sub-processes to identify critical points for quality control and time efficiency. Comparative analysis of the three beams complements the findings, highlighting differences in logistics, labor performance, and learning outcomes. The results demonstrate the potential of this handcrafted system to achieve high geometric accuracy in complex site conditions, with low embodied energy and strong replicability. Developed by bamboo specialists from Colombia and Peru with support from local assistants, this experience illustrates the viability of low-impact, appropriate construction solutions for ecologically sensitive contexts and advances the integration of sustainable, replicable practices in architectural design. Full article

Modeling the soil–plant–atmosphere continuum (SPAC) system requires multiple subprocesses and numerous parameters. Sensitivity analysis is effective to identify important model components and improve the modeling efficiency. However, most sensitivity analyses for SPAC models focus on parameter-level assessment, providing limited insights into process-level importance. To address this gap, this study proposes a process sensitivity analysis method that integrates the Bayesian network with variance-based sensitivity measures. Four subprocesses are demarcated based on the physical relationships between model components revealed by the network. Applied to a winter wheat SPAC system under different water conditions, the method effectively and reliably identifies critical processes. The results indicate that, under minimal water stress, the subprocesses of photosynthesis and dry matter partitioning primarily determine agricultural outputs. As the water supply decreases, the subprocesses of soil water movement and evapotranspiration gain increasing importance, becoming predominant under sever water stress. Throughout the crop season, the subprocess importance and its response to water stress are modulated by the crop phenology. Compared to conventional parameter sensitivity analysis, our method excels in synthesizing divergent parameter importance changes and identifying influential subprocesses, even without high-sensitivity parameters. This study provides new insights into adaptive SPAC modeling by dynamically simplifying unimportant subprocesses in response to environmental changes. Full article

Preventing secondary crises resulting from emergency incidents in engineering projects is a crucial and complex task for project operation management. Public opinion and its underlying sentiment can act as reliable indicators, reflecting the progression of emergency incidents, and warrant serious consideration. With the advent of Web 2.0, the management of online public opinion (OPO) through social platforms has advanced significantly. However, previous research has overlooked the diverse categories of participants contributing to OPO evolution. This article proposes an optimised bounded confidence model (BCM) for sentiment OPO evolution under emergency situations at railway stations, incorporating multiple participant categories. A conceptual model based on eleven assumptions is developed, involving four key participants (netizens, media, opinion leaders, and government) structured into four sub-processes. To illustrate this model, the case of the Wuhan railway stations’ blockade during the COVID-19 outbreak is examined. This case study demonstrates the initial data acquisition and simulation process. The standard simulation results are recorded, followed by a multiple-sensitivity analysis to investigate the impact of various critical factor combinations on OPO evolution. Finally, policy recommendations are provided to government departments to enhance their response to emergency situations, particularly those involving railway stations, thereby ensuring public safety. Full article

This paper focuses on the optimization of a critical operation in the furniture manufacturing process, identifying it as a key priority for improvement by applying Systems Theory. The primary objective of this study is to develop a mathematical model for optimizing the detected key process by employing artificial neural networks (ANNs) which mirror adaptive management principles. Three input and three output parameters significantly impacting the effectiveness of this key process have been systematically identified and experimentally measured. It was necessary to perform multi-objective optimization (MOO), which consisted in achieving the minimum values of cost and process time and the maximum value of the quality index through the optimal setting of the input parameters (cutting speed, feed rate, and volume of removed material). The application of ANNs in MOO in this research study is a novelty in this field. The results obtained through application of the ANN method reveal the optimal values of the examined parameters, which represent the best combination of input technical variables leading to the best results in output economic parameters. This multi-objective optimizing solution facilitates enhanced process efficiency. By integrating Systems Theory, Complexity Theory, and adaptive management, this research advances sustainable process improvements by minimizing resource use, reducing waste, and enhancing overall system efficiency. Full article

Introduction: The frozen section intra-operative consultation is a pathology procedure that provides real-time evaluations of tissue samples during surgery, enabling quick and informed decisions. In the pre-analytical phase, errors related to sample collection, transport, and identification are common, and tools like failure mode, effects, and criticality analysis help identify and prevent risks. This study aims to enhance patient safety and diagnostic quality by analyzing risks and optimizing sample management. Materials and Methods: The failure mode, effects, and criticality analysis was conducted by a multidisciplinary team to analyze the workflow of frozen section sample handling from collection in the operating theater to acceptance at the pathology lab. Six steps were identified, each assigned tasks and responsibilities, with risks assessed through the risk priority number, calculated from severity, occurrence, and detectability. Severity was classified based on the WHO framework, ranging from “No Harm” to “Death”, to prioritize risks effectively. Results: The study identified 12 failure modes across 11 sub-processes, prioritized by risk. Key failures included missing patient identification, incorrect sample retrieval, missing labels, misdirected samples, and samples sent to the wrong lab. Discussion: Pre-analytical errors in pathology pose risks to diagnosis and patient care, with most errors occurring in this phase. A multidisciplinary team identified key issues, such as sample mislabeling and delays due to staff unavailability, and implemented corrective actions, including improved signage, staff re-training, and sample tracking systems. Monitoring and regular checks ensured ongoing adherence to protocols and reduced the risks of misidentification, transport delays, and procedural errors. Conclusions: The frozen section intra-operative consultation is vital in surgical pathology, with the pre-analytical phase posing significant risks due to potential errors in sample handling and labeling. Failure mode, effects, and criticality analysis has proven effective in identifying and prioritizing these failures, despite resource demands, by allowing corrective actions that enhance patient safety and healthcare quality. Full article

This research addresses a critical gap in understanding the environmental impact of natural rock aggregate production in Sri Lanka. The study employs life cycle assessment (LCA) and SimaPro Software to simulate natural coarse aggregates’ extraction and manufacture process. Key findings reveal significant environmental impacts, with human carcinogenic toxicity (2.45938 × 10⁻⁶ Pt), eutrophication of freshwater (1.59326 × 10⁻⁶ Pt), and fossil resource scarcity (1.4823 × 10⁻⁶ Pt) being significant concerns. The crushing process in particular shows the highest levels, contributing 2.21 × 10⁶ to human carcinogenic toxicity and 8.92 × 10⁷ to freshwater eutrophication. High electricity consumption, particularly from hard coal in electricity generation, is identified as a primary contributor. Although the sole source of coarse aggregate production in Sri Lanka is natural rock crushing, there is a lack of country-specific environmental impact assessment data for this process. This study provides a valuable dataset for the Sri Lankan construction industry, covering various environmental impact categories and encompassing the sub-processes inherent to natural rock aggregate production. The research highlights the necessity of implementing sustainable practices in quarry operations, proposing a transition towards more environmentally friendly energy sources. By quantifying environmental effects, this study provides valuable insights for stakeholders in the construction sector, enabling informed decision-making and targeted interventions to enhance overall sustainability while offering aggregate manufacturers opportunities to adopt more sustainable practices. Full article

This paper presents a new edge detection process implemented in an embedded IoT device called Bee Smart Detection node to detect catastrophic apiary events. Such events include swarming, queen loss, and the detection of Colony Collapse Disorder (CCD) conditions. Two deep learning sub-processes are used for this purpose. The first uses a fuzzy multi-layered neural network of variable depths called fuzzy-stranded-NN to detect CCD conditions based on temperature and humidity measurements inside the beehive. The second utilizes a deep learning CNN model to detect swarming and queen loss cases based on sound recordings. The proposed processes have been implemented into autonomous Bee Smart Detection IoT devices that transmit their measurements and the detection results to the cloud over Wi-Fi. The BeeSD devices have been tested for easy-to-use functionality, autonomous operation, deep learning model inference accuracy, and inference execution speeds. The author presents the experimental results of the fuzzy-stranded-NN model for detecting critical conditions and deep learning CNN models for detecting swarming and queen loss. From the presented experimental results, the stranded-NN achieved accuracy results up to 95%, while the ResNet-50 model presented accuracy results up to 99% for detecting swarming or queen loss events. The ResNet-18 model is also the fastest inference speed replacement of the ResNet-50 model, achieving up to 93% accuracy results. Finally, cross-comparison of the deep learning models with machine learning ones shows that deep learning models can provide at least 3–5% better accuracy results. Full article

Risk assessment is a critical sub-process in information security risk management (ISRM) that is used to identify an organization’s vulnerabilities and threats as well as evaluate current and planned security controls. Therefore, adequate resources and return on investments should be considered when reviewing assets. However, many existing frameworks lack granular guidelines and mostly operate on qualitative human input and feedback, which increases subjective and unreliable judgment within organizations. Consequently, current risk assessment methods require additional time and cost to test all information security controls thoroughly. The principal aim of this study is to critically review the Information Security Control Prioritization (ISCP) models that improve the Information Security Risk Assessment (ISRA) process, by using literature analysis to investigate ISRA’s main problems and challenges. We recommend that designing a streamlined and standardized Information Security Control Prioritization model would greatly reduce the uncertainty, cost, and time associated with the assessment of information security controls, thereby helping organizations prioritize critical controls reliably and more efficiently based on clear and practical guidelines. Full article

The mining industry is the leading supplier of raw materials in modern society. This sector of human activity has experienced a severe crisis due to the energy transition and has been revived in recent years due to the need for critical metals that are essential in the post-coal era. In underground and open pit mining, processes such as extraction, transportation, safety, underground ventilation, waste management, and rehabilitation are of major importance, and their “design” is critical to the economic survival of the mine. All the above processes required to operate a mine are strongly reminiscent of an example of nature’s workman: the ant. The sympatric insect uses the same processes as the ones aforementioned during the creation of its nest. The ants dig to “extract material from the ground”, and they transport this material from the nest‘s site to the waste deposition location. The ants ensure the safety of the underground opening and the proper ventilation needed for them to live there for a long time. This article attempts to identify the relations between all the above processes and sub-processes, and how human mining and ant colony development correlate with each other. Furthermore, we examine how an ant colony has aided in the development of mining technology, and what more humans can learn and adopt from a “miner” that is 66 million years old, in order to improve their processes. Full article

Selective laser sintering (SLS) is one of the most well-regarded additive manufacturing (AM) sub-processes, whose popularity has been increasing among numerous critical and demanding industries due to its capabilities, mainly manufacturing parts with highly complex geometries and desirable mechanical properties, with potential to replace other, more expensive, conventional processes. However, due to its various underlying multi-physics phenomena, the intrinsic complexity of the SLS process often hampers its industrial implementation. Such limitation has motivated academic interest in obtaining better insights into the process to optimize it and attain the required standards. In that regard, the usual experimental optimization methods are time-consuming and expensive and can fail to provide the optimal configurations, leading researchers to resort to computational modeling to better understand the process. The main objective of the present work is to develop a computational model capable of simulating the SLS process for polymeric applications, within an open-source framework, at a particle-length scale to assess the main process parameters’ impact. Following previous developments, virgin and used polymer granules with different viscosities are implemented to better represent the actual process feedstock. The results obtained agree with the available experimental data, leading to a powerful tool to study, in greater detail, the SLS process and its physical parameters and material properties, contributing to its optimization. Full article

Rare earth elements (REEs including Sc, Y) are critical minerals for developing sustainable energy sources. The gradual transition adopted in developed and developing countries to meet energy targets has propelled the need for REEs in addition to critical metals (CMs). The rise in demand which has propelled REEs into the spotlight is driven by the crucial role these REEs play in technologies that aim to reduce our carbon footprint in the atmosphere. Regarding decarbonized technologies in the energy sector, REEs are widely applied for use in NdFeB permanent magnets, which are crucial parts of wind turbines and motors of electric vehicles. The underlying motive behind exploring the energy and carbon footprint caused by REEs production is to provide a more complete context and rationale for REEs usage that is more holistic. Incorporating artificial intelligence (AI)/machine learning (ML) models with empirical approaches aids in flowsheet validation, and thus, it presents a vivid holistic picture. The energy needed for REEs production is linked with the source of REEs. The availability of REEs varies widely across the globe. REEs are either produced from ores with associated gangue or impurities. In contrast, in other scenarios, REEs can be produced from the waste of other mineral deposits or discarded REEs-based products. These variations in the source of feed materials, and the associated grade and mineral associations, vary the process flowsheet for each type of production. Thus, the ability to figure out energy outcomes from various scenarios, and a knowledge of energy requirements for the production and commercialization of multiple opportunities, is needed. However, this type of information concerning REEs production is not readily available as a standardized value for a particular material, according to its source and processing method. The related approach for deciding the energy and carbon footprint for different processing approaches and sources relies on the following three sub-processes: mining, beneficiation, and refining. Some sources require incorporating all three, whereas others need two or one, depending on resource availability. The available resources in the literature tend to focus on the life cycle assessment of REEs, using various sources, and they focus little on the energy footprint. For example, a few researchers have focused on the cumulative energy needed for REE production without making assessments of viability. Thus, this article aims to discuss the energy needs for each process, rather than on a specific flowsheet, to define process viability more effectively regarding energy need, availability, and the related carbon footprint. Full article

Modular production has been recognized as a pivotal approach for enhancing productivity and cost reduction within the industrialized building industry. In the pursuit of further optimization of production processes, the concept of cognitive modular production (CMP) has been proposed, aiming to integrate digital twins (DTs), artificial intelligence (AI), and Internet of Things (IoT) technologies into modular production systems. This fusion would imbue these systems with perception and decision-making capabilities, enabling autonomous operations. However, the efficacy of this approach critically hinges upon the ability to comprehend the production process and its variations, as well as the utilization of IoT and cognitive functionalities. Knowledge graphs (KGs) represent a type of graph database that organizes data into interconnected nodes (entities) and edges (relationships), thereby providing a visual and intuitive representation of intricate systems. This study seeks to investigate the potential fusion of KGs into CMP to bolster decision-making processes on the production line. Empirical data were collected through a computerized self-administered questionnaire (CSAQ) survey, with a specific emphasis on exploring the potential benefits of incorporating KGs into CMP. The quantitative analysis findings underscore the effectiveness of integrating KGs into CMP, particularly through the utilization of visual representations that depict the relationships between diverse components and subprocesses within a virtual environment. This fusion facilitates the real-time monitoring and control of the physical production process. By harnessing the power of KGs, CMP can attain a comprehensive understanding of the manufacturing process, thereby supporting interoperability and decision-making capabilities within modular production systems in the industrialized building industry. Full article

The rapid development of coastal economies has aggravated the problem of pollution in the coastal water bodies of various countries. Numerous incidents of massive-scale marine life deaths have been reported because of the excessive discharge of industrial and agricultural wastewater. To investigate the diffusion of typical pollutants after discharge, in this study, a multi-process fusion simulation analysis model of pollutants under the action of ocean currents was established based on the concentration analysis method. Furthermore, key technologies involved, such as the parameter value, data selection, and visualization, were investigated. The iterative analysis and programming realization of three independent sub-processes, such as pollutant diffusion and transport, and the drift path and concentration distribution of pollutants after their discharge into the sea, were visualized. The case study revealed that the increase in the concentration of pollutants in the ocean was affected by the diffusion sub-process, and the transport sub-process plays a critical role in the long-distance transport of pollutants. The proposed method can provide technical support for marine environmental risk assessment and dynamic tracking of marine pollutants. Full article

The Stroop test represents a widely used task in basic and clinical research for approaching the cognitive system functioning in humans. However, a clear overview of the neurophysiological signatures associated with the different sub-domains of this task remains controversial. In the present study, we leveraged the EEG technique to explore the modulation of specific post-stimulus ERPs components during the Stroop test. Critically, to better disentangle the contribution of facilitation (i.e., faster color identification times for color-congruent Stroop words) and interference (i.e., longer color identification times for color-incongruent Stroop words) processes prompted by the Stroop test, we delivered congruent and incongruent trials in two separate experimental blocks, each including the respective neutral condition. Thanks to this methodological manipulation, we were able to clearly dissociate the two sub-processes. Electrophysiological results suggest specific markers of brain activity for the facilitation and the interference effects. Indeed, distinctive Stroop-related ERPs (i.e., the P3, the N450, and the LPC) were differently modulated in the two sub-processes. Collectively, we provide evidence of selected brain activities involved in the reactive stage of processing associated with the Stroop effect. Full article

In the attempt to process lower-grade ores, mineral flotation has taken centre stage as the preferred recovery route. However, in many instances, the froth product does not have a high grade due to the entrainment of gangue minerals. Industry has solved this challenge by introducing froth washing mechanisms. Clean wash water is introduced into or on top of the froth to reduce the amount of entrained gangue in the final concentrate. This article reviews froth-washing systems in detail and highlights the advantages and disadvantages of each wash-water delivery mechanism. Comments on industrial uptake are provided. The indications are that froth washing improves the grade of the concentrate and influences froth stability and mobility. Other researchers have reported an improvement in recovery—especially of coarse particles—with wash water being added, while others have reported a reduction in recovery, especially with composite particles. Froth washing is generally applied in mechanical flotation cells by washing at the lip. In column flotation cells and Jameson cells, wash water is added to the entire froth surface. The literature also indicates that the wash-water rate, wash-water quality, type of wash-water delivery/ distribution mechanism and the area covered by wash water are critical parameters that dictate the efficacy of the washing system. Further research is necessary on the impact of wash-water quality on the froth phase sub-processes including froth rheology. Full article