Abstract Metal additive manufacturing involves manufacturing techniques that add material to produce metallic components, typically layer by layer. The substantial growth in this technology is partly driven by its opportunity for commercial and performance benefits in the aerospace industry. The fundamental opportunities for metal additive manufacturing in aerospace applications include: significant cost and lead-time reductions, novel materials and unique design solutions, mass reduction of components through highly efficient and lightweight designs, and consolidation of multiple components for performance enhancement or risk management, e.g. through internal cooling features in thermally loaded components or by eliminating traditional joining processes. These opportunities are being commercially applied in a range of high-profile aerospace applications including liquid-fuel rocket engines, propellant tanks, satellite components, heat exchangers, turbomachinery, valves, and sustainment of legacy systems. This paper provides a comprehensive review of metal additive manufacturing in the aerospace industry (from industrial/popular as well as technical literature). This provides a current state of the art, while also summarizing the primary application scenarios and the associated commercial and technical benefits of additive manufacturing in these applications. Based on these observations, challenges and potential opportunities are highlighted for metal additive manufacturing for each application scenario.
Meera Prajapati, Parikshya Gurung, Aashirbad Pokharel
et al.
ABSTRACT Background African swine fever (ASF) is a contagious fatal viral disease of pigs caused by ASF virus (ASFV). Since its introduction into Nepal in 2022, ASF has caused major losses to pig farmers. However, information on the seroprevalence and associated risk factors in Nepal remains limited, and this study represents the first systematic investigation of ASFV seroprevalence and farm management practices in the country. Objectives To determine the seroprevalence of ASF in domestic pigs and slaughterhouse in Nepal and to identify farm management practices that contribute to disease transmission. Materials and Methods A cross‐sectional study was conducted across pig farms in Bagmati Province. A total of 324 serum samples were collected and tested for ASFV antibodies using competitive ELISA. Additionally, 43 farmers were interviewed to assess management practices and biosecurity measures. Data were analysed using Chi‐square and Fisher's exact tests. Results Of the 324 samples screened, seven tested positive, with an overall seroprevalence of 2.16%. Seropositivity was detected in multiple districts, but no significant differences were observed by location, sample source, sex, or age group (p > 0.05). The survey revealed poor biosecurity practices: only 6.9% of farmers used protective clothing, 27.9% used foot dips, and 62.8% did not quarantine new animals. Swill feeding was common (34.9%), while 39.5% of farmers used both swill and commercial feed. Improper carcass disposal and sale of sick or dead pigs were also reported. Conclusions Detection of ASFV antibodies in slaughtered and farm pigs indicates ongoing circulation of the virus in Nepal. Combined with poor farm‐level biosecurity, this underscores the urgent need for preventive measures, including strengthened surveillance, farmer awareness, and enforcement of swill‐feeding bans, to safeguard Nepal's pig industry.
Quantitative assessment was carried out on the sticking risk in the drilling and completion stage to fill the gap in this field， and provide scientific basis and technical support for the risk control of sticking in drilling operations. Based on the analysis of causes， inducing factors and common types of sticking， a fault tree model was constructed and mapped to a Bayesian network model. Using expert knowledge and real data from an oil field in Southwest Sichuan Basin， fuzzy analytic hierarchy process （FAHP） was used to quantify the risk of sticking， and the main risk factors affecting sticking were identified by sensitivity analysis. The research results show that human error is the main cause of sticking， with its contribution rate significantly exceeding other factors. Sensitivity analysis identifies key causal factors such as improper drilling parameters， inadequate connections and long rig downtime. The constructed model can dynamically update the risk probability， thereby effectively supporting risk warning and decision-making. The research conclusions not only provide theoretical support for the oil and gas industry， but also provide practical guidance for risk management in drilling operations， and have important engineering application value.
Chemical engineering, Petroleum refining. Petroleum products
In the field of contemporary mineral resource development, “safety, environmental friendliness, intelligence, and efficiency” has become the core guiding principle and technological development paradigm for global open-pit mining. This concept stems from a profound reflection on the history of mining engineering development. In a systematic review of historical mining accident cases, the fundamental mechanism of disaster occurrence was determined to be closely related to an ambiguous cognition of geological conditions and the uncertainty of disaster threat assessment. These two cognitive deficiencies constitute the main limiting factors in mine production safety. This study considers the current production practice of open-pit mines, systematically sorts the logical characteristics of hidden disaster-causing factors, and focuses on four key dimensions: geological structural anomalies, hydrogeological conditions, distribution of adverse geological bodies, and fire hazards, thereby deeply analyzing the mechanisms that impact the safety of open-pit mining. Through a combination of historical retrospective and bibliometric analysis, the development trajectory of mining geological exploration technology is divided into four distinct stages of technological evolution: early empirical, instrumental, digital, and the current intelligent detection stage. This presents a complete paradigm shift in hidden disaster factor detection technology from qualitative to quantitative, from single to comprehensive, and from artificial to intelligent. In terms of technical system research, this study is based on systems engineering theory and constructs a multilevel classification framework for mining exploration technology, dividing the existing detection technologies into three levels: basic, professional, and intelligent. To detect hidden disasters in open-pit mines, a systematic evaluation and comparative study of existing methods was conducted considering four aspects: technical performance characteristics (including technical indicators such as resolution and detection depth), research progress (covering academic dimensions such as theoretical innovation and method improvement), engineering application effectiveness (involving practical indicators such as applicable conditions and operational efficiency), and future development potential (including forward-looking evaluations such as technology integration and intelligent upgrades). The research further reveals the multidimensional challenges of current detection methods from three perspectives: technological bottlenecks, engineering application barriers, and management practice pain points, including but not limited to limited detection accuracy, difficulties in multisource data collaboration, economic cost constraints, monitoring cycle limitations, insufficient interpretability of mechanisms, lack of data integrity, and fluctuations in result reliability. For future technological development, this study proposes three innovative breakthrough paths. First, a theoretical and technical system for collaborative inversion of multiple physical fields should be constructed to achieve deep integration of geological information. Second, paradigm innovation of intelligent perception and early warning technology is promoted to establish a dynamic risk assessment mechanism. Finally, the collaborative development framework between the standard system and green technology should be improved to form a sustainable technological ecosystem. These multimodal fusion and intelligent evolution strategies are expected to break through existing technological bottlenecks, provide strong technical support and theoretical guidance for the high-quality development of open-pit mines in China, and ultimately achieve coordinated and sustainable development of economic benefits, safety production, and ecological environment protection in mineral resource development. This research not only has important theoretical innovation value but also provides a systematic technical solution and development roadmap for safety in mining production practices, which has important practical significance for promoting technological progress and industrial upgrading in China’s mining industry.
In the continuous development of the green building industry, construction safety management faces increasing challenges, particularly in safety and environmental protection, which requires precise evaluation and control. Therefore, this study proposes a coupling analysis method for green building construction based on particle swarm optimisation neural network. The purpose is to strengthen safety risk management in green building construction by combining particle swarm optimisation with neural network algorithms. A risk coupling performance comparison was conducted between traditional and research algorithms. In the results, when using a back propagation neural network for prediction, the actual construction risk rate increased from 0.235 to 0.431. the optimised algorithm showed an increase from 0.168 to 0.453, and the prediction error improving from −0.352 to 0.014, demonstrating a high degree of adaptability and accuracy to actual changes. Compared with traditional methods, the prediction error of this algorithm is significantly reduced, and the data fitting accuracy is improved to 0.99809, indicate its effectiveness in predicting construction safety risks. The research results not only contribute to improving the efficiency of safety management during the construction process, but also provide technical support for risk prediction models in the future green building field.
The global port and maritime industry is experiencing a new paradigm shift known as the artificial intelligence transformation (AX). Thus, domestic container-terminal companies should focus beyond mere automation to a paradigm shift in AI that encompasses operational strategy, organizational structure, system, and human resource management. This study proposes a resilience-based AX strategy and implementation system that allows domestic container-terminal companies to proactively respond to the upcoming changes in the global supply chain, thus securing sustainable competitiveness. In particular, we aim to design an AI-based governance model to establish a trust-based logistics supply chain (trust value chain). As a research method, the core risk factors of AX processes were scientifically identified via text-mining and fault-tree analysis, and a step-by-step execution strategy was established by applying a backcasting technique based on scenario planning. Additionally, by integrating social control theory with new governance theory, we designed a flexible, adaptable, and resilience-oriented AI governance system. The results of this study suggest that the AI paradigm shift should be promoted by enhancing the risk resilience, trust, and recovery of organizations. By suggesting AX strategies and policy as well as institutional improvement directions that embed resilience to secure the sustainable competitiveness of AI-based smart ports in Korea, this study serves as a basis for establishing strategies for the domestic container-terminal industry and for constructing a global leading model.
Purpose of the article – Abnormal cargo will create abnormal problems and those require exceptional logistics solutions. This study delves into the complexities and challenges of transporting project cargo, explicitly focusing on items with extraordinary dimensions, weights, and shapes. Despite its small volume in global cargo flow, project cargo presents significant operational risks and contributions, particularly in the logistics sector. The research concentrates on the Balkan Peninsula, aiming to unravel the critical factors influencing project cargo transport and highlighting the challenges faced by industry experts.
Research methodology – Employing the Delphi method, initially developed for military applications and now prevalent in project management, the research seeks expert consensus on critical elements affecting project cargo logistics in the Balkans. This method facilitates the gathering and analysis of expert opinions to identify and address the core factors impacting project cargo transport.
Findings – The study uncovers various critical factors and challenges in project cargo logistics within the Balkan region. It provides insights into risk minimization, deviation control, and effective achievement of logistical objectives.
Practical implications – The research offers valuable perspectives for reducing risks and optimizing operations in project cargo transport. It also opens avenues for further research and development in the project logistics sector in the Balkans, contributing to enhanced efficiency and effectiveness.
Originality/Value – This research contributes uniquely by focusing on the lesser-explored area of project cargo transport in the Balkans. Employing the Delphi method brings forward expert insights, thus enriching the academic and practical understanding of this specialized logistics sector.
Agus Suprijanto, Yumin Tan, Syed Mohammad Masum
et al.
NDVI (Normalized Difference Vegetation Index) is an essential tool for climate and environmental monitoring, but it is often contaminated by clouds and unfavorable atmospheric conditions. In this study, we designed a new, simple yet effective method for reconstructing data, which we call the Hybrid Filter. This study is the first to reconstruct missing NDVI time series data in cloud-prone areas by combining several data reconstruction techniques with a forecasting technique based on Exponential Moving Average (EMA). The study was conducted in Cilegon City and Batu City using NDVI time series data from the Landsat 8 satellite for the period 2014-2022. Experimental results show that the hybrid filter significantly outperforms the Spatio-Temporal Savitzky-Golay (STSG) filter, Gap Filling Savitzky-Golay (GFSG) filter, Savitzky-Golay (SG) filter, and Whittaker filter. The hybrid filter is capable of recovering missing data with high accuracy, stability, noise reduction, and maintaining the temporal integrity of NDVI data even under conditions of large data gaps and high missing data rates, making it a reliable solution for NDVI analysis in cloud-prone areas. These findings affirm the superiority of the hybrid filter in producing accurate and reliable NDVI data for vegetation and environmental monitoring.
In a transition economy, the issues of reforming the transport system and overcoming the exacerbating problems of industry management become relevant. It seems that technological progress and the digital transformation of business processes should simplify the algorithm of action in management matters. However, as practice shows, during this period new conditions for the functioning of the industry arise (the emergence of new modes of transport or their modification) and elements of the transport system undergo changes. A more serious problem, according to the authors, is the unpredictability of foreign partners’ behaviour in relation to exclusive rights to software (SW) used by companies in the transport and logistics sector. The purpose of the study is to develop a model for assessing the cost of the risk of a company's sudden transition to domestic software and test it on the materials of companies in the transport and logistics industry of Altai Krai. The study was conducted in the context of trends in the digital transformation of the transport system, identifying factors and types of risk, and assessing their impact on the information technology landscape (IT landscape) of transport companies in the context of sanctions. The results confirm the need to prepare industry participants homegrown software and to minimise potential risks. A sudden software replacement is more likely to cause serious financial loss to organisation than planned actions that do not disrupt business processes and communications with partners and government agencies. The practical significance of the proposed risk assessment model lies in the possibility of its application when making management decisions on the digitalisation of business processes and the transition to domestic software, regardless of the industry and size of the company.
Natal'ya Solovova, Olga Kalmykova, Marina Belinskaya
The issues of improving the process of assessing and managing personnel risks are relevant for many modern industrial organizations due to an increase in the level of social tension and destructive conflict in the organizational environment. The article deals with the problem of improving the methods for assessing and managing the personnel risks of an industrial organization in modern socio-economic conditions. The authors of the article adhere to the hypothesis that the personnel risk management strategy is a necessary element of the personnel policy of a modern organization. The purpose of the study is to develop practical recommendations for the implementation of methods for identifying and assessing personnel risks arising in the course of the labor activity of personnel of industrial organizations. The object of the study is the methods for assessing the personnel risks of an industrial organization. As an empirical material, the analysis of scientific publications of Russian scientists on the research problem was carried out. The article analyzes practical tasks, the solution of which is aimed at improving the personnel risk management system on the example of industrial organizations in the oil and gas industry. The study (2023) used empirical methods: observation, survey; identification and assessment of the risks of the personnel management system and the risks of personnel behavior; expert survey. The assessment of personnel risks was carried out on the basis of the method of expert assessments of employees of an industrial organization in the oil and gas industry. The heads of the personnel management service and security officers of the industrial organization acted as experts. Based on the method of expert assessments, the significance and probability of the occurrence of risks are assessed, a profile of personnel risks is built, and the most significant personnel risks that create threats to the personnel security of an industrial organization are identified. Practical recommendations for the assessment and management of personnel risks of an industrial organization are presented.
Elaine Q. Borazon, Michelle D.L. De Los Santos, Meng-Tsung Lee
et al.
This study aims to understand the crises experienced by family-owned accommodation businesses in small island contexts and how resilience is built alongside the development of their dynamic capabilities (sensing, seizing, and transforming) as they navigate through crises. The study uses a qualitative method and focuses on the experiences of tourism businesses operating in the islands of Boracay, Negros, and Siargao, which are among the top island destinations in the Philippines. Semi-structured interviews with 18 participants were conducted through 1-hour online video calls, phone calls, and written interviews. Crises that affect tourism businesses are mostly due to external factors. Small islands are at high risk of natural hazards, but businesses do not consider typhoons and earthquakes as crises per se since they frequently experience these hazards. Family businesses have distinct strategies, such as knowledge transfer, maintenance of stable financial resources, infrastructure development, employee training, better marketing strategies, and a focus on sustainability to enhance their dynamic capabilities and build their resilience, thus making them more adaptive to future crises. However, government support for the local tourism industry is still needed to ensure a sustainable tourism industry. Dynamic capabilities and resilience are often linked with each other yet there is limited knowledge on how resilience is built specifically in the context of family-owned businesses in small island context. This study addresses this gap in the literature by using dynamic capabilities as a framework to understand resilience development. Resilience and dynamic capabilities are then adaptive strategies in crisis management.
The present study proposes a novel fatigue life prediction considering the temperature load, which may be neglected in the traditional assessment of suspension bridge steel deck welds under dynamic vehicle load. Vehicle fatigue, pavement temperature, and temperature gradient models are developed based on the test data from the weight-in-motion system, U-rib welds, pavement temperature, and environment temperature. The U-rib-to-deck and U-rib-to-U-rib welds fatigue stresses are obtained considering both vehicle and temperature loads with transient analysis method in ANSYS package. Then, the temperature gradient fatigue stress spectra are calculated. After that, the fatigue life of two weld types is predicted considering the coupled vehicle-temperature loads. The results indicate that the fatigue stress varies linearly with the temperature of the asphalt concrete. The effect of the temperature on the weld’s fatigue life decreases as the distance increases between the welds and the pavement. The dynamic vehicle load results in a higher fatigue stress than the temperature gradient, indicating that the vehicle load contributes mainly to the bridge’s fatigue damage. Finally, it is calculated that the fatigue damage of two weld types is magnified 5.06 and 1.50 times when the temperature effect is considered after 100-year service of Nanxi Yangtze River Bridge.
This paper focuses on the issue of green finance credit risk measurement, taking China's new energy vehicle listed companies as a sample, taking into account the actual situation of China's financial market, using a modified KMV model to estimate the default distance of selected companies, and comparing with the traditional industries in these three years. The results show that the average default distance of 30 new energy automobile companies is larger than that of 30 traditional automobile manufacturing companies, and the default probability is smaller. As the credit loan risk for the new-energy automobile industry is lower, commercial banks are encouraged to carry out green credit business, and policy and subsidy support related to the new-energy industry shall be given at the same time.
Abstract: Based on theoretical analysis, we select the relevant data of 30 provinces (autonomous regions and municipalities) in China from 2013 to 2019, and empirically test the impact of financial technology on the development of big data industry and its mechanism using dynamic panel data model, mediating effect test method and threshold effect model. The benchmark regression results show that the regression coefficient of financial technology to big data industry is significantly positive at the significance level of 10%, indicating that the financial technology can directly promote the development of big data industry. The regression coefficient of the dynamic lag term of big data industry is negative, but not significant, indicating that the dynamic lag effect of big data industry is not obvious. The mediating effect test results show that the financial technology can indirectly promote the development of big data industry by alleviating the big data enterprise financing constraints. The big data enterprise financing constraints have a partial mediating effect, and the mediating effect account for 27.63% of the total effect. In addition, the threshold effect test results show that the direct effect of financial technology on big data industry is significantly enhanced when the development level of financial technology is higher than 5.8790, that is, there is a positive threshold effect of financial technology directly promoting the development of big data industry. However, the indirect effect of financial technology on big data industry is relatively weak when the development level of financial technology is higher than 5.4328, that is, financial technology indirectly promotes the development of big data industry by alleviating the big data enterprise financing constraints, which has a negative threshold effect.
Engineering (General). Civil engineering (General), Risk in industry. Risk management