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This paper addresses the question whether energy consumption variables and the disaggregation of output matter in the context of efficiency analysis of container terminals. While it is obvious that the energy consumption of refrigerated cargo is higher than the energy consumption of non-refrigerated cargo, this work investigates whether those differences show in an overall efficiency analysis of terminals. This would point to a potentially important input for efficiency measures, to be considered in future productivity and efficiency analysis of terminals. Starting with a discussion on theoretical concepts and variable selection for measuring the energy dimension of terminal efficiency, this is the first paper that applies data envelopment analysis (DEA) comparing results with and without energy consumption, as well as differentiating productive outputs (dry and reefer container handling). The results reveal how the output disaggregation leads to substantially different efficiency scores and are a first step to show the relevance of output disaggregation and the inclusion of the energy variables as inputs in container terminal efficiency studies. [ABSTRACT FROM AUTHOR]

Operational efficiency is crucial in the container terminal industry to reduce costs, increase throughput, enhance customer satisfaction, optimize the supply chain, improve safety, minimize environmental impact, and ensure adaptability and resilience in a dynamic and competitive market. The article aims to estimate operational efficiency for CTOs by the Slacks-Based Measures (SBM) model when considering CO2 emissions and work accidents as undesirable outputs. In doing so, this paper adopted the cluster analysis to select homogeneous CTOs acting as Decision-making Units (DMUs). CTOs in the Cai Mep-Thi Vai port (the CMTV case) were then empirically studied to verify the validity of the suggested model. The results could have real-world applications for CTOs looking for managerial actions to boost operational efficiency and maintain high-quality growth. Additionally, the research model may provide an analytical reference for relevant port management applications. In particular, this study offers CTOs a scientific framework for evaluating the operational efficiency of CTOs located in a port. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal Maritime Policy & Management, discussing the issue of air emissions from the shipping industry and the need for green port technologies. The article highlights the significant amount of marine fuel consumed by shipping activities and the resulting emissions of sulfur dioxide, nitrogen oxide, carbon dioxide, and particulate matter. It emphasizes that most of these emissions occur within 400 km of the coastline, causing severe environmental problems. The editorial also introduces a special issue on "Green Port and Maritime Shipping," which includes research papers on topics such as internal truck renewal, berth and yard allocation, stocks allocation, environmental management systems, marine greenhouse gas emission control, container terminal competitiveness assessment, multi-criteria decision support frameworks, and maritime transportation research capability analysis. The article concludes by emphasizing the importance of improving the environmental performance of ports for their competitiveness and the need for collaborative actions to control marine greenhouse gas emissions. [Extracted from the article]

Deep neural network is an important computer operating system in China. It has made great breakthroughs in target recognition, image classification and other fields. It plays a key role in multi-system integration and intelligent manufacturing industry, such as training and testing. The purpose of this paper is to study the multi-system fusion of deep neural network and its application in intelligent manufacturing industry. By setting up experiments, the multi-system fusion of neural network is carried out, combining with big data and artificial intelligence to verify the efficient operation of neural network in multi-system fusion. Using the methods of mathematical analysis and big data fitting, the collected data are classified, the experimental data are collected and then analyzed. In this paper, the reliability of this method is verified by research; the application of multi-system fusion based on deep neural network in intelligent manufacturing industry can effectively integrate all systems, improve the comprehensive working efficiency of each system by about 20% and improve the multi-system fusion of deep neural network and its application in intelligent manufacturing industry by about 15%. Thus, as the terminal of the system, the deep neural network plays the leading role of multiple systems. It can effectively integrate the various systems and improve the comprehensive work efficiency of each system, which has guiding significance for the development of industry. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore how a start-up entering maritime logistics networks (MLNs) in the container shipping industry integrates resources underlying value cocreation patterns in these networks. Design/methodology/approach: The paper is based on a single case study of a technological start-up, providing tracking, tracing and other information services to MLN members using internet-based software. An interorganizational theory perspective informs the case study to unveil the resource integration for value cocreation in the network. Findings: The start-up holds multiple resource interaction roles and the start-up's involvement enables the creation of new knowledge resources, which facilitate new revenue streams and manage resource dependencies. Hence, the findings indicate that the start-up changes value cocreation patterns in the network by reconfiguring and integrating existing resources so that the service is customized for various customers, including shippers and freight forwarders. Practical implications: The results provide insights about how technological start-ups can unlock resources within MLNs. Originality/value: The study extends previous studies on resource roles in business networks and shows how start-ups can perform multiple roles simultaneously within these networks. In addition, the study contributes to the literature by studying information and knowledge as resources configured in different ways in a unique network setting. [ABSTRACT FROM AUTHOR]

This research is focused on the analysis of a container terminal common in industrial zones in urban areas, called the logistic services container terminal (LSCT), defined as a small to medium size inland terminal with no intermodal facilities. The main function of an LSCT is to provide services to a hinterland market. The material handling equipment used is limited to reach stacker cranes and front loaders, and ground transportation in and out is performed using trucks exclusively. Moreover, there are many operations related to servicing customers in special service areas within the yard. In this research, we explicitly recognize the limitations and features associated with the operations within LSCT yards, which means that the problems and solutions must be conceived and addressed in a very unique way, considering that services have to be coordinated with the strategies proposed for managing the movement of containers in and out. This paper characterizes the different aspects present in the treatment of an LSCT and proposes an adapted version of three known greedy decision rules for determining the location for an arriving container. We evaluate the performances of these rules using a discrete-event simulator: the min-max rule outperforms the other tested rules. These rules are also extended to consider the cost of moving relocating containers. The results show that using cost-aware rules while increasing the expected number of relocations reduces the total expected cost significantly. [ABSTRACT FROM AUTHOR]

Cyber-manufacturing Systems (CMS) have been growing in popularity, transitioning from conventional manufacturing to an innovative paradigm that emphasizes innovation, automation, better customer service, and intelligent systems. A new manufacturing model can improve efficiency and productivity, and provide better customer service and response times. In addition, it may revolutionize the way products are produced, from design to completion. Therefore, it is likely that this newmanufacturing model will become increasingly popular. By building new technologies on top of existing CMS, these systems will ensure that data exchange and integration between decentralized systems are reliable and secure. Recently published case studies from industry and the literature support this claim; some challenges remain to be overcome. In general, the use of CMS can revolutionize the manufacturing industry. This study comprehensively analyzes these systems and their potential applications and implications. An overview of the field is then given and various aspects of CMS are also explored with more details. A taxonomy of the most common and current approaches to CMS is presented, including networked cyber-manufacturing systems, distributed cyber-manufacturing systems, cloud-based cyber-manufacturing systems, and cyber-physical systems (CPS). Furthermore, our survey identifies several popular open-source software and datasets and discusses how these resources can reduce barriers to CMS research. In addition, we identify several important issues and research opportunities associated with CMS, including better integration between hardware and software, improved security and privacy protocols, communication protocols, and improved data management systems. In summary, this paper presents a comprehensive overview of current technology and valuable insights are provided for the potential impact of CMS on society and industry. [ABSTRACT FROM AUTHOR]

Automation in the construction industry has become more appealing in recent years. Although the industry fosters material mixing and structural application, automating quality control is not investigated broadly. The industry relies on manual inspection, leading to inaccuracy and lower productivity. This research focuses on the quality check of bolted steel members using automating ESP32 camera to detect missing bolts. Earlier studies focused on improving the quality of tightening the bolts. However, the major problem of missing bolts has not been extensively addressed. Inadequate bolting of steel members causes a considerable reduction in mechanical strength, and it may cause a structure to fail. Hence, this paper aims to detect missing bolts in assembling steel structures. The study was conducted by developing a system that utilizes an ESP32 camera module to capture the steel members in real-time. Captured video is processed in Visual Studio (C++ language), another approach carried out in the study is using a faster region-based convolutional neural network (Faster R-CNN) where it extracts the area of interest, the bolts, and holes in the steel members with the assistance of an image dataset and training. The trained model can be used to detect bolts and holes. The results showed that the developed system is reliable and can alter the user to any missing bolts, having TensorFlow object detection with the Faster R-CNN algorithm successfully provided desired results with 95% precision. This technique increases the efficiency of quality monitoring. Consequently, the steel manufacturing industry can rely on smart cameras to monitor the quality control of steel frames, leading to productive output. This also saves the workers time from performing the tedious task of inspecting every steel member and assists in maintaining the quality of the assembled steel structures. [ABSTRACT FROM AUTHOR]

Production control in the manufacturing industry involves complex circumstances and high demand for timeliness. Unlike traditional production control methods, the approach that integrates machine learning and industrial big data can enable manufacturing industries to dynamically adapt to the changing environment and respond in a timely manner to market changes due to production optimisation and improve economic benefits. In order to explore the relationship between production system optimisation and lean strategic planning based on machine learning and big data, the paper conducts an exploratory case analysis based on Suzhou GCL Photovoltaic Technology, a successful company in the photovoltaic industry in China. We sort and investigate the first-hand interview data and second-hand news and video data, and then use the qualitative research method. Based on the analysis and observation, we find that machine learning has a positive impact on quality management. Data, information, knowledge, intelligence collectively impact the performance of intelligence production systems. Our research provides valuable insights for practitioners to effectively accelerate the transformation to intelligent manufacturing. [ABSTRACT FROM AUTHOR]

The business world is becoming increasingly complex, disrupted, and uncertain. As Industry 4.0 (I4.0) takes shape, to endure this change and focus on growth, industries must learn to embrace change and move quickly; this is particularly so for the automotive industry, which is a major sector of any economy. Agility is needed to embrace this change and promote sustainability across every industry. To face these contingencies, it is essential to identify and understand the strategies that can aid industries in sustaining real‐time scenarios. In this paper, we aim to assess the agility indicators that bring resilience to the inclusion of I4.0 and sustainability in the automotive sector. The unexpected changes in the environment related to sustainability and technological disruptions of I4.0 require the identification of sustainable agile mitigation strategies to manage such situations. The fuzzy Delphi methodology assesses agility indicators based on a questionnaire for prioritization. Further, the fuzzy technique for order performance by similarity to ideal solution (TOPSIS) is utilized to prioritize these agility indicators with the agility strategies that manufacturing industries can deploy to enhance the sustainability of their operations. The fuzzy extension to the methodologies helps in incorporating vagueness and ambiguity in the decision‐making process. A fuzzy approach to prioritizing agile strategies as the industries are changing is helpful. The decision makers have only a few clear ideas about how best to integrate them. Among the agile strategies, "resilience," "technological capabilities," and "customer‐centric innovation" are the top strategies. By executing the strategies suggested in the study and understanding the agility indicators, practitioners can ensure resilience in the work systems and minimize the impact of the disruptions caused by the industrial revolution. Thus, this research has been underpinned by the organizational change theory and discusses all mitigation strategies concerning the rapid changes organizations are facing with the fourth industrial revolution. [ABSTRACT FROM AUTHOR]

Purpose: Support structures are often needed in additive manufacturing (AM) to print overhangs. However, they are the extra materials that must be removed afterwards. When the supports have many contacts to the model or are even enclosed inside some concavities, removing them is very challenging and has a risk of damaging the part. Therefore, the purpose of this paper is to develop a new type of tree-support, named Escaping Tree-Support (ET-Sup), which tries to build all the supports onto the build plate to minimize the number of contact points. Design/methodology/approach: The methodology is to first classify the support points into three categories: clear, obstructed and enclosed. A clear point has nothing between it and the build plate; an obstructed point is not clear, but there exists a path for it to reach the build plate; and an enclosed point has no way to reach the build plate. With this classification, the path for the obstructed points to come clear can be found through linking them to the clear points. All the operations are performed efficiently with the help of a ray representation. Findings: The method is tested on different overhang features, including a lattice ball and a mushroom shape with a concave cap. All the supports generated for the examples can find their way to the build plate, which looks like they are escaping from the model. The computation time is around one second for these cases. Originality/value: This is the first time truly realizing this "escaping" property in the generation of tree-like support structures. With this ET-Sup, it is expected that the AM industries can reduce the manufacturing lead time and save much labor work in post-processing. [ABSTRACT FROM AUTHOR]

The paper describes the glass manufacturing process, the process areas and their energy intensity. The implementation of an energy management system, its operation and participation in the decision-making chain as well as benefits from implementation are also described. The use of regression as a numerical technique for determining energy consumption is presented with reference to the historical experience of the glassworks and its developed trends on the example of gas consumption in the melting process in the glass furnace. The paper compared the deviation of actual energy consumption in the glass melting process to that calculated from linear regression variables for data before and after the implementation of the energy management system. The study confirmed the sensibility of implementing the described managing system. Constant observation and response to factors affecting the running process allows for its stabilization and optimization. [ABSTRACT FROM AUTHOR]

In recent years, the stability of supply chains has been undermined due to many disruptions. Although it is normal for the disruptions in supply chains to occur periodically, they have great impact on the rise of the costs and they create strong imbalances in business. For years, most world trade has been performed by maritime transport due its cost-effectiveness. The COVID-19 pandemic has disrupted the global supply chain like nothing before, and the Russian invasion of Ukraine has only deepened the crisis that is affecting the global supply chain. The focus of this paper has been placed on volatile freight rates in the maritime container industry due to the COVID-19 pandemic and the Russian invasion of Ukraine and the consequences of these crises on the changes of the prices of consumer goods in the European Union. [ABSTRACT FROM AUTHOR]

Due to imbalances in the global transport of containerised goods, liner shipping companies go to great lengths to match the regional supply and demand for empty containers by transporting equipment from surplus to deficit regions. Making accurate forecasts of regional empty container availability could support liner companies and other involved actors by making better relocation decisions, thus avoiding unnecessary transport costs of empty equipment. Previously proposed container availability prediction models are limited to the application in individual regions and typically characterized by a high degree of temporal aggregation. Against this background, this paper introduces two novel approaches based on machine learning and probabilistic techniques to predict the future weekly availability of empty containers for more than 280 locations worldwide. The machine learning and probabilistic prediction models are built by analysing a unique data set of more than 100 million events from past container journeys. These events represent different stages during the transport process of a container. Both models use a two-step forecast logic. First, the expected future location of a container is predicted. Second, the expected timestamp for arriving at that location is estimated. The machine learning model uses artificial neural networks and mixture density networks to forecast the movements of containers. The models are quantitatively assessed and compared to the actual availability of containers and two more conventional forecasting approaches. The results indicate that the probabilistic prediction approach can keep up with conventional approaches while the neural network approach significantly outperforms the other approaches concerning every evaluation metric. [ABSTRACT FROM AUTHOR]

Statistical techniques have a beneficial effect on measuring process variability, analyzing the variability concerning product requirements, and eliminating the variability in product manufacturing. Process capability indices (PCIs) are not only easy to understand but also able to be directly employed by the manufacturing industry. The process yield index offers accurate measurement of the process yield, and it is a function of two unilateral six sigma quality indices. This paper initiates to develop the confidence intervals of the process yield index by using joint confidence regions of two unilateral six sigma quality indices for all quality characteristics of a product. Then integrate these joint confidence regions to find the confidence intervals of the product yield index. All manufacturing industries can use these confidence intervals to make statistical inferences to assess whether the process capability of the product and all quality characteristics has reached the required level, and to grasp the opportunities for improvement. An illustrated example on driver integrated circuit of micro hard disk is provided. [ABSTRACT FROM AUTHOR]

The ITER WAVS (visible and infrared Wide Angle Viewing System) provides measurements of surface temperature for the plasma facing components by infrared thermography. One of its most important roles of WAVS is to protect the plasma facing components from damage. It also takes images of the plasma visible spectral range emission. The WAVS diagnostic consists of 15 views distributed in four equatorial ports (3, 9, 12 and 17), and it will contribute to Machine protection, Basic control and Physics analysis. The final design of the WAVS system in the equatorial port 12 (EP12) includes three lines of sight (LoS) that have to installed for ITER first plasma. This paper is focussed on the final design for the opto-mechanical ex-vessel components in equatorial port 12 (EP12). The most important function of the ex-vessel optics is to relay the image produced by the Port Plug optics at the vacuum window, up to the detectors placed at the backend of the optical chain in the Port Cell, while keeping the required performance. The image relays through the Interspace, Bioshield and Port Cell covers around 10 m in length before reaching the cameras. The ex-vessel optics is arranged in several optical modules, assembled on the EP12 support structures, i.e. Interspace Support Structure (ISS) and Port Cell Support Structure (PCSS). A detailed description of the opto-mechanical design that supports the optical modules, including the alignment capabilities, will be presented in the paper. Moreover, the assembly and alignment strategies planned for the ex-vessel subsystems, taking into account all constraints imposed on the site, are also described in the paper. [ABSTRACT FROM AUTHOR]

This paper explores the cultivation paths of innovation ecosystems in the digital age by constructing a theoretical framework consisting of innovators, elements of innovation, platforms and digital governance. Four cities in China, Shenzhen, Hangzhou, Xuzhou and Weifang, were selected for in-depth case studies. Findings revealed that: (1) regions with 'head enterprises' and a strong industrial base achieve a competitive advantage via the synergy of digital industrialisation and industrial digitalisation; (2) digital technology pioneer regions can adopt digital industrialisation; (3) regions with a strong industrial base and traditional manufacturing industries can prioritise industrial digitalisation and (4) manufacturing regions with a large number of small and mid-size enterprises (SMEs) who can combine industrial digitalisation and digital value. This study provides theoretical and practical value for the cultivation of regional innovation ecosystems. [ABSTRACT FROM AUTHOR]

Using a fractional-order replication dynamic equation to describe the process of learning and modifying strategies, this paper tries to develop a fractional-order game model of green and low-carbon (GLC) innovation evolution in manufacturing enterprises. The uniqueness, local stability, and global stability of the evolutionary dynamics are discussed to determine the evolutionary stable strategy (ESS) of the game. The discretized form of the fractional-order game model, the ESS of the discretized game, and the sensitivity analysis of parameters are studied. Then, the influence of variable parameters on the ESS is analyzed using phase diagrams and bifurcation diagrams. The results show that: manufacturing industry can develop into a stable situation where all enterprises adopt GLC innovative technologies if their incomes exceed their investment; decreasing the fractional order of the model, the convergence rate to ESS will increase, but the stability of the system will decrease. [ABSTRACT FROM AUTHOR]

Shipping containers are known as a crucial equipment for global trade. Most of their lifespan containers are spending empty, being in process of repositioning or sitting in a yard. Increasing trade imbalances globally set up empty container logistics as one of the main concerns in container shipping industry. In line with that issue, this paper will analyse the roots of empty container imbalance and port congestion, emphasizing the importance of proper empty container management and challanges which appear along the way. Results of the research show that there is room for improvement while dealing with management of empty containers which primarly implies a cognition of the imbalance problem and long-term predictions as well as mutual cooperation of all parties involved. Furthermore, new solutions and higher level of digitalization will be required to succesully and efficiently handle today's challanging situations. Reducing empty container transport to a minimum is the key point, where foldable containers and container interchange between carriers can greatly contribute. Efficiency of empty container management must be reflected in increased environmental sustainability throughout the shipping process. [ABSTRACT FROM AUTHOR]

The significance of sustainability and environmental conservation is on the rise, garnering increasing attention from customers, businesses, industries, and regulatory authorities. To foster sustainable manufacturing practices, this research paper presents a comprehensive framework that integrates synergistic elements such as rework strategies, green technology, and carbon control policies. Imperfect quality items are being considered in the model which are proposed to be dealt with a dual approach: either salvaging or reworking them. The study also investigates the impact of carbon control policies on the manufacturing industry, focusing on sustainability and the equilibrium between environmental protection and economic growth. These policies aim to regulate carbon emissions by imposing penalties or incentives, thus encouraging companies to adopt practices that reduce their carbon footprint. Furthermore, investing in green technology offers environmental benefits, cost savings, a competitive advantage, and regulatory compliance. It helps in carbon reduction through which organizations can assess their cleaner practices and environmental efforts. The objective of the developed model is to maximize the expected profit by optimizing selling price, green investment, and production quantity. Analysis of the numerical results of the proposed study is being provided to demonstrate its validity and reliability. Results demonstrated that the utilization of the rework process yields superior outcomes compared to the scenario without rework. A closer evaluation reveals that the limited carbon policy with the rework process leads to significantly diminished levels of carbon emissions and augmented profitability in contrast to alternative policies. Importantly, this approach exhibits a remarkable 2.13% higher economic efficiency and an impressive 37.10% greater adherence to environmentally sustainable practices, thus highly favoring the adoption of such strategies. Consequently, the manufacturing industry can reduce greenhouse gas emissions while promoting economic growth and profitability through the implementation of carbon control policies and investment in green technology, particularly in conjunction with rework processes. [ABSTRACT FROM AUTHOR]

Purpose: The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM). Design/methodology/approach: In the present study, the authors use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing. Findings: The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices. Originality/value: A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originality. [ABSTRACT FROM AUTHOR]

Background: Improving the performance of marine terminals is one of the major concerns of both researchers and decision-makers in the maritime transportation sector. The problem of container storage planning and the berth allocation problem (BAP) are the two mainstays of optimizing port operations. Methods: In this work, we address these two issues, proposing two mathematical models that operate sequentially and are applicable to both static and dynamic cases. The first developed model is a mixed-integer linear problem model aimed at minimizing vessel traffic time in the port. The second model developed is a multi-objective optimization model based on goal programming (GP) to minimize both container transfer time and the number of storage areas (minimizing container dispersion). Results: The robustness of the proposed models has been proven through a benchmark with tests using data from the literature and real port data, based on the IBM ILOG CPLEX 12.5 solver. Conclusions: The two developed mathematical models allowed the both minimization of the transfer time and the number of used storage areas, whatever the number of operations handling companies (OHCs) operating in the seaport and for both static and dynamic cases. We propose, as prospects for this work, the development of a heuristic model to deal with the major instances relating to the case of large ports. [ABSTRACT FROM AUTHOR]

We analyse the value‐added creation effect of global value chain (GVC) participation activities of APEC member economies, using a fixed‐effects regression model analysis based on country–industry data from Organisation for Economic Co‐operation and Development (OECD) Inter‐country Input–Output Tables. We find that forward participation in GVCs is more desirable than backward participation for creating domestic value‐added and that the industry position in the middle stages of the production line creates higher domestic value‐added per output unit. These results hold regardless of the application of standard Ordinary Least Squares (OLS) or fractional logit for panel data and of the characteristics of the interconnected countries in GVCs (APEC member vs. non‐APEC member economies, Asian APEC vs. non‐Asian APEC members, developed vs. developing countries). This implies that the conventional firm‐ or product‐specific U‐shaped 'smile curve hypothesis' is not applicable at the economy‐wide, country–industry level. This finding suggests that depending on the product type, manufacturing industries can be a major driving force for less‐developed APEC member economies to climb the development ladder. Since GVC participation gains are diversified across industries and upgrading country–industry positions in GVCs are challenging for APEC member economies, we strongly recommend that they construct effective domestic value chains and coordinate with other members while upgrading their GVC participation. [ABSTRACT FROM AUTHOR]

The pharmaceutical industry is facing significant economic challenges due to measures aimed at containing healthcare costs and evolving healthcare regulations. In this context, pharmaceutical laboratories seek to extend the lifespan of their machinery, particularly fluid bed dryers, which play a crucial role in the drug production process. Older fluid bed dryers, lacking advanced sensors for real-time temperature optimization, rely on fixed-time deterministic approaches controlled by operators. To address these limitations, a groundbreaking approach taking into account Exploration Data Analysis (EDA) and a Catboost machine-learning model is presented. This research aims to analyze and enhance a drug production process on a large scale, showcasing how AI algorithms can revolutionize the manufacturing industry. The Catboost model effectively reduces preheating phase time, resulting in significant energy savings. By continuously monitoring critical parameters, a paradigm shift from the conventional fixed-time models is achieved. It has been shown that the model is able to predict on average a reduction of 50.45% of the preheating process duration and up to 59.68% in some cases. Likewise, the energy consumption of the fluid bed dryer for the preheating process could be reduced on average by 50.48% and up to 59.76%, which would result on average in around 3.120 kWh energy consumption savings per year. [ABSTRACT FROM AUTHOR]

The growth trend in the container industry has had a significant operational and infrastructural impact on the ports. In this context, the offshore port systems are expected to be an innovative port system in the effective use of mega container ships by offering operational and structural advantages. This study aims to guide the authorities on whether OCPS is worth investing in, in terms of investment costs. This article also highlights the financial feasibility of an OCPS that can allow countries with difficult nautical access to mega vessels to enter the global trade network. The data in this research were obtained from similar projects and studies with a series of industry assumptions. Overall, these results indicate that OCPS can compete with conventional ports in terms of investment costs, operating costs, and return on investment. Thus, a more sustainable container shipping network can be achieved with the advantages of OCPS. [ABSTRACT FROM AUTHOR]

Liner shipping plays an important role in international shipping. This article systematically reviews studies regarding liner shipping alliance management from three decision-making levels: strategic decision, tactical operations and management policy. These studies are addressed and summarized throughout the management process, including making an alliance, partner selection, cooperation mechanism design, synergy planning and performance appraisal. In order to make a scientifical classification, this paper further elaborates comprehensively on journals, research topics and modelling approaches of the studies and the relationships among them. At last, in terms of the gap between the practices of liner container shipping industry and existing academic studies, diversification and flexibility of cooperation modes, intelligent operations, multi-objective optimization, environmental protection and a sound supervision mechanism in liner shipping alliance management are expected to be covered in future research. The main purpose of this paper is to improve the understanding of the management concept and technology of liner shipping alliance, and encourage more research and application. ● Liner shipping alliance management from three decision-making levels is investigated. ● Problems to form a liner shipping alliance such as partner selection and cooperation design are discussed. ● Journals, research topics and modelling approaches of the studies are reviewed. [ABSTRACT FROM AUTHOR]

Based on the concept of 'materials by design,' strengthening mechanism of 54SiCrV6 spring steel was investigated to guide the alloy design of higher strength spring steel. The main strengthening mechanisms of 54SiCrV6 spring steel are dislocation strengthening, precipitation strengthening and fine grain strengthening. Targeted alloy design was carried out under the guidance of theoretical calculation about strengthening mechanisms. A 2100 MPa new spring steel based on 54SiCrV6 with a relatively good match of strength and ductility which can be manufactured by industry was obtained by optimizing the alloy composition, and the effect of Nb and Si was investigated. Nb can refine the microstructure so that it improves strength and ductility at the same time. The retardation of ε-θ transformation is the main reason why the strength and sag resistance of high-Si steel are better than that of low-Si steel. [ABSTRACT FROM AUTHOR]

In this research, the feasibility of using Acanthocardia tuberculata shell waste from the canning industry in the manufacturing of self-compacting mortar (SCM) was tested. The seashells were finely ground to be used as filler instead of the limestone filler normally used in this type of SCM. First, a physicochemical and microstructural characterisation of all raw materials was carried out, including the particle size distribution of both fillers. Subsequently, the self-compactability properties in the fresh state of SCM were evaluated using a total substitution by volume of limestone filler for seashell powder, using different self-compactiblity parameters. The mineralogical phases of all the SCM tested were identified once hardened by means of X-ray diffraction technique, thermogravimetric and differential thermal analysis. In addition, the mechanical properties, water absorption capacity, dry bulk density and accessible porosity of water of hardened mortars at 28 days of curing were analysed. The effect of replacing limestone filler by Acanthocardia tuberculata filler resulted in a decrease in compressive strength of 29.43, 16.84 and 2.29%, respectively. The results indicate that it is possible to completely replace natural limestone filler with Acanthocardia tuberculata shell filler without significantly affecting the mechanical properties of SCM. [ABSTRACT FROM AUTHOR]

Chinese listed manufacturing companies face more serious financing constraints. Considering financing constraints and uncertainties, an accurate assessment of the innovation efficiency of China's listed manufacturing industry can provide decision support for China to become an innovation nation. We construct a heterogeneous stochastic frontier model to analyze the overall innovation efficiency, innovation quality efficiency, and innovation quantity efficiency of the Chinese manufacturing industry, using Chinese A-share listed manufacturing companies as a research sample from 2011–2019. The research results show that (1) Financing constraints make enterprises deviate from innovation efficiency, and internal financing significantly reduces enterprise financing constraints and does not bring uncertainty to subsequent financing, commercial credit, and external financing; although it can alleviate enterprise financing constraints, it will increase the uncertainty of subsequent financing constraints and the enterprise size.. (2) Internal financing, commercial credit financing, external financing, and firm size can effectively alleviate the financing constraint for firms to carry out quality innovation activities of invention patents and quantitative innovation activities of utility patents; internal financing, external financing, and firm size can effectively alleviate the financing constraint for quantitative innovation activities of design patents, while commercial credit financing has no significant effect. (3) The influence of factors such as financing constraints makes the real innovation efficiency of China's listed manufacturing industry only about 20%, and enterprises face more serious financing constraints. (4) The heterogeneity results found that the overall innovation efficiency, innovation quality efficiency, and innovation quantity efficiency in the eastern region are the highest, and the innovation efficiency level is higher than the national average, followed by the central and western regions. The overall innovation efficiency, innovation quality efficiency, and innovation quantity efficiency of the central region are higher than the national average, but the innovation quality efficiency and innovation quantity efficiency of the western region are lower than the national average; the overall innovation efficiency, innovation quality efficiency, and innovation quantity efficiency of large-scale enterprises are the highest and even higher than the average value of the whole sample, while the innovation efficiency of medium and small-scale enterprises are lower than the average value of the whole sample. [ABSTRACT FROM AUTHOR]

This article aims to review the structural transformation in the container shipping industry from 1995 to 2020 and identify the growth strategies of global carriers to enhance their market presence. Along with large investments in mega vessels, the growing deployment of charter capacity in all ship sectors has been a prominent trend in fleet expansion. The waves of mergers/acquisitions and bankruptcies have narrowed the group of global carriers. In addition to internal and external growth, they have been increasingly involved in strategic alliances to expand service coverage and compete well with powerful rivals. A few mega carriers have progressively captured the industry. In 2020, the Top 12 controlled 88 % of the global supply, and nearly 56 % was in the hands of the Top 4. Besides the substantial expansion of the four biggest carriers, we can identify the de-concentration within this gigantic group through the less inequality between their market shares. [ABSTRACT FROM AUTHOR]

The article presents the discussion on Aluminum industries getting involved in Environmental Stewardship. Topics include increasing use of aluminum leading to significant weight reductions of components in the automotive and aerospace sector; and saving a large amount of fuels in the use phase of cars, trucks, and planes.

Although long-haul intermodal container transportation combines multiple transportation modes including shipping, the most critical aspect of enabling door-to-door service is container port drayage operation. Container port drayage connects customers and container ports and provides an effective solution to the first/last-mile problem faced by the container logistics industry. Therefore, container port drayage has a significant impact not only on the economic advantages of the long-haul intermodal container transportation chain, but also the management, environment and social benefits of the stakeholders involved. In this paper, we comprehensively review the existing literature on container port drayage studies from different methodological angles (e.g., model development and algorithm design) and technological angles (e.g., autonomous truck and blockchain). Moreover, with the shifting of industrial focus from traditional operations and management of container port drayage towards e-commerce platforms, we identify potential issues for future research directions. • Critically review the studies on container port drayage from the perspective of operations and management. • Systematically examine the interactions between main stakeholders of container port drayage services. • Identify future research focuses and directions of container port drayage operations and management. [ABSTRACT FROM AUTHOR]

This article investigates the role of demand uncertainty in explaining cyclical investment fluctuations in the container shipping industry. I develop and estimate a dynamic oligopoly model with learning in which firms choose investment and scrapping. In this model, firms are uncertain about the true parameters in the underlying process for demand, and form and revise their beliefs using available information. Counterfactual analysis reveals that uncertainty about the demand process amplifies investment cycles through (i) leading firms to revise beliefs more drastically as they experience demand fluctuations, and (ii) intensifying strategic incentives among firms. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to draw attention to the familiarity effect among international multimarket contact (MMC) firms on coopetition in the global container shipping industry and to better understand the contingency model of structural holes and in-degree centrality on joint price elevation actions and subsequent performance. Design/methodology/approach: Drawing on competitive dynamics and the literature on networks, a panel data model is developed from 6,489 competitive and 7,146 cooperative actions of the top 21 shipping firms in 18 global arenas with a structured content analysis method being applied. Findings: Stronger MMC by firms requires increased levels of cooperative actions to elevate prices. This coopetition relationship is enhanced or weakened when the focal firm occupies a higher level of structural hole or position of competitive in-degree centrality. Practical implications: Shipping liners seeking to cooperate with joint action in oligopolistic markets are offered guidelines and strategies to increase their performance through their actions. Originality/value: This study contributes to the literature on coopetition networks by further analyzing interfirm relationships and interactions that enhance performance, while exploring network positioning strategies to mitigate risks. [ABSTRACT FROM AUTHOR]

To produce flawless glass containers, continuous monitoring of the glass gob is required. It is essential to ensure production of molten glass gobs with the right shape, temperature, viscosity and weight. At present, manual monitoring is common practice in the glass container industry, which heavily depends on previous experience, operator knowledge and trial and error. This results in inconsistent measurements and consequently loss of production. In this article, a multi-camera based setup is used as a non-invasive real-time monitoring system. We have shown that under certain conditions, such as keeping the glass composition constant, it is possible to do in-line measurement of viscosity using sensor fusion to correlate the rate of geometrical change in the gob and its temperature. The correlation models presented in this article show that there is a strong correlation, i.e., 0.65, between our measurements and the projected viscosity. [ABSTRACT FROM AUTHOR]