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The MolSSI Driver Interface (MDI) Project is an effort to simplify and standardize the process of enabling tight interoperability between independently developed code bases and is supported by numerous software packages across the domain of chemical physics. It enables a wide variety of use cases, including quantum mechanics/molecular mechanics, advanced sampling, path integral molecular dynamics, machine learning, ab initio molecular dynamics, etc. We describe two major developments within the MDI Project that provide novel solutions to key interoperability challenges. The first of these is the development of the MDI Plugin System, which allows MDI-supporting libraries to be used as highly modular plugins, with MDI enforcing a standardized application programming interface across plugins. Codes can use these plugins without linking against them during their build process, and end-users can select which plugin(s) they wish to use at runtime. The MDI Plugin System features a sophisticated callback system that allows codes to interact with plugins on a highly granular level and represents a significant advancement toward increased modularity among scientific codes. The second major development is MDI Mechanic, an ecosystem management tool that utilizes Docker containerization to simplify the process of developing, validating, maintaining, and deploying MDI-supporting codes. Additionally, MDI Mechanic provides a framework for launching MDI simulations in which each interoperating code is executed within a separate computational environment. This eliminates the need to compile multiple production codes within a single computational environment, reducing opportunities for dependency conflicts and lowering the barrier to entry for users of MDI-enabled codes. [ABSTRACT FROM AUTHOR]

Container ports are increasingly deploying electric trucks (ETs) for inter-terminal transportation (ITT). However, en-route recharging activities, which reduce ITT efficiency, are necessary for ETs because of limited battery capacity. This study proposes a real-time strategy to schedule the en-route recharging activities for ETs. The strategy dynamically monitors the ITT system, and simultaneously designates charging piles for ETs and determines their positions in the service sequences. An ET may be designated to a remote but shorter-queued pile. The total queue time of the ETs served by a designated pile can be reduced by adjusting the service sequence. The effectiveness is demonstrated in comparison with an alternative strategy in a real-world case. The results indicate that the proposed strategy effectively reduces container throughput time by 21.8% and ET energy consumption by 12.3%. Sensitivity analyses suggest that the strategy is applicable for ports with multiple charging stations and moderate ET fleet size. [ABSTRACT FROM AUTHOR]

This study explored the effectiveness of various employer-based travel demand management strategies in promoting multimodality and mode substitution among employees in Washington state using a mixed multinomial logit model. The study found that employee transportation coordinators played an important role in encouraging the use of sustainable travel modes. Spatial analysis revealed that individuals who lived and worked in proximity were more likely to adopt multimodal transportation. The study also highlighted the convenience of driving alone and the lack of information on sustainable alternatives as two major barriers to the adoption of sustainable transportation modes and recommended educational campaigns to increase awareness. To inform practice, this study identified transit subsidies, parking pricing, and work schedule flexibility as the most effective TDM strategies to promote multimodality and mode substitution, followed by compressed workweeks, and providing easy access to transit and amenities. [ABSTRACT FROM AUTHOR]

Park-and-ride (P&R) facilities, when combined with the advantages of urban rail transit, have the potential to alleviate congestion problems and promote multimodal transportation. However, some facilities face operational and utilization issues in terms of efficiency and capacity, which may not fully meet users' needs. Therefore, it is essential to conduct research that addresses challenges related to user parking and facility operation. A critical step in this direction is to study relevant indicators that comprehensively measure the operational efficiency and capacity of the facilities, and to find effective methods to tackle problems in P&R operation. This study examined and selected data indicators for P&R facilities around targeted stations, taking those around Nanjing urban rail transit stations as an example. Data envelopment analysis was employed to establish a P&R operational efficiency and capacity evaluation model. This model was enriched with a comprehensive evaluation of multiple factors that affect P&R, with these factors significantly influencing the recorded evaluation results. By identifying operational issues in P&R facilities around Nanjing urban rail transit stations, this study proposed optimization solutions to enhance their operational efficiency and capacity. The findings can serve as a reference for the coordinated development of P&R facilities and urban rail transit. [ABSTRACT FROM AUTHOR]

The transition from an error-prone, slower, and extremely high-volume legacy system like monolithic system to a faster, lighter, and error-free microservices based system is not always so simple. Microservices are independently deployable and allow for a better team autonomy. In this work, several migration efforts to migrate from a legacy based monolithic system to a pure distributed microservicesbased system has been tested and deployed in keeping two DevOps principles, the software code build and deployment time and latency in monolithic and microservices. Some real-time projects are considered to measure the performance and the time taken to execute the experiments. To measure the total build and deployment time and latency, Jenkins, Prometheus, and JMeter are installed which are industryrecommended softwares. It is observed that there is a total of 7 seconds taken to build and deploy at containers for 10 microservices whereas 10 monolith applications took almost 260 seconds to be built and deployed to the application server. While increasing more requests per second it is observed that upto 3000 requests per second, it impacted the response time of monolith applications but microservices stays the same. The main conclusion is that microservices are rarely impacted in response time with respect to requests per second. [ABSTRACT FROM AUTHOR]

Efficient coordinated scheduling has long been a focal point in port research, complicated by the diverse optimization goals dictated by different port characteristics. This study focuses on Yangtze River ports, exploring coordinated scheduling amidst river–sea intermodal transportation. Our research aims to reduce berth deviation costs and shorten the total scheduling time for ships, while maximizing berth utilization rates for ports. Initially, we analyzed the operational realities of Yangtze River ports and waterways. Subsequently, we innovatively introduced three key factors influencing scheduling: berth preferences, seagoing ship inspections, and planning cycles. Finally we proposed the optimized Non-dominated Sorting Genetic Algorithm III (NSGA-III). Evaluating the model using a seven-day dataset of vessel activities at Yangtze River ports revealed significant improvements: the optimized NSGA-III enhanced objective values by 30.81%, 13.73%, and 12.11% compared to the original scheduling approach, surpassing both conventional NSGA-III and NSGA-II algorithms. This study underscores the model's efficacy in not only reducing operational costs through optimized ship and berth sequencing but also in enhancing clearance efficiency for relevant authorities. [ABSTRACT FROM AUTHOR]

As an extension of networked control systems, cloud control systems (CCSs) have emerged as a new control paradigm to improve the service quality of emerging control missions, such as data-driven modeling and automated vehicles. Existing studies have used the workflow-based restructured method to optimize the computation-intensive algorithms in the CCSs. However, the challenges here are how to define and submit these algorithms' workflows as cloud services and execute these algorithms' workflows in a containerized manner. Based on these challenges, we propose a containerized solution for the control-algorithm-as-a-service (C3aS) in the CCSs, namely ControlService. It offers the control algorithm as a cloud workflow service and uses a customized workflow engine to realize the containerized execution. First, we employ a cloud workflow representation method to define a control algorithm into an abstract cloud workflow form. Afterward, we provide a cloud service representation of the abstract cloud workflow. Next, we design a workflow engine and submit the cloud service to this workflow engine to implement containerized execution of this cloud service in the CCSs. In the experiment, we discuss the cloud service form and containerized implementation of the subspace identification method. Experimental results show that the proposed ControlService has significant performance advantages in computational time, reduction percentage, and speedup ratio compared with the baseline method. [ABSTRACT FROM AUTHOR]

Summary: This study underscores the growing significance of multimodal transportation within the cargo sector and its consequential environmental impacts. We present a novel mathematical model for operation scheduling, incorporating variables such as resource availability, customer service benchmarks, and environmental considerations. Our objective is to mitigate transportation expenses and reduce delivery delays. The proposed approach advocates LU decomposition with a pivot strategy for rapid model resolution, adherence to convergence criteria, optimization of cost strategies, and efficient resource utilization. Leveraging the adaptive neural fuzzy inference system (ANFIS) and genetic algorithm (GA), our methodology facilitates learning from past decisions to enhance solutions, aligning supply, and demand efficiently. We evaluate financial and environmental implications across four scenarios, offering insights into the economic and environmental advantages of various transportation modes—trains, ships, airplanes—compared to truck transportation, with a specific focus on CO2 emission impacts. Implementing the ANFIS+GA model in multimodal scenarios yield impressive results: minimal MAPE transportation cost of 0.17%, R2 transportation cost of 0.996, MAPE CO2 emissions of 0.13%, and R2 CO2 emissions of 0.996. By identifying cost‐efficient routes and optimizing resource allocations, our approach enables informed decisions regarding vehicle distribution, supplier selection, and contract negotiations. Additionally, we use LU decomposition to establish the supplier risk threshold, crucial for comparing emission trade variances. Multimodal scenarios typically yield lower emissions, favoring buying emission allowances low and selling them high. Notably, the risk threshold affects low‐emission provider utilization, impacting transportation emissions. With a risk threshold of 0.12 and an emission price of 1.2, our ANFIS+GA‐based multimodal approach achieves a significant −20% deviation in CO2 emissions. [ABSTRACT FROM AUTHOR]

Air-rail intermodal transportation (ARIT) plays a crucial role in China's intermodal transportation system. This study aims to model and optimize issues such as inefficiency and complexity in China's ARIT freight transportation using Business Process Reengineering and Stochastic Petri Nets theories. The Petri Net (PN) model for incoming freight transport in ARIT is based on actual operations, employing a new method involving Stochastic Petri Nets and isomorphic Markov Chains theory for performance analysis. Performance analysis results help intuitively identify areas needing optimization. Based on optimization principles, elements such as railway container packaging are improved, resulting in an optimized PN model for ARIT. Finally, data analysis shows that the optimized ARIT model reduces total delay by 7.7% compared to the original. This demonstrates that the new method, combining Markov Chain performance analysis and optimization principles, is feasible and effective for ARIT optimization. [ABSTRACT FROM AUTHOR]

In the context of bulk grain container transportation, the complex logistics can lead to grain mildew and subsequent economic losses. Therefore, there is a pressing need to explore swift and real‐time mildew detection technology. Our investigation, simulating actual transportation conditions, revealed that Aspergillus, Penicillium, and Rhizopus were the primary molds responsible for soybean mildew during container transportation. Utilizing gas chromatography‐ion migration spectroscopy (GC‐IMS), we analyzed the correlation between the mVOCs (microbial volatile organic compounds) produced by dominant mold and the VOCs emitted during soybean mildew. Principal Component Analysis (PCA) and clustering results demonstrated the distinctive identification of VOCs in soybeans with varying degrees of mildew. The mildew degree significantly influenced the content variation of VOCs. As the mildew degree increased, the concentrations of nonanal, octanal, etc. progressively decreased, contrasting with the rising levels of phenylacetaldehyde, 3‐methyl‐2‐butenal, etc. Therefore, the combination of GC‐IMS with chemometrics proves to be a viable method for identifying the mildew degree of soybeans. Therefore, this study underscores the importance of implementing effective mildew detection techniques in the challenging context of bulk grain container transportation. [ABSTRACT FROM AUTHOR]

The international literature indicates a wide interest in the distances public transport users walk to access their services. Urban and transport planners seek information on acceptable walking distances (AWD) in the provision of minimum levels of service coverage. This study uses a large database from Melbourne, Australia, to analyze trip length frequency distributions (TLFD) of walking access and egress to consider AWD in a multimodal public transportation system and to examine tolerable walking distances (TWD). AWD provides a guide to planners about stop/station locations for desirable minimum service coverage. TWD is a representative maximum walking distance for assessing the effectiveness of that service provision and can be used in conjunction with AWD. A statistical distribution function for walking distances can facilitate the use of regional values for AWD and TWD in transit service planning. The Burr Type XII distribution is shown to provide a good fit to the observed data. This provides a valuable tool for the analysis of percentile walking distances and suggests a general framework for the study of AWD and TWD in any city or region. [ABSTRACT FROM AUTHOR]

In metro areas in the United States, those who cannot drive have limited access to job opportunities and services located in low-density suburbs that are poorly served by public transit; at the same time, congested metropolitan freeways cause significant travel delays for commuters who have no option but to drive long distances to job sites during peak periods. In this Viewpoint, I present a systemwide approach to financing and fulfilling the transportation needs of those who do not drive while at the same time providing a congestion-free travel choice for those who do. It combines congestion pricing with cash rewards for those who choose to share rides on a network of congestion-free lanes converted from general-use lanes to priced lanes. Net revenues support multimodal travel options including transit, carpooling, and mobility hubs with transportation services to and from trip origins and final destinations, also known as first- and last-mile services. The strategy would allow existing metropolitan freeway networks to be transformed into financially viable, sustainable, and equitable multimodal systems providing high-quality travel choices that could lead to more sustainable urban development patterns. [ABSTRACT FROM AUTHOR]

Similar to world trade, container transportation and the tendency to transport various cargoes with containers is increasing in Türkiye. Moreover, in a world where production centers will be shifting with the Covid-19 pandemic, it can be said that Türkiye's production will increase and over and above expectations, container traffic in its ports will expand. In this context, it is anticipated that the urban of Kocaeli will need more container transportation between container ports and logistics centers. Development processes need to be well planned so that this increase in container transportation does not disrupt the traffic of cities, ports and logistics centers. This planning should be carried out by reconciling the current situation, the demands of the logistic stakeholders and the future vision. This study aims to reveal whether Kocaeli container ports, logistics centers and the urban need alternative transportation methods for economic and sustainable development and to offer solution suggestions by analyzing the opinions of all logistics stakeholders. The data collected in the research were analyzed using the Kruskal Wallis H test in the IBM SPSS 22.0 program, and the hypotheses were questioned. Reconsidering alternative opportunities in transportation for the development of ports and logistics stakeholders of Kocaeli urban in a competitive structure, increasing the road and railway transportation capacities between the ports and Köseköy Logistics center will be appropriate, a new/alternative transportation method will have a positive impact on the efficiency of the ports/logistics businesses, inland waterway/canal to the logistics center. It has been concluded that integration with transportation may be beneficial in the future and may have a moderate impact on business volume and productivity. [ABSTRACT FROM AUTHOR]

The study reviews the essence of inland dry ports (IDPs) in Nigeria and the vital role played by inland transportation as performance-enhancing factor in the mutually complementary services of IDPs and seaports. The study adopts survey research design and used primary and secondary sources of data. Questionnaire and interview were the instruments used for sourcing primary data. Six objectives of the study centre on the problem and the study adopted the Taro Yamane formula to determine 343 as the sample size, out of study population of 1412. The study uses descriptive statistics and identifies the challenges faced by rail, road and inland waterways modes of transport in the quest to connect them to IDPs in Nigeria from interview, literature review, media reports, port operators and other stakeholders. It identifies the effects of poor port hinterland connectivity, and noted the high-cost element in transporting containers through deplorable roads to IDPs in Nigeria, which is taking its toll on the performance of the IDPs. The study recommends that Government should adopt a holistic national freight logistics strategy, which involves all tiers of government and industry, and make provision for enhanced coordinated national multi-modal and intermodal approaches to freight planning. This would facilitate the reduction of trade costs and enhance the optimization of the performance of inland dry ports in Nigeria. [ABSTRACT FROM AUTHOR]

Intercity passenger rail services must be designed in a way that users will be willing and able to ride. This study examines potential users' current travel behaviors and willingness to ride a proposed passenger rail between New Orleans and Baton Rouge in Louisiana, US. By analyzing a user preference survey with discrete choice experiment (DCE), the results show a strong support of the proposed rail service, but distinct deficiencies in the current multimodal transportation environment connecting to and from proposed stations. In addition, previous analyses include potentially faulty assumptions about trip purposes, which have critical implications for service design. The findings of this study reflect the priorities of likely rail passengers and advance efforts to plan for successful passenger rail service operation in Southeast Louisiana. The potential user-based and data-driven approach of this case study can also be used for planning other passenger rail services in the US. [ABSTRACT FROM AUTHOR]

As the aspirational Complete Streets movement sweeps North America with multimodal transportation policies and plans, communities need practice-ready tools that reinforce social inclusion and sustainable development through the movement's implementation. Here we present two such tools: (1) gender analysis, which considers gendered experiences and roles in the context of Complete Streets so as to benefit diverse travelers with improved safety, transportation choices, support for caregivers, and more; and (2) an approach called SDG 360 Thinking, a holistic analysis of Complete Streets policy co-benefits, unintended harms, and trade-offs using the UN Sustainable Development Goals. We piloted these tools in workshops with community members and technical experts. Workshop participants gained new insights, made connections across sectors, took more inclusive and diverse perspectives, and made recommendations about Complete Streets policies. These tools are practical, easy to understand, and could be broadly impactful for communities. [ABSTRACT FROM AUTHOR]

With the continuous development of large-scale engineering projects such as construction projects, relief support, and large-scale relocation in various countries, engineering logistics has attracted much attention. This paper addresses a multimodal material route planning problem (MMRPP), which considers the transportation of engineering material from suppliers to the work zones using multiple transport modes. Due to the overall relevance and technical complexity of engineering logistics, we introduce the key processes at work zones to generate a transport solution, which is more realistic for various real-life applications. We propose a multi-objective multimodal transport route planning model that minimizes the total transport cost and the total transport time. The model by using the ε − constraint method that transforms the objective function of minimizing total transportation cost into a constraint, resulting in obtaining pareto optimal solutions. This method makes up for the lack of existing research on the combination of both engineering logistics and multimodal transportation, after which the feasibility of the model and algorithm is verified by examples. The results show that the model solution with the introduction of the key processes at work zones produces more time-efficient and less time-consuming route planning results, and that the results obtained using the ε − constraint method are more reliable than the traditional methods for solving multi-objective planning problems and are more in line with the decision maker's needs. [ABSTRACT FROM AUTHOR]

Advances in technology have converted the traditional paper-based bill of lading into an electronic bill of lading (eBL) format in recent years. In this regard, different eBL systems have been introduced and are increasingly being employed in the maritime transportation industry. In line with this, some major container shipping lines have started to declare their eBL services to their customers recently. However, it has been observed that there have not been come across any Türkiye origin container shipping lines to utilize eBL systems. Therefore, considering container shipping lines of Türkiye, this paper sought to disclose the barriers to the transition of eBL systems in Türkiye. The barriers have been revealed through literature review and confirmed by industrial experts. Following that Fuzzy-AHP method was performed to prioritize their importance quantitatively. It was revealed that the legal barrier among the main barriers appeared as the most important barrier. [ABSTRACT FROM AUTHOR]

In this paper, we study the k edge-connected L-hop-constrained network design problem. Given a weighted graph G = (V , E) , a set D of pairs of nodes, two integers L ≥ 2 and k ≥ 2 , the problem consists in finding a minimum weight subgraph of G containing at least k edge-disjoint paths of length at most L between every pair { s , t } of D. The problem has several applications in telecommunications network design. It also has applications in reliable container transportation network design and public transportation. Even if the problem has been studied for several decades, it appears, to the best of our knowledge, that the associated polytope is not well known, even when L ∈ { 2 , 3 } . In this paper, we are particularly interested in the polyhedral analysis of the problem as well as an exact solving of the problem for large-scale instances. We consider the case where L ∈ { 2 , 3 } and present two integer programming formulations introduced in Diarrassouba et al. (2016). Then, we consider the polytope associated with these formulations, and present several new classes of valid inequalities, involving the so-called design variables. We also present separation algorithms for these inequalities and devise Branch-and-Cut algorithms for solving the problem. Finally, we present an extensive computational study for L ∈ { 2 , 3 } and k ∈ { 3 , 4 , 5 } , in which we test the efficiency of our Branch-and-Cut algorithms. We also compare the algorithm against a resolution using CPLEX Branch-and-Cut and CPLEX Benders Decomposition frameworks. The results show that our Branch-and-Cut algorithm can be competitive against these two frameworks. [ABSTRACT FROM AUTHOR]

Land cover and its changes over time are significant for better understanding the Earth's fundamental characteristics and processes, such as global climate change, hydrology, and the carbon cycle. A number of land cover-geoprocessing models have been proposed for land cover-data production with different spatial and temporal resolutions. With the massive growth in land cover data and the increasing demand for efficient model utilization, developing efficient and convenient land cover-geoprocessing models has become a formidable challenge. Although some model-migration methods have been proposed for handling the massive data, the intricacy of land cover-data and -heterogeneity models frequently prevent current strategies from directly meeting demand. In this paper, we propose the PAMC-LC-containerization approach to overcome the difficulties associated with moving existing land cover models in the open web environment. Based on the idea of model migration, we design a standardized model description and hierarchical encapsulation strategy for land cover models, and develop migration and deployment methods. Furthermore, we assess the viability and efficacy of the proposed approach by using coupled workflows for model migration and the introduction of visualization on the Mts-WH dataset and the Google dataset. The experimental results show that the PAMC-LC approach can simplify and streamline the model migration process, with important ramifications for increasing productivity, reusing models, and lowering additional data-transmission costs. [ABSTRACT FROM AUTHOR]

The primary objective of this research was to implement a more efficient handling strategy in the container terminal storage yard at the Baltic Hub Container Terminal (BHCT). Following an analysis of the current logistics system associated with starboard-side mooring, an attempt was made to shift the mooring to the port side. Two routing strategies corresponding to the analysed mooring operations were tested using a developed model. Simulations were conducted to enable a comparison of various handling strategies that were suitable for the mooring arrangements and equipment under investigation. This analysis was based on reliable information obtained from a real process. Evaluative criteria for examining the impact of changing the handling strategy of internal movement vehicles (IMVs) in the terminal storage yard in terms of process efficiency included the total time and distance of container transportation and the truck utilisation level. A new route for IMVs was developed to accommodate the changed mooring operation. This adjustment aimed to enhance the discharge process and to reduce the distance travelled, resulting in a reduction in fuel consumption. The shorter travel distance also positively impacted productivity, and contributed to a reduction in operating costs. The changes to the mooring arrangement directly resulted in a 10% increase in the quay crane gross productivity, measured in moves per hour (mph). Given that many ports do not pay attention to how ships are moored, the case study and analysis presented here can help staff quickly improve their handling strategy and productivity, thus increasing the port's competitiveness. [ABSTRACT FROM AUTHOR]

• Evaluated pedestrians' and bicyclists' perceptions on six AV regulations. • Examined pedestrians' and bicyclists' perceptions of testing AVs in public streets. • Identified strong support for AV-related data sharing regulations. • Older respondents showed higher approval of AV testing on public roadways and less support for regulating AVs. • AV technology familiarity and safe road sharing perceptions with AVs resulted in lower support for AV regulations. Introduction: Development and implementation of autonomous vehicle (AV) related regulations are necessary to ensure safe AV deployment and wide acceptance among all roadway users. Assessment of vulnerable roadway users' perceptions on AV regulations could inform policymakers the development of appropriate AV regulations that facilitate the safety of diverse users in a multimodal transportation system. Method: This research evaluated pedestrians' and bicyclists' perceptions on six AV regulations (i.e., capping AV speed limit, operating AV in manual mode in the sensitive areas, having both pilot and co-pilot while operating AVs, and three data-sharing regulations). In addition, pedestrians' and bicyclists' perceptions of testing AVs in public streets were evaluated. Statistical testing and modeling techniques were applied to accomplish the research objectives. Results: Compared to the other AV regulations assessed in this research, strong support for AV-related data sharing regulations was identified. Older respondents showed higher approval of AV testing on public roadways and less support for regulating AVs. AV technology familiarity and safe road sharing perceptions with AVs resulted in lower support for AV regulations. Conclusions: Policymakers and AV technology developers could develop effective educational tools/resources to inform pedestrians and bicyclists about AV technology reliability and soften their stance, especially on AV regulations, which could delay technology development. Practical Applications: The findings of this research could be used to develop informed AV regulations and develop policies that could improve pedestrians' and bicyclists' attitudes/perceptions on regulating AVs and promoting AV technology deployments. [ABSTRACT FROM AUTHOR]

As a process, containerization in Africa is still on-going. In the last decades, a number of new port projects have emerged in parallel with the reorganization of services by shipping companies. To reflect these changes, this paper studies several questions on the geographic differentiation of maritime connectivities across the continent as well as on the longitudinal trends in maritime connectivities in relation to evolving economic, trade and logistical contexts. To this end, we propose a synthetic visualization of the country-level containerized links using the Liner Shipping Bilateral Connectivity Indicator (LSBCI). The resulting graph, obtained through Multidimensional Scaling (MDS), shows that West Africa and East Africa appear as relatively separated subsystems, mediated by several countries in the Northern and the Southern parts of Africa. Between 2006 and 2016, West Africa, initially tied to Europe and America, is increasingly tied to Asia. Our results show major improvements in the maritime connectivity of Morocco, Djibouti and several West African ports. Conversely, most of mainland East African countries stagnate or decline. Connectivities have not converged across the continent. In some cases changes in maritime connectivity mostly result from variations in vessel size. We explore a method to identify these situations. [ABSTRACT FROM AUTHOR]

In the rapidly evolving IT industry, containerization has introduced new security challenges including cloud data breaches. DL-HIDS explores the application of Deep Learning (DL) techniques for detecting such attacks. Various system call-based features, including the sequence, frequency, and metadata of system calls, as well as images, derived from these calls were explored. While using images as features is effective for DL models, determining the optimal image feature size can be challenging and requires extensive experimentation. The existing approach uses pre-trained Convolutional Neural Networks (CNNs) that incorporate system call parameters with metadata that are redundant resulting in a low detection rate. To address these limitations, we employ a deep CNN that takes images generated from system call logs as input. Our experimentation involves varying image size, system call parameters, and CNN architecture using the Leipzig Intrusion Detection DataSet-2019 dataset containing recent containerized cloud environment attack data. Our results demonstrate improvement over state-of-the-art methods toward accuracy, precision, recall, F1 score, and false-positive rate. [ABSTRACT FROM AUTHOR]

In this study, we extend the research on the multimodal routing problem by considering flexible time window and multi-uncertainty environment. A multi-uncertainty environment includes uncertainty regarding the demand for goods, the travel speed of the transportation mode, and the transfer time between different transportation modes. This environment further results in uncertainty regarding the delivery time of goods at their destination and the earliness and lateness caused by time window violations. This study adopts triangular fuzzy numbers to model the uncertain parameters and the resulting uncertain variables. Then, a fuzzy mixed integer nonlinear programming model is established to formulate the specific problem, including both fuzzy parameters and fuzzy variables. To make the problem easily solvable, this study employs chance-constrained programming and linearization to process the proposed model to obtain an equivalent credibilistic chance-constrained linear programming reformulation with an attainable global optimum solution. A numerical case study based on a commonly used multimodal network structure is presented to demonstrate the feasibility of the proposed method. Compared to hard and soft time windows, the numerical case analysis reveals the advantages of the flexible time window in reducing the total costs, avoiding low reliability regarding timeliness, and providing confidence level-sensitive route schemes to achieve flexible routing decision-making under uncertainty. Furthermore, the numerical case analysis verifies that it is necessary to model the multi-uncertainty environment to satisfy the improved customer requirements for timeliness and enhance the flexibility of the routing, and multimodal transportation is better than unimodal transportation when routing goods in an uncertain environment. The sensitivity analysis in the numerical case study shows the conflicting relationship between the economic objective and the reliability regarding the timeliness of the routing, and the result provides a reference for the customer to find a balance between them. [ABSTRACT FROM AUTHOR]

In this paper, we focus on some aspects of structural operational semantics for a selected domain-specific language for robot control. After defining a syntax for two independent languages for control of a robot's movements, we define a structural operational semantics for them. The integrated part of structural operational semantics is abstract implementation, which consists of defining abstract machine and transformation of a programming language to abstract machine instructions. The achieved results as well as the mentioned research are a part of the research in the field of semantic methods, where we focus on the formalization of semantic methods for software engineering. This area is also very important for the training of students and young IT experts because the semantic methods can help to understand program behavior and detect errors in program design. To make the teaching of formal semantics in the field of domain-specific languages more attractive, we have also prepared an application that serves to visualize the individual steps of the program on an abstract machine - simulation of translated code with visualization of a robot's movement. [ABSTRACT FROM AUTHOR]

The core problem of a multimodal transportation system is integrating various transportation modes into a cohesive, efficient, and user-friendly network. This study introduces a novel centralized load concentration approach for regions facing geographic challenges. The principal aim is improving multimodal transportation systems by mitigating CO2 emissions and improving operational efficiency. This will significantly reduce high logistics costs and the environmental impact caused by greenhouse gas emissions, particularly in land transportation, aligning with the global sustainable development goals and offering a promising path towards a more sustainable future. The proposed method implicates direct cargo transportation from its origin to the export ports without passing through intermediate centers. The mathematical model determines the most efficient means of transportation for each route, considering variables such as distance, volume, and type of cargo. Research results indicate that multiple hubs may not be necessary in scenarios with high freight concentration, which could streamline transportation and logistics operations. The modal preferences vary depending on regional dynamics and cargo characteristics, with rail and sea transport emerging as preferable options in specific circumstances, outperforming road transport. The proposed model shows reductions in logistics costs and CO2 emissions compared to road-focused scenarios. This study provides an adaptable framework for optimizing multimodal transportation systems in regions with similar geographic and logistical attributes. It offers a versatile solution to various contexts and needs. Lastly, the strategic integration of multiple modes of transportation is fundamental to improving efficiency and sustainability. [ABSTRACT FROM AUTHOR]

Port congestion significantly impacts the reliability of container ship schedules. However, the existing research often treats vessel time in port as a random variable, failing to systematically consider the complex impact of port congestion on ship schedules. This study addresses the issue of container ship schedule design under port congestion. Vessel waiting times in ports are predicted and quantified by queueing theory, along with information on vessel schedules, cargo handling volumes, and available port operating time windows. We propose a mechanism for selecting container handling efficiencies for arriving vessels, thereby determining their in-port handling times. By jointly considering the uncertainty of vessel waiting and handling times in port, we establish a mixed-integer nonlinear programming model aimed at minimizing the total cost of liner transportation services. We linearize the model and solve it using CPLEX, ultimately devising a robust ship schedule. A simulation analysis is conducted on a real liner shipping route from Asia to the Mediterranean, revealing that extreme weather events, geopolitical conflicts, and other factors can lead to severe congestion at certain ports, necessitating timely adjustments to vessel schedules by shipping companies. Moreover, such events can impact the marine fuel market, prompting shipping companies to adopt strategies such as increasing vessel numbers and reducing vessel speeds in response to high fuel prices. Additionally, the container handling efficiency selection mechanism based on information sharing enables shipping companies to flexibly design liner schedules, balancing the economic costs and service reliability of container liner transportation. [ABSTRACT FROM AUTHOR]

This study addresses the critical need for effective resource management through software container migration in cloud data centers. It emphasizes its role in avoiding resource shortages and reducing energy consumption in cloud environments. This study introduces a novel multi-objective integer linear programming (ILP) approach for software container replacements, complemented by a specialized algorithm designed to migrate software containers between over- and underutilized hosts to enhance efficiency compared to traditional optimization methods. The simulation results demonstrate the algorithm's effectiveness, validating its potential for optimizing energy usage and resource allocation in cloud environments. Statistical analyses confirm the proposed model's and algorithm's superiority over benchmark approaches, highlighting their potential for enhancing resource management in cloud computing systems. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, allocation plan of ventilators is reinforced for fulfilling its critical needs to the hospitals. The death rate caused by COVID-19 could have been diminished globally if the proposed medical logistics network is possible to be timely deployed between suppliers and demand regions. Owing to operate and organize medical logistics network and to minimize the total delivery time (TDT), we have formulated a mathematical model based on mixed-integer non-linear programming (MINLP). The application of the proposed model is considered employing the real-life pandemic data, which is alarmed to the severely affected regions in India. The results denote that the anticipated model of medical logistics network efficiently performs in a pandemic demand scenario. The responsiveness of the developed model is also examined under three demand scenarios based on Covid-19 pandemic wave-1, wave-2, and wave-3. Lastly, the sensitivity analyses are done to examining the impact of the critical parameters of the model, and findings are discussed. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused severe disruptions throughout global supply chains. In response to this situation, container carriers had been cancelling services and port calls. The reasons behind the cancellations were diverse: restoring schedule reliability; coping with sudden demand decreases; or with severe port congestion. To fully understand the implications of this practice, it is crucial to have a robust measurement method. This paper presents a novel method to estimate the incidence of port call cancellations based on AIS data. A normal service is first defined, on the basis of the most frequent port call sequence, and deviations are measured subsequently. As a first glance at the unique value of this method, we apply it to the ports along the Europe-Far East route. A binomial logistic model expresses the probability a port is skipped, based on its own characteristics, the size of vessels, and the region in which the port is located. We find non-trivial effects related to vessel size. At the largest end of the vessel size scale, the ports attracting mega vessels (with a capacity above 15 K TEUs) were less affected by cancellations in 2018–2019. This relationship reversed during the 2020–2021 period, and handling mega vessels seemed to have become a burden for ports during the pandemic. Another important result of this study is that, before the COVID-19 pandemic, the rate of cancellations was much more uneven between world regions than after the COVID-19 outbreak. This study provides useful operational insights to port authorities and governments, enabling them to anticipate the effects of future crises. [ABSTRACT FROM AUTHOR]

The advent of container technology has emerged as a pivotal solution for application developers, addressing concerns regarding the seamless execution of developed applications during the deployment process. Various low-level container runtimes, including runC, Kata Container, and gVisor, present themselves as viable options for implementation. The judicious selection of an appropriate low-level container runtime significantly contributes to enhancing the efficiency of Kubernetes cluster utilization. To ascertain the optimal choice, comprehensive testing was conducted, encompassing both performance and security evaluations of the low-level container runtimes. This empirical analysis aids developers in making informed decisions regarding the selection of low-level container runtimes for integration into a Kubernetes cluster. The performance assessments span five key parameters: CPU performance, memory utilization, disk I/O efficiency, network capabilities, and the overall performance when executing an nginx web server. Three distinct tools—sysbench, iperf3, and Apache Benchmark—were employed to conduct these performance tests. The findings of the tests reveal that runC exhibits superior performance across all five parameters evaluated. However, a nuanced consideration of security aspects is imperative. Both Kata Container and gVisor demonstrate commendable host isolation, presenting limited vulnerability to exploitation. In contrast, runC exposes potential vulnerabilities, allowing for exploits against the host (worker node), such as unauthorized directory creation and system reboots. This comprehensive analysis contributes valuable insights for developers, facilitating an informed decision-making process when selecting low-level container runtimes within a Kubernetes environment.

This study explores the transformative role of containerized microservices in the sphere of mobile application development, especially within public cloud ecosystems. It focuses on how technologies such as Docker and Kubernetes contribute to improving deployment, scalability, and overall management of mobile applications, with an emphasis on containerizing backend services. We analyze their efficiency in streamlining deployment processes, focusing on how they improve the application’s performance and reliability. Additionally, we examine various alternative deployment strategies, such as blue-green, rolling, and canary releases, to emphasize their effectiveness in minimizing risks and facilitating smooth transitions in dynamic cloud environments. The study takes a comprehensive approach to achieve this goal, which includes a systematic review of existing literature, a thorough examination of relevant use cases, and an assessment of open-source technologies. Our findings reveal not only the practical benefits of these strategies but also their strategic application, offering important insights for software engineers and decision-makers. This study emphasizes the significance of integrating and optimizing containerized microservices in mobile app development to achieve more efficient, scalable, and manageable application lifecycles on cloud-based platforms. [ABSTRACT FROM AUTHOR]

The coal loading operation of the coal preparation plant of an open pit coal mine causes chaos in coal mine vehicle scheduling due to the unreasonable arrival times of outgoing and container transportation vehicles. To further reduce the length of time that vehicle transportation equipment waits for each other and to reduce the total cost of container transportation, the optimisation model of container transportation vehicle scheduling in an open pit coal mine is constructed to minimise the minimum sum of the shortest time of container reversal and the lowest cost of container transportation. To accurately measure the total cost of container backward transportation, waiting time and unit waiting time cost parameters are introduced, and the total cost of container transportation is measured using the transportation cost and the waiting time cost transformation method. An improved grey wolf algorithm is proposed to speed up the convergence of the algorithm and improve the quality of the solution. When employing the genetic algorithm (GA) and grey wolf optimisation algorithm (GWO) for optimising the scheduling of container transport vehicles in coal mines, it is noted that while the GA can achieve the global optimum, its convergence speed is relatively slow. Conversely, the GWO converges more quickly, but it tends to be trapped in local optima. To accelerate the convergence speed of the algorithm and improve the solution quality, a hybrid GA−GWO algorithm is proposed, which introduces three genetic operations of selection, crossover, and mutation of GA into the GWO algorithm to prevent the algorithm from falling into the local optimum due to the fall; at the same time, it introduces hunting and attacking operations into the elite retention strategy of GA, which improves the stability of the algorithm's global convergence. Analysis indicates that, compared to SA, GWO, and GA, the hybrid algorithm enhances optimisation speed by 43.1%, 46.2%, and 43.7%, increases optimisation accuracy by 4.12%, 6.1%, and 3.2%, respectively, and reduces the total container reversal time by 35.46, 22, and 31 h. The total cost of container transportation is reduced by 2437 RMB, 3512 RMB, and 1334 RMB, respectively. [ABSTRACT FROM AUTHOR]

Sea-rail intermodal transportation around the globe faces complex challenges that affect the satisfaction of shippers' needs. An efficient cargo flow between the port and its hinterland depends particularly on efficient connectivity between the seaport and rail. Sea-rail intermodal can be a cost-efficient and green alternative to unimodal road transportation. Inefficient sea-rail connectivity in the seaport slows cargo flow and affects port capacity. Various factors could affect the system's efficiency and create bottlenecks in the system. A case study adopts a discrete event-based simulation approach to assess bottlenecks in the sea-rail connection that affect cargo flow and generate congestion. The data were collected from the Port of Trois-Rivières, the focus of our investigation. Our objective is to identify bottlenecks in the sea-rail intermodal system in the port, identify strategies to mitigate bottlenecks and accelerate cargo flow. To this end, we examined various scenarios, including an increase in the share of trains for cargo transportation and an increase in the number of daily train convoys. The findings underscore that elevating the train share to 40% and introducing two daily train services yield significant enhancements in key performance indicators. Noteworthy advantages encompass a reduction in the average time ships spend in the port, a decrease in the average waiting time for trains to depart from the port, an overall improvement in cargo handling efficiency within the port, and a notable alleviation of bottlenecks within the system. [ABSTRACT FROM AUTHOR]

To address the issue of unreasonable route planning in modern logistics intelligent distribution, a comprehensive, efficient, and practical path planning method is provided by modern logistics intelligent distribution. This study takes a comprehensive multimodal transportation perspective between cities as its starting point. Firstly, a physical network system model describes logistics distribution's fundamental physical network elements. Additionally, a logistics service network system model is established to depict the service network elements of logistics distribution. These two models are then combined. Building upon this foundation, variables such as risk and cost in logistics path planning are examined and considered, leading to the design of a logistics path planning model. Simulation analysis is employed in this study for data analysis. Analyzing section utilization and planning results reveals key information in logistics path planning. Segment 4 exhibits the lowest utilization rate at 46.2%, while Segment 7 demonstrates the highest utilization rate at 100%. This indicates that the model favors segment 7 as the primary path due to its superior physical facility elements and road conditions. By integrating section planning with cost and risk considerations, the optimal planning path is determined to be 1–3–6–7, encompassing sections 2, 7, and 10. This plan is based on the analysis results of the model and is considered the most economical and safe logistics path after comprehensive deliberation. The designed green logistics path planning model for the physical network system enables comprehensive planning of combined logistics routes, thereby enhancing the distribution path. The studied and designed green logistics path planning model of the physical network system allows for a comprehensive perspective in planning combined logistics traffic paths, resulting in improved distribution paths. This presents a novel and effective path-planning method for modern logistics intelligent distribution, accomplishing the research objectives and providing important references for subsequent related research. [ABSTRACT FROM AUTHOR]

With the rapid development of global industrialization, the consumption of refined oil is at a high level. The scale of refined oil transportation is huge. For refined oil sales enterprises, determining a method for making reasonable transportation plans and reducing transportation cost has become an important means to improve competitiveness. The current method of developing a primary logistics plan does not consider the characteristics of various transportation modes, which can lead to an increase in overall costs. The logistics system in this case has multiple transportation modes, including road, rail, and pipeline. In addition, a multiproduct pipeline has multiple injection points. For such a complex system, this work unifies the time dimension of continuous transportation by pipeline and discrete transportation by rail and road based on the idea of multimodal transportation. This paper couples a pipeline scheduling submodel and a logistics optimization submodel to construct a daily scheduling model for refined oil which is close to the actual operation in the field. This model was validated using a refined oil logistics system in China, and the results show that the optimized logistics solution decreased total transportation costs by 14.5% compared with the actual cost. This model can serve as a guide for actual operations. [ABSTRACT FROM AUTHOR]

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This research stems from the urgent need to address the complex challenges faced in multimodal transportation within the cargo sector, particularly concerning the reduction of transportation costs and CO 2 emissions. Existing transportation systems often struggle to efficiently balance economic and environmental concerns, necessitating innovative solutions. In this paper, we have introduced a novel mathematical model that integrates Singular Value Decomposition (SVD), Genetic Algorithms (GA), and the Adaptive Neural Fuzzy Inference System (ANFIS). The synergy of GA and the ANFIS forms a dynamic learning system that continually refines solutions by adapting to historical decisions and aligning with supply-demand dynamics. This integrated approach aims to optimize cost allocation and resource utilization while simultaneously minimizing CO 2 emissions. Deploying the ANFIS+GA+SVD model in multimodal scenarios yields impressive results, with a minimal average percentage error (MAPE) of 0.28% for transportation costs and a remarkable coefficient of determination R2 of 0.992 for the Truck + Ship scenario. Similarly, a MAPE of 0.11% for CO 2 emissions, supported by an impressive R2 of 0.989, underscores the model's accuracy. Observations of people's transportation choices in response to emission price changes reveal a significant decrease in truck usage, plummeting from 84% to below 50%. Alternative transportation methods, especially in the 3S-3 M-3 C scenario, show a substantial surge, with railways leading at a remarkable 30% increase in utilization. The combined quantity and cost comparison, utilizing the ANFIS+GA+SVD model, emphasizes the suitability of the Truck + Ship scenario for Iraq's transportation network, reinforcing the case for sustainable and efficient multimodal transportation systems. [ABSTRACT FROM AUTHOR]

Engineering multilayer networks that efficiently connect sets of points in space is a crucial task in all practical applications that concern the transport of people or the delivery of goods. Unfortunately, our current theoretical understanding of the shape of such optimal transport networks is quite limited. Not much is known about how the topology of the optimal network changes as a function of its size, the relative efficiency of its layers, and the cost of switching between layers. Here, we show that optimal networks undergo sharp transitions from symmetric to asymmetric shapes, indicating that it is sometimes better to avoid serving a whole area to save on switching costs. Also, we analyze the real transportation networks of the cities of Atlanta, Boston, and Toronto using our theoretical framework and find that they are farther away from their optimal shapes as traffic congestion increases. Finding an optimal shape for transport networks, represented as multilayer structures, is a challenging problem. The authors propose analytical and computational frameworks to analyze sharp transitions from symmetric to asymmetric shapes in optimal networks, that can be applied for planning and development of improved multimodal transportation systems within a city. [ABSTRACT FROM AUTHOR]