Operational Service Research Articles

Developing a severe gust load spectrum for transport aircraft based on measured load data

PurposeDeveloping severe load spectrum of transport aircraft structures is crucial for enhancing the fatigue damage correlation between full-scale fatigue testing results and operational service. The lack of consensus on severe spectrum development methods for transport aircraft has prompted the current research, resulting in a proposed approach for a severe gust load spectrum based on the acceleration cumulative exceedance surface.Design/methodology/approachThe measured load data were analyzed using a model based on the cumulative exceedance number surface to describe the variation in exceedance numbers. An improved sampling method based on multivariate Markov Chain Monte Carlo was employed to obtain the fleet fatigue damage distribution, enabling the determination of the severity of severe spectrum and the corresponding cumulative exceedance number surface, and a severe gust load spectrum was developed based on the surface.FindingsThe method that characterizes load spectrum variation using the cumulative exceedance surface minimizes the randomness of peak-trough pairs by incorporating the correlation of load spectrum peaks and troughs. This approach reduces the variation in fleet fatigue damage, thereby lowering the requirements for the severity of severe spectrum fatigue damage.Originality/valueThe proposed methodology extends from a one-dimensional curve to a two-dimensional surface, accounting for the correlation between peak and trough values to develop a severe spectrum. This approach more accurately describes the variation in acceleration cumulative exceedance numbers, directly benefiting fatigue damage calculation. This study provides valuable references for developing severe spectrum for transport aircraft.

A probabilistic forecast for multi-year ENSO using bayesian convolutional neural network

Abstract A robust El Niño Southern Oscillation (ENSO) prediction is essential for monitoring the global climate, regional monsoons, and weather extremes. Despite dedicated efforts spanning decades, the precise prediction of ENSO events through numerical modelling beyond a couple of seasonal lead times remains a daunting challenge. The advent of deep learning-based approaches marks a transformative era in climate and weather prediction. However, many machine learning-based studies attempting ENSO prediction are confined to singular estimates, lacking adequate quantification of uncertainty in learned parameters and overlooking the crucial need for a nuanced understanding of ENSO prediction confidence. Here, we introduce a deep learning-based Bayesian Convolutional Neural Network (BCNN) model that provides robust probabilistic predictions for ENSO with a lead time of up to 9-10 months across all seasons. The Bayesian layers within the convolutional neural network maintain the capability to predict a distribution of learned parameters. Augmented with bias correction, our model reproduces the amplitude of the Nino 3.4 index with fidelity for lead up to 9-10 months. The inherent capacity for uncertainty modelling enhances the reliability of BNNs, making them particularly valuable in operational services. This research holds substantial socio-economic implications as it enhances our forecasting capabilities and rigorously quantifies forecast uncertainties, providing valuable insights for planning and policymaking.

Lessons learnt from a real-time attribution and contextualisation trial in a National Meteorological and Hydrological Service

Abstract When a record hot month occurs, timely and credible attribution and contextualisation information can enhance public understanding and future preparedness. This is particularly effective if provided in real time by a National Meteorological and Hydrological Service (NMHS). Many NMHSs are working to integrate research-based attribution methods into their operational services. In this study, researchers and climate service staff collaborated to assess the feasibility of delivering such information swiftly and aligned with standard NMHS data and procedures. The record warm July (winter) temperatures of Tasmania, Australia in 2023 were chosen to illustrate the trial. Rapid results were available three days after the event. Approximately half of the unusual warmth was attributed to climate change, with the likelihood of breaking the previous record at least 17 times higher in the current climate compared to a stationary pre-industrial climate (14% vs. 0.4%). The warming trend became evident in the 1980s, and by 2060, average July temperatures in Tasmania match the record temperature of July 2023 under a high emissions scenario. However, average July minimum temperatures were not well modelled, necessitating the addition of a higher-resolution forecast-based attribution method. In subsequent analysis, almost all the forecast temperature anomaly, and reduced storm activity, was attributable to climate change. Statistical analysis revealed that a weak El Niño partly offset the unusual warmth. To expedite these additional approaches, information drawn from real-time forecasts could be used. Lessons learnt from this trial include technical improvements to align better with NMHS protocols including using consistent datasets and baselines, and refining and automating the method suite. Logistical and communication enhancements included training staff to run the suite, improving communication materials, and developing delivery channels. These learnings provide key considerations for NMHSs as they move towards providing timely and credible climate attribution and contextualisation information as part of their operational services.

Risk Management Strategy Implementation for Business Plan at Material Handling Rental Services Startup LogisMe

PT. LogisMe Berkat Bersama (LBB) is a Limited Liability Company (PT) engaged in the business of providing Business-to-Business (B2B) warehousing and logistics solutions. LogisMe has the tagline "One Stop Logistics Solutions" with end-to-end solution coverage starting from the provision of warehousing and logistics operational equipment such as forklifts, material handling and cleaning equipment, provision of spare parts and technicians in order to meet the maintenance needs of these operational equipment, warehouse racking design, warehouse and logistics operational consultant services to the provision of a digital platform for monitoring units rented by customers in real-time. This document outlines the implementation of a risk management strategy for the business plan of LogisMe, a Material Handling Rental Services startup company. The framework adopted is based on the ISO 31000:2018 standard and focuses on integrating risk manage. the report is returned to the risk mitigation supervisor to be checked that it has met the requirements and stored in the company's cloud repository, disseminated to all employees and company stakeholders. Monitoring is carried out to understand and prevent risks that occur as a strategy to gain a competitive advantage for the company.

Research on New Spatial Data Infrastructure Supports the Circulation of Geographic Information Data Elements

Abstract. Data is an important factor of production in the digital economy era and an important support for building a new development pattern. As an important part of data resources, geographic information data is also an important new factor of production, which plays a unique role in the construction of digital China and economic and social development. From the perspective of releasing the value of data elements, data infrastructure is a new type of infrastructure that provides integrated data aggregation, processing,circulation, application, operation, and security services for society under the support of network, computing power and other facilities. It is an organic whole covering hardware, software, open source protocols, standards, and mechanism design. Spatial data infrastructure (SDI) is a collection of technologies, policies and systems that realize the access and access of spatial data. Through the construction of new spatial data infrastructure (NSDI), it supports the circulation of geographic information data elements,making it an important part of data infrastructure. Firstly, this paper gives the connotation of geographic information data elements circulation. Secondly, it proposed to construct the circulation system of geographic information data elements on the basis of enumerating and summarizing the spatial data infrastructure, in order to better support the construction of digital China and economic and social development. Thirdly, the basic requirements, technical indicators and overall architecture of NSDI are designed. Finally, the opportunities and challenges faced by the circulation of geographic information data elements were summarized, mainly focusing on the aspects of policy mechanisms and security systems. At the end of the paper is a summary and acknowledgments.

Dynamic Analysis of Metamorphic Mechanisms with Impact Effects During Configuration Transformation

Metamorphic mechanisms have attracted considerable attention owing to their capability to switch their topology to adapt to different operational tasks. One feature of topological change is the re-contact of different bodies, which inevitably causes collisions affecting operation accuracy and service life. Consequently, in this study, a collision incidence matrix was introduced to describe the topology of a system involved in collisions, and a method for reducing the closed-loop system to an open-loop system was proposed. The complex movement of the metamorphic mechanism in a changing topology was classified into two different running stages of the source metamorphic mechanism. Based on the relative coordinate method, dynamic modeling of the source metamorphic mechanism considering the impact effects was conducted. Combining the classical collision theory and Newton–Euler equation, the generated impact impulse and the motion after collision were determined. Subsequently, a dynamic analytical method for the full configuration of metamorphic mechanisms was proposed to reflect the changes in the topological structure in the dynamic model. Finally, two typical metamorphic mechanisms used in packaging and spinning were considered as examples to verify the correctness and effectiveness of the proposed method, and their impact characteristics during configuration transformation were analyzed. The proposed analytical method of internal impact for a variable topology process provides effective theoretical guidance for the stability analysis of configuration transformation and structural design aimed at minimizing impacts.

Research on cargo volume prediction and personnel scheduling optimization of sorting center based on BP neural network and improved genetic algorithm

With the development of Internet technology, e-commerce has become the primary choice for consumers to shop, and the huge order transaction volume has generated logistics problems. As an important part of the logistics network, the sorting platform is essential to improve its management efficiency. To enhance the efficiency of the sorting center, the topological relationship diagram is used to show the logistics network, and the cargo volume of the sorting center in the next 30 days and every hour is predicted based on the BP neural network model. Based on the mixed integer linear programming model and the improved genetic algorithm SCAGA model, the total man-days are minimized to ensure that the cargo volume is processed. At the same time, to ensure that the actual hourly efficiency is as balanced as possible, the optimization problem of personnel scheduling is studied. To maintain the efficient operation of the logistics industry, the accurate prediction of the number of goods and the reasonable scheduling of personnel, to provide a set of efficient and economical personnel scheduling programs for the logistics company, thus improving the overall operational efficiency and service quality.

Talent Management for Enterprises in Guangdong–Hong Kong–Macao Greater Bay Area: Analysis of Current Situation and Suggestions for Countermeasures

In response to the above findings, this study concludes that enterprises should strengthen the construction of talent echelon and cultivate talents adapted to the development of the enterprise through the combination of industry-university-research and emphasizing the development of innovation ability. Secondly, enterprises need to build a scientific talent evaluation system, oriented by ability and performance, and utilize talent evaluation techniques to ensure the fairness and accuracy of evaluation. In addition, enterprises should design career development channels to provide employees with opportunities for growth and promotion, as well as improve the working environment and provide competitive compensation and benefits to attract and retain talents. In terms of talent planning, enterprises should adopt a long-term perspective and develop talent planning that meets more than three years of business operation services to cope with the uncertainty of the future market. Finally, enterprises should strengthen communication with employees, establish a transparent feedback mechanism, and enhance employee job satisfaction and loyalty by empowering employees and high-level consensus. These countermeasures help enterprises build an effective talent management system and enhance competitiveness.

The role of indigenous healers in treating surgical conditions in the rural Eastern Cape of South Africa.

Indigenous knowledge healers (IKHs) provide alternative healthcare to formal health services in rural South Africa, but there is a gap in knowledge regarding their treatment of surgical conditions. This study evaluated IKH surgical care and described their perspective of the dual health system. A cross sectional survey of IKHs in the Madwaleni Hospital catchment of the Eastern Cape, South Africa was conducted. Topics included the training and experience of IKHs, treatment of nine common surgical conditions, referral patterns, disease origin beliefs, benefits and limitations of care, and collaborative opportunities between the two health systems. Thirty-five IKHs completed the survey. IKHs were consulted by persons with all nine surgical conditions. The most common forms of treatment were application of an ointment on the affected site (88%) and oral medication (82%). Operative treatment was only done for abscess. Referrals to the formal healthcare sector were made for all surgical conditions. IKHs reported that they were limited by their lack of training and resources to perform operations. On the other hand, they perceived the treatment of the spiritual aspect of surgical disease as a benefit of their care. Thirty-five (100%) IKHs were interested in closer collaboration with the formal health sector. IKHs treat surgical conditions but refer to the formal health sector when diagnostic and operative services are needed. More research is needed to determine the potential advantages and disadvantages between the formal health sector and IKH collaboration.

Electronic Docket Adoption in Rural Police Stations

The adoption of electronic record management systems in rural policing presents significant challenges, impacting case management and service delivery. This study investigates the barriers hindering the implementation of electronic docket systems in rural police stations, focusing on the Amatole District Municipality in the Eastern Cape, South Africa. Data from 37 police officers across six rural stations was analysed using descriptive statistics and machine learning techniques. The descriptive statistics results reveal that while a considerable number of officers acknowledge the potential benefits of electronic docket systems, manual case docket management persists due to various barriers. The decision tree model analysis achieved promising results with an accuracy of 75%, precision of 100%, recall of 66.7%, and an F1 score of 80%. These results revealed that usability and perceived efficiency are key predictors of adoption. The study suggests strategic interventions to enhance operational efficiency and service delivery in rural police stations.

The Impact of Artificial Intelligence in The Financial Sector: Opportunities and Challenges

This article explores the transformative impact of Artificial Intelligence (AI) in the financial sector, highlighting its various applications and associated challenges. Through a detailed analysis, key technologies such as machine learning, computer vision, and natural language processing are examined, which have revolutionized critical areas such as customer service, risk management, and financial advising. Although AI has significantly improved operational efficiency and service personalization, it also presents important risks and limitations, such as data privacy, lack of model interpretability, and cybersecurity. Additionally, the article addresses future forecasts for AI adoption in finance, suggesting that its integration will continue to expand, driven by technological advances and regulatory improvements. However, this expansion must be balanced with adequate oversight to mitigate potential risks and maximize benefits. Overall, this study offers a balanced view of the opportunities and challenges that AI presents for the future of global finance.

Degradation law of bond strength of reinforced concrete with corrosion-induced cracks and machine learning prediction model

During long term operation service, reinforced concrete (RC) structures are subjected to corrosive media such as chloride salts, which results in the degradation of interface bond performance, subsequently reducing the load bearing capacity and service life of the structures. To accurately assess the durability of RC structures in environments such as oceans and salt lakes, this study combines experimental methods with machine learning (ML) techniques to explore the degradation patterns and predictive models of bond performance under chloride salt corrosion. Accordingly, a series of central pull-out tests were conducted based on an electrochemical accelerated-dry-wet cycle corrosion regime to investigate the degradation patterns of bond performance between corroded steel bars and concrete under different corrosion degree and corrosion-induced crack widths. By comparatively analyzing the evolution patterns of cracks and the morphology of the steel bars in reinforced concrete specimens and their failure modes, and integrating the mechanical degradation mechanism with chemical and microscopic corrosion mechanisms, an in-depth analysis of the corrosion-induced degradation patterns at the bond interface was performed. The internal relationships between corrosion degree, corrosion-induced crack width and bond strength were discussed, and the variation patterns of bond strength with corrosion-induced crack width and corrosion degree were explored. Moreover, the bond strength patterns were analyzed from an energy perspective. The results indicate that bond strength and bond energy decrease with an increase in the corrosion-induced crack width of the concrete. Additionally, based on the experimental data from this study, ML predictive models for bond strength were established using corrosion-induced crack width as the control variable. The results show that the ML-based bond strength predictive models fit well with the experimental data, offering higher accuracy and reliability compared to traditional bond strength models. The research findings provide significant theoretical basis and practical guidance for safety assessment, maintenance, reinforcement, and full-life design of corroded RC structures.

Perancangan Sistem Informasi Toko Pak Kusno Sidorukun Untuk Mempermudah Jual Beli Barang

Modern digital life relies heavily on information systems (IS) to manage data efficiently, make better decisions, and increase productivity. Mr. Kusno's shop faces problems of goods management, finance, and product promotion. This research designs a customized information system to solve those problems, such as easier digital transaction recording, more optimized goods management, and better financial management. The implementation of this system is expected to increase the efficiency and competitiveness of Toko Pak Kusno. The development method used is the waterfall method, which ensures each step is carried out sequentially from analysis, design, implementation, to system testing. The results showed efforts in designing an information system that can improve the operational efficiency and service quality of Toko Pak Kusno in buying and selling goods and store management.

Practical considerations for assessing crew noise exposure in armored vehicles.

Measurement and analysis of the continuous and intermittent noise produced by armored vehicle (AV) platforms, including the output from communications systems as experienced by crew, are necessary for the purposes of exposure prediction, to support the selection of hearing protection and communication devices, and to facilitate assessments of compliance with occupational health and safety legislation. Practical estimation of the personal noise exposure of AV crews requires the assessment of the vehicle, communications and special-to-role activity noise sources, and an understanding of how these sources combine. Procedures are described that consider instrumentation requirements, AV configuration and build standard, operating conditions representative of actual use, the application of speed thresholding to measurements, and derivation of communications noise levels. Real-world examples are given where these procedures have been applied to an in-service tracked AV to estimate crew noise exposure. The procedures and methods presented are a compromise between precision, repeatability, reproducibility, and pragmatism. Measurements of AV noise are expected to be obtained during the commissioning stage of vehicle design, immediately prior to the vehicle being put into operational service and following any major modifications to the vehicle to inform the necessary engineering, administrative, and personal protective equipment control measures.

Delivery optimization for collaborative truck-drone routing problem considering vehicle obstacle avoidance

Drone participating in logistics delivery has gained much concern due to its potential superiority of operational efficiency and service costs. How to optimize the hybrid vehicle delivery trip is still a crucial issue. Besides, complex urban environment and regulatory no-through zones bring more rigorous challenges. The aim is to develop an efficient routing solution that considers obstacles encountered by the truck and drone, ensuring the timely delivery. This paper proposes an efficient deep reinforcement learning to optimize the collaborative truck-drone routing problem (C-TDRP) under vehicle obstacle avoidance trail. Specifically, the trucks deploy a fleet of drones to serve the random distributed consumers subject to minimum delivery cost. Homogeneous drones serve multiple demand nodes per trip with limited energy carrying capacity. We design the C-TDRP formulation with studying the feasibility of obstacle avoidance routing. Owing to the computational complexity of the proposed mathematical model, an intelligent optimization algorithm based on deep reinforcement learning is developed, named pointer network (Ptr-Net), which can capture the position weights associated with network sequence automatically. Through rigorous experimental validations, the results demonstrate that the vehicle energy consumption of final trajectory reduces by more than 42%, even for large-scale delivery scenarios. Our proposed method not only enhances the optimization performance but also lays the foundation for more intelligent and adaptable logistics delivery in complex urban environments.

Pt-Ta microhotplate with low resistance temperature coefficient and low resistance drift

Micro thermal plates are widely used in various sensors in the aerospace and automotive fields. With the reduction of chip size and the development of MEMS, micro thermal plates are also moving towards integration. The device failure caused by the Pt film at high temperatures seriously affects the stability of the sensor, and degradation determines the sensor's operating temperature and service life. In this paper, Pt, Pt-Ta, and Pt-Pd microcalorimetric plates were prepared and analyzed for their sintering behavior, microstructure, electrical properties, and high-temperature stability using co-firing of resistors and alumina substrates. The film layers prepared by co-firing were better matched to the alumina substrate, and the sintered network of the film layers was better. The Pt-10%Ta microthermal plate has a 46% lower resistance temperature coefficient TCR compared to the Pt microthermal plate, and the resistance drift at 800°C is reduced to 2.11%/Day. And even at 1100°C, the Pt-Ta system also has a very low resistance drift -12%/Day (Pt26.6% /Day and Pt-Pd35.18%/Day) with improved high temperature stability. The addition of Ta allows the membrane layer to have fewer voids at high temperatures, reinforcing the high temperature characteristics of the resistance thermometer. It improves the stability and accuracy of the heating plate in high temperature gas sensor applications.

Quantifying the external costs of autonomous on-demand ride pooling services

Mobility On Demand (MOD) services, such as ride-pooling, provide convenient and cost-effective transportation options. While previous studies focused on operational costs and service quality, we take a broader perspective by examining the external costs associated with autonomous ride-pooling services. Incorporating external costs into the design and evaluation of MOD services enables a comprehensive understanding of their impact on the entire urban population, informing effective regulations and incentives. We present an approach for calculating space-varying external costs, accounting for factors like air pollution, climate impact, noise and accidents. These costs are integrated into FleetPy, an agent-based simulation tool for ridesharing analysis and optimization. A case study in Munich uncovers the tradeoffs between external costs, internal costs, and service quality. Our findings suggest that mid-sized vehicles with a three-person capacity strike a balance between energy efficiency and transport capacity. By applying our approach, external costs can be reduced by up to 37%.

Promotion Effects of High-Speed Rail on Urban Development: Evidence from Three Lines in China

Amid the vigorous development of the high-speed rail (HSR) network, local governments in China generally consider the construction of HSR as a crucial task in their regional development strategies. Currently, most provincial capitals and prefecture-level cities in the eastern and central regions of China already have operational HSR services. This study aims to examine a key question: has the objective of local governments to promote urban development through the construction of HSR been effective? The research selects cities along the Beijing–Shanghai, Beijing–Guangzhou, and Harbin–Dalian HSR lines as the study subjects. Based on the principles of proximity and similarity, cities with operational HSR and those without are chosen as the experimental group and the control group, respectively. Following the double difference (difference-in-differences) approach, an advantage index is proposed to systematically evaluate the impact of HSR operation on urban development from three dimensions: population aggregation, economic development, and expansion of construction land. Furthermore, the evaluation results are systematically clustered to identify city types that exhibit different promotional effects in various dimensions. The research findings indicate the following: (1) The promotion effect of HSR on the development of small to medium-sized cities is more reflected in economic growth and construction land growth. (2) The promotion effect of HSR on the development of large cities is more reflected in the growth of the employment population. (3) For smaller or economically less-developed cities, HSR may be detrimental to the aggregation of resident and employment populations. (4) Cities with moderate size and good economic development have the opportunity to use HSR to promote population aggregation. On this basis, combined with the findings above, strategies to promote the coordinated development of high-speed rail construction and urban systems are discussed.

A study on the development of O2O operation service platform using augmented reality

An attempt of sequence selection is made for multiple alignments of transmembrane proteins in order to detect the structural similarity in the protein families. Treatment for the sequence selection, which is based on the pairwise sequence identities, is applied to ten sequence data sets of functional group of transmembrane proteins extracted from SWISS-PROT. The treatment excludes the sequences with low sequence identity from the divergent data sets effectively. Obtained multiple alignments are evaluated using two newly developed indices. The selected sequences, which are well aligned with few inserted gaps, seem to contain enough information for extracting the structural features of transmembrane functional groups.

Identifying psychological risk and resilience in high-risk military personnel

IntroductionCombat exposure has been associated with the emergence of post-traumatic stress disorder (PTSD) symptoms in military personnel; however, not all veterans are negatively impacted by their operational experience. In many...