Shorter Delivery Time Research Articles

Dosimetric comparison of gamma knife and linear accelerator (VMAT and IMRT) plans of SBRT of Lung tumours

This study evaluates dosimetric differences in Stereotactic Body Radiation Therapy (SBRT) for lung tumors using plans of Gamma Knife, and Volumetric Modulated Arc Therapy (VMAT), Intensity-Modulated Radiation Therapy (IMRT) plans based on Linear Accelerator, aiming to inform the reader of appropriate treatment strategy selection. Ten patients with 23 lung tumor lesions treated with SBRT at Zhongshan Hospital of Dalian University were analyzed. Plans of Gamma Knife, and VMAT, IMRT plans based on Linear Accelerator were created for each lesion, totaling 18 plans per type. Lesions were treated with 30–50 Gy in 5–10 fractions. Dosimetric parameters, including gradient index (GI), heterogeneity index (HI), conformity index (CI), and doses to the plan target volumes (PTVs), the gross tumor volumes (GTVs) and organs at risk (OARs) were compared. Plans of Gamma Knife showed superior HI and GI, higher PTV and GTV doses, and reduced doses to the ipsilateral and contralateral lungs, esophagus, spinal cord, and heart compared to VMAT and IMRT plans (p < 0.05). However, Plans of Gamma Knife required longer delivery times. When comparing VMAT and IMRT plans, VMAT plans had shorter delivery times than IMRT plans, but required more monitor units (MUs). Additionally, IMRT plans delivered a lower mean dose to the ipsilateral lung compared to VMAT plans. Gamma Knife SBRT plans achieves steeper dose falloff and minimizes radiation to normal lung tissue compared to VMAT and IMRT plans, but with longer delivery times. VMAT and IMRT plans displayed similar dose distributions for lung SBRT.

The Integration of AI and Machine Learning in Supply Chain Optimization: Enhancing Efficiency and Reducing Costs

One of the biggest issues today is the increasing intricacy of supply chain networks and supply chain networks becoming more global. Following is the research paper on supply chain management accompanying the integration of AI &amp; ML for effectiveness &amp; efficiency &amp; reduction of cost impacts. The purpose of the research is to assess the effectiveness of adoption of Artificial Intelligence and Machine Learning tools based on predictive analytics, automation, and real-time decision models with supply chain management tendencies in demand forecasting, inventory control, and logistics. In line with the research design that is mixed method, the data were obtained from high-impact case studies and industry reports with further support from the literature review. The usefulness analysis of AI and ML was conducted in line with the supply chain performance measures that include lead time, cost and system metrics for the actual implementation. The results suggest that the application of AI and ML leads to the key performance improvement in companies, such as an average of 20% decrease in operational costs and 15% shorter delivery times. The study will also provide a new understanding of the real world incorporation of AI and ML in supply chain and a path forward in the literature and practice. These technologies reveal the development prospect of how these supply chains can be rebuilt to be more robust as well as flexible.

Express shipments with autonomous robots and public transportation

Growing urbanization, exploding e-commerce, heightened customer expectations, and the need to reduce the environmental impact of transportation ask for innovative last-mile delivery solutions. This paper explores a new express shipment model that combines public transportation with Autonomous Robots (ARs) and studies its real-time management. Under dynamic demand arrivals with short delivery time promises, we propose a rolling horizon framework and devise a machine learning-enhanced Column Generation (CG) methodology to solve the real-time AR dispatching problem. The results of our numerical experiments with real-world delivery demand data show the significant potential of the proposed system to reduce travel time, vehicle traffic, emissions, and noise. Our results also reveal the efficacy of the learning-based CG methodology, which provides almost the same quality solutions as the classical CG approach with much less computational effort.

Part feeding scheduling for mixed-model assembly lines with autonomous mobile robots: benefits of using real-time data

ABSTRACT Mixed-model assembly is increasingly widespread to meet customer requirements for customisation and short delivery times. Flexible part feeding systems are required to timely replenish assembly stations with materials, avoid station idle times, and limit inventory levels on the shop floor. Part feeding scheduling is a complex and dynamic problem, affected by processing time fluctuations, equipment failures, and variations of product mix. Although real-time data of factory processes and resources is widely available and can be exploited using a digital twin of the part feeding system, there is a lack of scientific evidence on the benefits of using real-time data in part feeding scheduling. This research addresses this gap by developing an agent-based simulation model of a part feeding system with a fleet of autonomous mobile robots (AMRs) and comparing a real-time dynamic part feeding scheduling approach with static benchmark approaches. Numerical results indicate that using real-time data improves the performance of the part feeding system and the assembly system significantly, and allows improving the trade-off between the AMR fleet size and the total storage capacity on the shop floor, resulting in lower investment costs for AMRs given a certain storage capacity or lower required storage capacity given an AMR fleet.

Optimisation of a multilevel logistics network for prepositioned warehouses under an omni-channel retail model

A new omni-channel retailing mode, prepositioned warehouses, which developed rapidly in China during the COVID-19 pandemic, was introduced, and a three-level logistics network optimisation model for agricultural products was presented. This study takes into account the characteristics of the omni-channel retail model, which combines the two delivery methods of vehicle delivery and consumer in-store purchase, adds the freshness penalty incurred by fresh agricultural products in the storage process of the prepositioned warehouse, and establishes a mathematical optimisation model of the three-stage logistics network of the prepositioned warehouse that meets the characteristics of omni-channel retailing on the basis of the classical EOQ inventory model. In this work, the shortest delivery time and the smallest total cost are set as the optimisation objectives, and the multi-objective problem is transformed into a single objective through normalisation and linear weighting methods to obtain a mixed-integer optimisation model. This paper solves the problems of site selection for prepositioned warehouses, merchandise replenishment strategies, and optimisation of delivery routes based on data from real cases. Additionally, through sensitivity analysis, the management inspiration of the enterprise is obtained.

Efficacy and safety of a novel temperature-controlled catheter for cavotricuspid isthmus ablation.

Maintaining an adequate temperature at the target site is essential for effective ablation. We hypothesized that a tissue temperature-controlled (T-Con) catheter for cavotricuspid isthmus (CTI) ablation could improve the procedural ablation parameters. To evaluate the efficacy and safety of the T-Con (DiamondTemp™) catheter for CTI ablation compared with non-irrigation (Non-Irri) and irrigation (Irri) catheters. We analyzed 150 patients who underwent prophylactic CTI ablation combined with pulmonary vein isolation. The Non-Irri, Irri, and T-Con catheter groups comprised 50 patients each, and the ablation procedural parameters and complications were compared between these groups. There were no significant differences in clinical background characteristics among the three groups. The Kruskal-Wallis and post hoc tests demonstrated that the T-Con group showed the lowest total radiofrequency energy delivery time among the three groups (median [25 and 75 percentiles]: 340 [209, 357], 147 [100, 199], and 83 [61, 109] s, respectively in the Non-Irri, Irri, and T-Con groups; T-Con versus Non-Irri, p < .01; T-Con versus Irri, p < .01). The total procedural time and acute reconnection rate in the T-Con group (264 s and 4%, respectively) were lower than those in the Non-Irri group (438 s and 24%) but were similar to those in the Irri group (268 s and 6%). No significant complications were observed in any group. The T-Con catheter achieved a short energy delivery time and a low acute reconnection rate, indicating its potential as an alternative catheter for CTI ablation.

Angiographic and clinical impact of balloon inflation time in percutaneous coronary interventions with sirolimus-coated balloon: A subanalysis of the EASTBOURNE study

IntroductionPercutaneous coronary intervention (PCI) with drug-coated balloon (DCB) is an attractive strategy for the treatment of obstructive coronary artery disease (CAD). The implantation technique strongly influences the outcome of DCB PCI: accurate and adequate lesion preparation, short delivery time and sufficient DCB inflation time are deemed crucial to warrant adequate drug transfer and mitigate the risk of immediate vessel recoil and flow-limiting dissections. However, the optimal DCB inflation time is unclear, current consensus documents suggesting 30–60 s based on Experts' opinion. However, clinical studies comparing the prognostic role of different inflation times are scarce and mainly involve paclitaxel-coated balloons. In this study we aimed to assess the impact of different inflation times in patients undergoing PCI with a sirolimus-coated balloon (SCB). MethodsWe conducted a post-hoc analysis of the prospective, multicenter, EASTBOURNE study, classified into two study groups according to balloon inflation time: long (>30 s) versus short (≤30 s). The primary endpoint was target lesion revascularization (TLR) at 24-month follow-up. Secondary clinical endpoints were major adverse clinical events (MACE), death, non-fatal myocardial infarction (MI), and BARC 2–5 bleedings. Furthermore, angiographic endpoints (the rate of bailout stenting and post-procedural TIMI flow <3) were also addressed. ResultsA total of 2289 lesions (2092 in the long inflation group, 197 in the short inflation group) were included in the analysis. Median balloon inflation time was 60 s in the long inflation and 30 s in the short inflation group. The two study groups experienced a similar rate of TLR [6.2 % in the short versus 6.3 % in the long inflation group, p = 1.00] as well MACE (p = 0.683), death (p = 0.102), non-fatal MI (p = 0.822), and BARC 2–5 bleedings (p = 0.252). These results were consistent when considering subpopulations with different target lesion phenotypes (in-stent restenosis, de-novo lesions, large and small vessels). Interesting, the rate of bailout stent implantation and post-procedural TIMI flow <3 was higher in the short SCB inflation time, as compared to the standard strategy. ConclusionsShort vs. long SCB inflation time is associated with a higher need of bailout stenting after PCI with SCB, with similar clinical outcomes at 24-month follow-up.

From rush to responsibility: Evaluating incentives on online fashion customers’ willingness to wait

Online retail is characterized by an increasing emphasis on shorter delivery times, which often comes with high logistics costs and a negative environmental impact. Conversely, longer order fulfillment times improve cost and eco-efficiency. This paper investigates whether customers in German-speaking countries are willing to wait longer for their online orders in the fashion, shoes, and accessories sector when offered incentives in return. The research employed an online survey using a within-subjects design. Results indicate that incentives significantly extend customers’ patience. Savings on shipping costs and information on the reduction in CO2 emissions greatly impact willingness to wait. Personal traits like gender, age, eco-awareness, and order urgency affect incentive responses. This demonstrates customers’ readiness to make trade-offs in delivery time and emphasizes the importance of explicitly addressing the ecological impact of delivery. Suggestions for online retailers and logistics include slower delivery choices and customized incentives aligned with target audience and market.

Bayes goes big: Distributed MCMC and the drivers of E-commerce conversion

This work researches the drivers of e-commerce conversion of one of the largest e-commerce companies in the Netherlands. We focus on product page conversion, i.e., the probability that a customer who visits a specific product page also buys the product. This probability differs between products, which we explain by a variety of factors like pricing, delivery times, reviews, seller type, and the quality of the content. Understanding the drivers of conversion is the first step in increasing it, and therefore an important step in increasing overall sales. We describe the process of transforming Big Data into valuable insights using Bayesian statistics and apply a Bayesian Binomial model to a dataset of 15 million records using a distributed MCMC algorithm. In addition, we apply a simple Binomial GLM on a small sample of these 15 million records for comparison. We find that using the full 15 million records results in a significant reduction in the variance of the estimated parameters. In terms of the actual drivers of e-commerce conversion, we observe that products with competitive pricing, short delivery times, and good content all achieve a higher conversion on average. Furthermore, reviews, average review score, and the number of reviews for a product have a large positive effect on conversion compared to other characteristics. This provides a unique insight into what makes people decide whether or not to purchase a product.

Incorporating supply and production digital twins to mitigate demand disruptions in multi-echelon networks

In today's globalized environment, supply chain (SC) and manufacturing operations are intrinsically linked to satisfy consumer demand. Faced with rising preferences for personalized goods, convenience, and price competitiveness, many companies pivot towards e-commerce strategies and product family approaches to offer wider product varieties and shorten delivery times. This is evident in the fast-moving consumer goods (FMCG) industry, where multi-echelon networks are often utilized to optimize inventory and lower costs. While these methods can fulfill consumer expectations, decreased network resilience is a key obstacle, leaving supply networks vulnerable to supply and demand disruptions. Hence, a holistic approach is required to mitigate disruptions impacts in multi-echelon networks. As a digital enabler, digital twin (DT) technology can manage disruptions in SC networks and manufacturing shop floors. However, these solutions typically operate in silos without context considerations, resulting in illogical solutions. To overcome these challenges, this study proposes a novel supply and production (S&P) DT system to enhance resilience and disruption management in multi-echelon networks. A four-tier technology stack is introduced first, then resilience evaluation, SC replanning, and shop floor rescheduling methods are explored. Based on this, a DT-enabled disruption mitigation mechanism is proposed and featured in an F&B-oriented demand spike disruption case study. Results show the role of the hybrid S&P DT system in improving demand fulfillment rate and reducing production make span to enhance operational continuity.

On the fabrication of finer grain structure through using finer wire diameter for wire arc additive manufacturing of AZ31B magnesium alloy

Magnesium (Mg) alloys are the lightest structural metallic material, but it is hard to be applied to various industrial fields due to the crystallographic nature that is composed with hexagonal closed packed (HCP). Wire arc additive manufacturing (WAAM) could be a key to fabricating Mg parts with large sizes and complex designs. It has numerous advantages such as high material efficiency, short-delivery time by faster manufacturing speed, easy to modify, etc. Yet, most of the research mentioned the grain coarsening and degradation in mechanical properties when fabricating WAAM products using Mg alloy wires. To solve the problems, a solution was suggested with supposition that the lessen the deposit metal size a little by changing to the finer wire diameter can reduce the actual heat input and heat accumulation during the WAAM process. In this research, the WAAM of AZ31B has been conducted using a wire diameter of 1.0 mm rather using the 1.2 mm that is used in common. It was found that the finer (about 90 %) and equiaxed microstructure was composing the WAAM product made of wire diameter of 1.0 mm, and it results in about two times higher mechanical strength.

Intelligent Medicine Delivery Robot Trolley for Hospital Admitted Patients

The goal of the research is to enhance hospital patient care through the creation and application of an Intelligent Medicine Delivery Robot Trolley (IMDRT). In order to improve patient happiness, minimise mistakes, and expedite medicine delivery, the IMDRT makes use of cutting-edge technology including robots and artificial intelligence. The research combines human-computer interaction, robotics, and healthcare administration. Initial assessments indicate good feedback, shorter delivery times, and increased accuracy in the administration of medications. Key Words: healthcare, Medicine Delivery, robots and artificial intelligence

Optimizing the traversal time for gantry trajectories for proton arc therapy treatment plans

Background. Proton arc therapy (PAT) is an emerging radiation therapy technique where either the gantry or the patient continuously rotates during the irradiation treatment. One of the perceived advantages of PAT is the reduced treatment time, but it is still unclear exactly how long these treatment times will be, given that no machine capable of its delivery is available on the market at the time of writing. Objective. We introduce the algorithm arc trajectory optimization method (ATOM), which aims to determine an efficient velocity profile for the gantry for rapid delivery of a given proton arc treatment plan. This algorithm could be used to minimize the delivery time of a proton arc plan without changing the plan or updating the machine. Approach. ATOM computes the trajectory with the shortest delivery time while ensuring there is enough time to deliver all spots in each energy layer and switch energy between layers. The feasibility of the dynamic gantry movement was assured by enforcing maximum and minimum limits for velocity, acceleration, and jerk. This was achieved by discretizing the gantry velocity and combining the A* algorithm with the open-source motion generation library Ruckig. The algorithm was tested on a synthetic data set as well as a liver case, a prostate case and a head and neck case. Main results. Arc trajectories for plans with 360 energy layers were calculated in under a second using 256 discrete velocities. The delivery time of the liver case, the prostate case and the head and neck case were 284 s, 288 s and 309 s respectively, for 180 energy layers. Significance. ATOM is an open-source C++ library with a Python interface that rapidly generates velocity profiles, making it a highly efficient tool for determining proton arc delivery times, which could be integrated into the treatment planning process.

Update on the advances and challenges in bioequivalence testing methods for complex topical generic products.

Most of the government regulatory agencies, including the United States Food and Drug Administration and the European Medicine Agency, demand that the generic complex topical products prove pharmaceutical and bioequivalence. The evaluation of bioequivalence for complex topical dermatological formulations is a challenging task that requires careful consideration of several factors. Although comparative clinical studies are still considered the gold standard approach for establishing bioequivalence in most formulations, these studies can be costly and insensitive to detect formulation differences. Therefore, significant efforts have been made to develop and validate alternative approaches that demonstrate bioequivalence and expedite the availability of high-quality generic topical dermatological products. This article reviews the current methods for determining the bioequivalence of topical formulations in humans, with particular emphasis on recent advances in these methodologies. Most of the alternative methods are sensitive and reproducible, with the capability to ease the financial burden of comparative clinical studies within a short delivery time. The limitations associated with each technique are reviewed in detail.

Customisation and payload integration of hexacopter for enhanced grocery delivery

The research paper aims to customize the DJI F550 drone for efficient grocery deliveries by incorporating an electrically controlled hook and integrating set coordinates, providing a systematic and precise framework for the drone's navigation and delivery operations. The objectives encompass constructing a hexacopter equipped with a servo hook for payload detachment, creating a mobile application for order management, demonstrating ground operations and communication, and analyzing stress and load-bearing capacity.The primary goal is to automate grocery delivery, ensuring a hassle-free experience for customers in all weather conditions while addressing minimal privacy concerns. The current paper advocates for Urban Logistics, striving to save human resources, reduce customer burden, shorten delivery times, enhance privacy, and minimise delivery risks. Additionally, it explores optimization, including the upgrade of the drone’s battery and remote controller. Stress analysis using CATIA and ANSYS software is presented providing valuable insights into the structural integrity of the drone, revealing key considerations for enhancing performance and reliability. Conclusively, this paper investigates future scalability and commercial applications, ultimately proposing a fully functional drone-based grocery delivery system to revolutionise traditional shopping experience.

Bibliographic review of AI applied to project management and its analysis in the context of the metalworking industry

Competition between companies, especially in the metalworking industry, has driven the continuous search for strategies that aim to reduce costs, improve product quality, and shorten delivery times. However, a persistent challenge that these industries face is delivery delays, caused by a number of complex factors, including supply chain issues, project management difficulties, technical and work issues.To address this issue, several approaches and methodologies have been adopted. On the one hand, agile management tools such as Scrum, Prince2 Agile, Lean Project Management, Agile Project Management and DSDM (Dynamic Systems Development Method) have been implemented to increase the flexibility and adaptability of production processes. On the other hand, classic analytical solutions, such as the Gantt/Waterfall Method, PMBOK Guide, Spiral Model, Prince2 and the Critical Path Method (CPM) and/or the PERT method, have been applied to ensure a clearer and more structured view of projects.In addition to these traditional approaches, the use of Artificial Intelligence (AI) has emerged as a promising tool in project management. A detailed analysis of articles reveals a marked preference for Machine Learning (ML), with particular emphasis on Artificial Neural Networks (ANNs). These AI techniques have been successfully applied in a variety of projects, especially in civil construction, helping to predict and mitigate delays, improve operational efficiency and optimize resource allocation.Despite the progress made, research on the use of AI in project management still faces significant challenges, as reflected in the scarcity of available studies on the topic in project management in the metalworking industry.

Effect of Early Amniotomy on Delivery Outcome with/without Cervical Ripening: A Systematic Review and Meta-Analysis.

Although shorter labors are the benefits of Early Amniotomy (EA), it may lead to risks such as non-reassuring fetal testing and cesarean delivery. Also, the effect of cervical ripening to induce labor before amniotomy is unknown. This systematic review and meta-analysis evaluated the effect of EA on the delivery outcome with or without cervical ripening. Bibliographic search was conducted without time limit until June 2020. PubMed, Scopus SID Magiran, Cochrane Library Science website, and ISI databases were searched with English and Farsi keywords, including amniotomy, delivery, induced, and pregnancy outcome. The meta-analysis on ten clinical trials showed that the incidence of cesarean section was lower (0.89% VS 0.94; relative risk, 0.85; 95% confidence interval, 0.55-1.30) compared to the group without cervical ripening, and the time to induce labor was approximately 55 minutes (mean difference, 0.91 hour; 95% confidence interval, -1.43 to - 0.33). If EA is performed in women after cervical ripening, the incidence of cesarean section will not increase, and the duration of labor will be reduced. A shorter delivery time is associated with perinatal benefits and greater maternal satisfaction. Furthermore, EA with cervical ripening may reduce monitoring time in busy hospitals with limited medical staff.

Искусственный интеллект в глобальной логистике

In the article, through theoretical analysis and research of logistics development indicators in modern companies, the importance and features of the introduction of artificial intelligence are determined. Artificial intelligence (AI) is considered one of the most revolutionary technologies in modern history. Enterprises use AI to optimize their resources and improve operational efficiency. For companies wishing to introduce new technologies, artificial intelligence offers many advantages. AI has wide applications in supply chains and logistics. It is applied using many different applications that are used all over the world. The desire to remain competitive leads enterprises to decide to use more high-precision, modern equipment to improve production operations. The industry leaders are the companies that apply the latest technologies most decisively. Innovative solutions increase the efficiency of management tasks, which include prompt order fulfillment, improved inventory accuracy, shorter delivery times and more accurate forecasting models. The study helped to identify not only the advantages and benefits of implementing AI in enterprises, but also possible risks. A thorough examination has been conducted on the digital transformation within the multimodal transport corridors of the Republic of Kazakhstan. This transformation is intended to be implemented through cutting-edge technological advancements and adherence to standards, in collaboration with the business community encompassing trade, IT, and the transportation sector. Using SWOT analysis, the consequences of the introduction of AI have been studied both at the mycological and macrological levels of enterprise functioning. Based on all the studied data, the indicators that a company must have in order to implement an innovative technology called Artificial Intelligence are presented.

Impact of additive manufacturing and parametric design on the structure and economic efficiency of construction supply chains

Additive manufacturing and parametric design are anticipated to unfold potentially a strong momentum on costs regarding labor and material savings, as well as elevating automation and productivity within the construction industry. However, research on linking these technologies for use in the construction industry and analyzing the impact on the supply chain structure is scarce. Information in view of challenges regarding technical feasibility and requirements is limited. This paper aims to analyze the technology induced structural change of a construction supply chain and its thereby inherent influence on value creation and economic efficiency. A systematic literature review and a case study were conducted for understanding the impacts of combining additive manufacturing and parametric design. The impact on the structure of the supply chain is product specific, depending on the technology used to design, manufacture and automate as well as material performance, regulatory and technical requirements. Process steps can be encapsulated in technical modules driven by the digital characteristics of the two technologies. There are positive effects on (re-)working time, material consumption, waste reduction, strengthened innovative capacity and shorter delivery times, resulting in labour and cost optimization.

РОЛЬ ИНФОРМАЦИОННЫХ СИСТЕМ В ОПТИМИЗАЦИИ ЛОГИСТИКИ И ЦЕПОЧЕК ПОСТАВОК

With accelerated globalisation and ever-changing market conditions, effective logistics and supply chain management is becoming a critical success factor for companies of all sizes. Information systems enable more accurate demand forecasting, inventory optimisation and shorter delivery times, which ultimately contribute to the competitiveness of enterprises. An important component of effective logistics supply chain management is the integration of digital technologies. The research conducted analyses five key technologies – CRM, SCM, APS, MES and MRP, identifying their advantages and disadvantages. The impact of artificial intelligence in optimising logistics processes is also explored. The article also examines the process of global digitalisation in the logistics sector, including the adoption of the latest technologies and an analysis of possible threats to data security. Successful examples of the use of these technologies in various logistics companies are highlighted, and recommendations are given on the development of information technology infrastructure of enterprises and support for innovative projects in the field of logistics for effective adaptation to the rapidly changing digital requirements of society.