Supply Delivery Research Articles

Numerical model of a latent heat storage system for biological fluid administration – Development and experimental validation

The use of phase change materials (PCM) for heat storage is progressively increasing, since its effective contribution is continuously being proven. In medical applications the use of PCM can contribute not only to energy savings, but can also be a solution to assure the delivery of medical supplies or even biological fluid administration at the required temperature. In this paper, the development of a numerical model to simulate the warming up process during the administration of a biological fluid is presented. The model uses fundamental energy conservation laws applied to control volumes and the resulting differential equations are solved by means of the explicit finite difference method (FDM). An algorithm for solving the finite difference approximations to the resulting conservation equations was developed, and the reliability of the model was experimentally validated. The achieved results confirm the adequacy of the numerical model to simulate warming up processes during fluids administration. Furthermore, the modelling principle can be extended to other case studies.

Infection prevention and control practices of non-medical individuals in a neonatal intensive care unit: A Donabedian approach

ObjectiveTo describe the infection prevention and control practices of non-medical individuals in a neonatal intensive care unit, using the Donabedian model. DesignWe used an observational descriptive qualitative approach, collecting data over 38 h of non-participant, unstructured observations. An interprofessional observational team recorded their observations. SettingObservations were collected in the neonatal intensive care unit of a regional hospital in Gauteng, South Africa. ParticipantsWe observed non-medical individuals involved in the day-to-day operations related to infection prevention and control in a neonatal intensive care unit, specifically the cleaners and laundry staff, maintenance and technical services, and significant others. Individuals were selectively observed due to their proximity and convenient presence in the neonatal intensive care unit at the time of observation. Measurements and findingsData were deductively analysed and categorised into Donabedian's pre-existing structure- process- outcome framework. Themes that evolved were the absence of policies and procedures, inadequate written information, unsuitable physical layout of the neonatal intensive care unit, non-adherence to hand hygiene, lack of personal protective equipment, poor cleaning practices, poor service delivery and unavailability of documented evidence on cleaning routines, delivery of supplies and infection prevention and control training. Key conclusionsIn the neonatal intensive care unit, non-medical individuals did not adhere to infection prevention and control measures. The absence of standard operating procedures may contribute to cross contamination and an increase in hospital acquired infections. Implications for practiceInfection prevention should be regarded as an interprofessional team effort and requires situational awareness. The infection prevention control practitioner and unit manager should collaborate with support services and significant others to ensure that these individuals have appropriate knowledge about and adhere to infection prevention control practices in a neonatal intensive care unit. The infection prevention control committee should be expanded to include support staff managers. Orientation programmes should be implemented to raise the awareness of non-medical individuals about the important role they play in infection prevention and control.

Strategic leadership, environmental uncertainty, and supply chain risk: An empirical investigation of the agribusiness industry

The present study aims to examine the influence of strategic leadership and environmental uncertainty on supply chain (SC) risk in the agribusiness industry. The notion of SC risk refers to supply risk, delivery risk, and manufacturing process risk, and it is argued that these risks are an outcome of environmental uncertainty. This study postulates that strategic leadership mitigates the detrimental effect of environmental uncertainty on SC risk. On the basis of data collected from 227 agribusiness-related firms, the analysis was performed using partial least squares structural equation modelling (PLS-SEM) to test the hypothesized relationships. The findings suggest that environmental uncertainty has a direct influence on each dimension of SC risk. In addition to that, a high level of strategic leadership mitigates the impact of environmental uncertainty on supply risk and delivery risk. However, the data did not support moderation for manufacturing process risks. Findings imply that firms operating in highly uncertain environments are more prone to risks; however, high levels of strategic leadership help manage uncertainties and mitigate risks.

A Markov decision process approach for managing medical drone deliveries

Drone delivery is a fast and innovative method for delivering parcels, food, and medical supplies. Furthermore, this low-contact delivery mode contributes to reducing the spread of pandemic and vaccine-preventable diseases. Focusing on the delivery of medical supplies, this paper studies optimizing the distribution operations at a drone hub that dispatches drones to hospitals located at different geographic locations. Each hospital generates stochastic demands for medical supplies to be covered. This paper classifies stochastic demands based on the distance between hospitals and the drone hub. Satisfying the demands requires flying over different ranges, which is directly related to the amount of charge of the drone batteries. We develop a stochastic scheduling and allocation problem with multiple classes of demand and model the problem using a finite Markov decision process approach. We provide exact solutions for the modest sizes instances using backward induction and discuss that the problem suffers from the curses of dimensionality. Hence, we provide a reinforcement learning method capable of giving near-optimal solutions. We perform a set of computational tests using realistic data representing a prominent drone delivery company. Finally, we analyze the results to provide insights for managing drone hub operations and show that the reinforcement learning method has high performance compared with the exact and heuristic solution methods.

Quantitative Ground Risk Assessment for Urban Logistical Unmanned Aerial Vehicle (UAV) Based on Bayesian Network

The Unmanned Aerial Vehicle (UAV) has been used for the delivery of medical supplies in urban logistical distribution, due to its ability to reduce human contact during the global fight against COVID-19. However, due to the reliability of the UAV system and the complex and changeable operation scene and population distribution in the urban environment, a few ground-impact accidents have occurred and generated enormous risks to ground personnel. In order to reduce the risk of UAV ground-impact accidents in the urban logistical scene, failure causal factors, and failure modes were classified and summarized in the process of UAV operation based on the accumulated operation data of more than 20,000 flight hours. The risk assessment model based on the Bayesian network was built. According to the established network and the probability of failure causal factors, the probabilities of ground impact accidents and intermediate events under different working conditions were calculated, respectively. The posterior probability was carried out based on the network topology to deduce the main failure inducement of the accidents. Mitigation measures were established to achieve the equivalent safety level of manned aviation, aiming at the main causes of accidents. The results show that the safety risk of the UAV was reduced to 3.84 × 10−8 under the action of risk-mitigation measures.

Targeted Delivery Platforms for the Treatment of Multiple Sclerosis.

Multiple sclerosis (MS) is a neurodegenerative condition of the central nervous system (CNS) that presents with varying levels of disability in patients, displaying the significance of timely and effective management of this complication. Though several treatments have been developed to protect nerves, comprehensive improvement of MS is still considered an essential bottleneck. Therefore, the development of innovative treatment methods for MS is one of the core research areas. In this regard, nanoscale platforms can offer practical and ideal approaches to the diagnosis and treatment of various diseases, especially immunological disorders such as MS, to improve the effectiveness of conventional therapies. It should be noted that there is significant progress in the development of neuroprotective strategies through the implementation of various nanoparticles, monoclonal antibodies, peptides, and aptamers. In this study, we summarize different particle systems as well as targeted therapies, such as antibodies, peptides, nucleic acids, and engineered cells for the treatment of MS, and discuss their potential in the treatment of MS in the preclinical and clinical stages. Future advances in targeted delivery of medical supplies may offer new strategies for complete recovery as well as practical treatment of progressive forms of MS.

High-Mobility Satellite-UAV Communications: Challenges, Solutions, and Future Research Trends

Due to their fast deployment and high flexibility, unmanned aerial vehicles (UAVs) are regarded as vital enablers of various applications that include emergency rescue, telecommunications, delivery of supplies, and more. In order to connect UAVs to a core network anywhere, satellite communications is expected to provide wider backhaul coverage compared to ground wireless communications. Although UAV Communications has been studied in many technical works, high-mobility satellite-UAV communications has not been investigated well. Specifically, physical layer operations of the high-mobility satellite-UAV communication system are affected the most by high-mobility environments. In this article, we focus on the physical layer operation problem in satellite-UAV communications from three perspectives: time-varying channel estimation, the high-mobility transceiver, and high-mobility multiple access. Related key technologies, existing works, open problems, and the next development direction are introduced and surveyed. Furthermore, our proposed scheme is also discussed. Overall, this article aims to help readers understand challenges, solutions, and future research trends regarding physical layer operation issues on high-mobility satellite-UAV aerial communications.

Human drone interaction in delivery of medical supplies: A scoping review of experimental studies

The COVID-19 pandemic, ageing populations and the increasing shortage of skilled workers pose great challenges for the delivery of supplies for people with and without care needs. The potential of drones, as unmanned air vehicles, in healthcare are huge and are discussed as an effective new way to delivery urgent medicines and medical devices, especially in rural areas. Although the advantages are obvious, perspectives of users are important particularly in the development process. Investigating human drone interaction could potentially increase usefulness and usability. The present study aims to perform a systematic scoping review on experimental studies examining the human drone interaction in deliveries of drugs and defibrillators. Two databases (MEDLINE and CINAHL) and references of identified publications were searched without narrowing the year of publication or language. Studies that investigated the human drone interaction or medical delivery with drones in an experimental manner were included (research articles). All studies that only simulated the delivery process were excluded. The search revealed 83 publications with four studies being included. These studies investigated the user experience of drone delivered defibrillators, but no study was identified that investigated the human drone interaction in the delivery of drugs. Three categories of human drone interaction were identified: landing, handover, and communications. Regarding landing and handover, the most important issue was the direct physical contact with the drone while regarding communications users need clearer instructions about drone´s direction, sound and look like. The identified studies used technology-driven approaches by investigating human drone interaction in already existing technologies. Users must become integral part of the whole development process of medical drone services to reduce concerns, and to improve security, usability and usefulness of the system. Human drone interaction should be developed according to the identified categories of human drone interaction by using demand- and technology-driven approaches.

Drone Forensics and Machine Learning: Sustaining the Investigation Process

Drones have been increasingly adopted to address several critical challenges faced by humanity to provide support and convenience . The technological advances in the broader domains of artificial intelligence and the Internet of Things (IoT) as well as the affordability of off-the-shelf devices, have facilitated modern-day drone use. Drones are readily available for deployment in hard to access locations for delivery of critical medical supplies, for surveillance, for weather data collection and for home delivery of purchased goods. Whilst drones are increasingly beneficial to civilians, they have also been used to carry out crimes. We present a survey of artificial intelligence techniques that exist in the literature in the context of processing drone data to reveal criminal activity. Our contribution also comprises the proposal of a novel model to adopt the concepts of machine learning for classification of drone data as part of a digital forensic investigation. Our main conclusions include that properly trained machine-learning models hold promise to enable an accurate assessment of drone data obtained from drones confiscated from a crime scene. Our research work opens the door for academics and industry practitioners to adopt machine learning to enable the use of drone data in forensic investigations.

Overview of the Uncertainty and Supply Chain Risk in Vietnamese Garment and Textile Firms: The Moderating Role of Supply Chain Flexibility in Risk Mitigation

To stay competitive in the market, firms are forced to offer high levels of customization and product offerings, which leads to high uncertainty in the supply chain. Consequently, in uncertain environment, firms would face the risk of supply disruptions, production, and delivery delays, resulting in poor operational performance. This paper aims to analyze and review the literature on operational uncertainty and risks in the supply chain that enterprises face. On that basis, the study outlines research gaps and future research needs. Keywords: Supply chain risk; Environmental uncertainty; Supply chain flexibility DOI: 10.7176/JESD/13-7-01 Publication date: April 30 th 2022

Reconnaissance of Lake Simaki in North-Kazakhstan region for organizing lake commercial fish farm

Fishery is a traditional industry for North-Kazakhstan region and, therefore, restoration and organization of lake aquaculture enterprises will provide a significant rise in the economy of the region and creation of new jobs. Scale of the lake commercial fish farm development as a component of commercial fish farming, improvement of its contribution into delivery of food supplies to population depend first of all on the degree of its massive involvement. Status of Lake Peschanoye (Simaki) allows utilizing it for fish commercial cultivation. Application of science-based systems in organiz-ing the lake commercial fish farm will raise efficiency of using natural biopotential of this reservoir and correspondingly enlarge the volumes of production and quality of fish products. In 2021, the researches were conducted on Lake Peschanoye (Simaki). During the researches the hydrological regime of the lake was studied; the samples were taken and processed for hydrochemical and hydrobiological analysis; the material was collected to estimate the condition of ichtyofauna. Generally, the hydrological regime of Lake Peschanoye (Simaki) is found favorable for fish habitat. According to the research results of the year 2021, Lake Peschanoye (Simaki) is a freshwater reservoir with a total mineralization of 875.5 mg/dm3 which allows using it for obtaining commercial fish roe that will provide a greater economic effect from the lake’s utilization. According to the average value of the zoobenthos biomass, Lake Peschanoye (Simaki) belongs to a β-mesotrophic water reservoir which corresponds to the medium level of trophicity. According to the development of food reserves, the recommended volumes of stocking with commercial species are the following: northern whitefish (planktophage) – 4 500 larvae per ha (average value for the Northern Kazakhstan) and carp (benthophage) - 350 yearlings per ha. Pike stocking is recommended as an additional rearing target that will also act as a bio-ameliorator in the fight against the Amur sleeper.

Understanding Health Insurance from the Perspective of an Ecosystem: A Systematic Review and Meta-Synthesis

Background: The health insurance ecosystem involves all the organizations and institutions which act as ecosystem actors in accordance with the objectives, values and functions of the ecosystem as its elements. Comprehensive understanding of the elements and actors of this ecosystem can be effective in improving its current status and in the future. Thus, the present study aimed to discuss the key elements (objectives, values and functions), as well as the actors of the health insurance ecosystem by a systematic review method.
 Methods: This systematic review study was conducted in January 2021 using the six-step Walsh and Downe technique in the form of meta-synthesis. The question proposed a search strategy using the SPIDER framework, and the search was done in PubMed, Scopus, Web of Science ,Cochrane and Persian database of Magiran without considering a specific time and place. Content analysis method and MAXQDA 2020 were used to combine the findings. the Mixed Methods Appraisal Tool (MMAT) evaluation checklist version 2018 was used to evaluate quality of studies.
 Results: 1410 articles were found in major databases, and 10 papers were found by manual searching using the search strategy. In addition, 612 duplicate records were removed from the search results after entering the results in EndNote X7 software. Ultimately, 9 studies reached the stage of quality evaluation and meta-synthesis. The selected studies were reviewed one by one, and their key concepts and themes were identified using content analysis method. Finally, the list of actors and key elements of the health insurance ecosystem was made.
 Conclusion: Establishing fair participation and financial protection were identified as the most important key elements in the health insurance ecosystem, achieved through governance functions, resource supply, and service delivery in the context of an integrated and transparent structure. Applying governance is the most important role of ecosystem, forming the necessary participation and coordination between the actors in different roles, and regulating the interactions between them through mechanisms such as tariffs and the payment system. The role played by many actors with different roles or different roles by one actor in the ecosystem results in complexity and conflict of interest.

Developing a bi-objective resilience relief logistic considering operational and disruption risks: a post-earthquake case study in Iran

Today, according to the occurrence of numerous disasters in allover over the world, designing the proper and comprehensive plan for relief logistics has received a lot of attention from crisis managers and people. Besides, considering resilience capability along with operational and disruption risks leads to the robustness of the humanitarian relief chain (HRC), and this comprehensive framework ensures the essential supplies delivery to the beneficiaries and is close to real-world problems. The resilience parameters used for the second objective are obtained by a strong Best Worst Method (BWM). Another supposition of the model is the consideration of uncertainty in all stages of the proposed problem. Moreover, the multiple disasters (sub-sequent minor post disasters) which can increase the initial demand are considered. Furthermore, the proposed model is solved using three well-known metaheuristic algorithms includes non-dominated sorting genetic algorithm (NSGA-II), network reconfiguration genetic algorithm (NRGA), and multi-objective particle swarm optimization (MOPSO), and their performance is compared by several standard multi-objective measure metrics. Finally, the obtained results show the robustness of the proposed approaches, and some directions for future researches are provided.

COVID-19 vaccination challenge: what have we learned from the Brazilian process?

COVID-19 vaccination challenge: what have we learned from the Brazilian process?

A decision support model for selecting unmanned aerial vehicle for medical supplies: context of COVID-19 pandemic

PurposeIn recent times, due to rapid urbanization and the expansion of the E-commerce industry, drone delivery has become a point of interest for many researchers and industry practitioners. Several factors are directly or indirectly responsible for adopting drone delivery, such as customer expectations, delivery urgency and flexibility to name a few. As the traditional mode of delivery has some potential drawbacks to deliver medical supplies in both rural and urban settings, unmanned aerial vehicles can be considered as an alternative to overcome the difficulties. For this reason, drones are incorporated in the healthcare supply chain to transport lifesaving essential medicine or blood within a very short time. However, since there are numerous types of drones with varying characteristics such as flight distance, payload-carrying capacity, battery power, etc., selecting an optimal drone for a particular scenario becomes a major challenge for the decision-makers. To fill this void, a decision support model has been developed to select an optimal drone for two specific scenarios related to medical supplies delivery.Design/methodology/approachThe authors proposed a methodology that incorporates graph theory and matrix approach (GTMA) to select an optimal drone for two specific scenarios related to medical supplies delivery at (1) urban areas and (2) rural/remote areas based on a set of criteria and sub-criteria critical for successful drone implementation.FindingsThe findings of this study indicate that drones equipped with payload handling capacity and package handling flexibility get more preference in urban region scenarios. In contrast, drones with longer flight distances are prioritized most often for disaster case scenarios where the road communication system is either destroyed or inaccessible.Research limitations/implicationsThe methodology formulated in this paper has implications in both academic and industrial settings. This study addresses critical gaps in the existing literature by formulating a mathematical model to find the most suitable drone for a specific scenario based on its criteria and sub-criteria rather than considering a fleet of drones is always at one's disposal.Practical implicationsThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.Social implicationsThe proposed methodology incorporates GTMA to assist decision-makers in order to appropriately choose a particular drone based on its characteristics crucial for that scenario.Originality/valueThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.

Multi-Commodity distribution under uncertainty in disaster response phase: Model, solution method, and an empirical study

When earthquakes and other natural disasters occur, a surge in demand for life-supporting commodities can occur alongside transportation network disruption. Slow and ineffective delivery of water, food, medical supplies, and survival equipment can lead to high levels of anxiety in the population, mistrust for the government, and potentially social conflict and increased casualties. Thus, the issue of how to effectively and efficiently distribute essential commodities in the highly stochastic post-disaster response phase is extremely critical. In this research, we create a network flow model which allows the transportation of goods not only from distribution center to relief centers, but also among the relief centers. This structure allows local relief centers to support each other, which we show leads to much greater efficiency of commodity distribution to meet the demands of each relief center in the face of uncertain transportation network flow. We collaborate with the National Science and Technology Center for Disaster Reduction (NCDR) in Taiwan to model and simulate the relationship between earthquake attributes (e.g., magnitude, time of strike) and the resulting status of the transportation network and speed of vehicle traffic. By applying simulation optimization techniques to solve the split delivery multiple destination inventory routing problem, we are able to obtain the best vehicle and inventory routing decision under varying disaster scenarios. The resulting actionable insights can alleviate civil unrest and anxiety as well as save lives after a major earthquake.

Using Mid-IR spectroscopy (ATR-FTIR) as a fast analytical tool to reveal association between protein spectral profiles and metabolizable protein supply, protein rumen degradation characteristics and estimated intestinal protein digestion before and after rumen incubation of faba bean partitions and faba bean silage

To our knowledge, there is little research done in using vibrational MID-IR molecular spectroscopy- attenuated total reflectance - Fourier transform infrared spectroscopy (ATR-FTIR) for ruminant system study. The objective of this study was to use ATR-FTIR as a fast analytical tool to reveal association between protein molecular structure in faba and metabolizable protein supply and nutrient delivery, and to explore the relationship between protein molecular structure in original and ruminal degraded residue and in situ rumen protein degradation and protein metabolism characteristics of faba bean samples (whole crop, stem, leaf, whole pods, and faba silage). The experiment for ruminant nutrition research was RCBD. Fourier transform Infrared (FTIR) spectra of faba samples before and after 12 and 24 h rumen incubations were collected with JASCO FT/IR-4200 with ATR at mid-IR range (ca. 4000–700 cm−1) with 128 scans and at 4 cm−1 resolution. The univariate molecular spectral analysis was carried using OMNIC software. Protein related spectral parameters before and after rumen degradation included amide region (ca. 1730–1480 cm−1), amide I region (ca. 1713–1558 cm−1) and amide II region (ca. 1558–1485 cm−1). Within amide I region, α-helix (ca. 1644 cm−1) and β-sheet (ca. 1630 cm−1) were studied. The results showed that ATR-FTIR protein molecular spectral features were significantly different before and after rumen incubation. Protein availability and digestion characteristic are mainly determined by original ATR-FTIR spectral profiles. Total truly digestible protein value (DVE) of faba partitions could be predicted with this equation: DVE (g/kg DM) = 1207.7 HAII12 + 228.7 alpha_beta24 − 310.8 (with R-square = 0.94, RSD = 8.06, model P < 0.001). The study shows that vibrational MID-IR molecular spectroscopy (ATR-FTIR) show a high potential to be a fast analytical tool to predict nutrient delivery.

Supporting value sensitivity in the humanitarian use of drones through an ethics assessment framework

AbstractThe current humanitarian use of drones is focused on two applications: disaster mapping and medical supply delivery. In response to the growing interest in drone deployment in the aid sector, we sought to develop a resource to support value sensitivity in humanitarian drone activities. Following a bottom-up approach encompassing a comprehensive literature review, two empirical studies, a review of guidance documents, and consultations with experts, this work illuminates the nature and scope of ethical challenges encountered by humanitarian organizations embarking upon innovation programmes. The Framework for the Ethics Assessment of Humanitarian Drones (FEAHD) identifies five values and five key questions related to ethical considerations along the decision chain of humanitarian drone activities. It fills a gap between high-level, principle-based guidance related to humanitarian innovation, and detailed operation-oriented checklists for projects involving the use of drones. In this way, the FEAHD contributes to support value sensitivity in the humanitarian use of drones.

Delay Prediction to Mitigate E-commerce Supplier Disruptions using Voting Mechanism

COVID-19 has severely affected supply chains and last-mile logistics. With a remarkable expansion in the internet-based deals of B2C online business and changed customer behaviors, it is critical to estimate delays with sufficient precision to avoid time-related uncertainty. The risk of a supplier's delivery being late will harm businesses' ability to fulfill client orders. Shipment information of an online business organization is utilized for the investigation. The dataset is pre-processed and important features are extracted using the Random Forest technique. We propose an enhanced hybrid voting-based classification model with Trees, and Ensemble techniques (like bagging and boosting) enabled by parameters like shipping mode, scheduled shipment time, and order type to anticipate the postponement with the highest accuracy. Since the base classifiers in the voting mechanism cannot perform at the same level, we assigned various weights and noticed a significant improvement in classification performance. Consistently, the proposed model depicts improved performance and provides strategic, operational, and industrial insights for decision-making in last-mile businesses.

Blockchain-based secure delivery of medical supplies using drones

The advantages provided by the drones with regards to three dimensional mobility and ease of deployment makes them a viable candidate for 5G and beyond (B5G) networks. Significant amount of research has been conducted on the aspect of networking for using drones as base stations to provide different services. In this work, we deviate from the traditional use of drones to provide connectivity and explore the delivery of products through drones in the context of maintaining social distancing. However, drone delivery process for critical applications such as delivering medical supplies is vulnerable to attacks such as impersonation attacks. The security of drone operation is important to save the users from any breaches that can lead to financial and physical losses. To cope with these security issues and to make the delivery process transparent, we propose a blockchain-based drone delivery system that registers and authenticates the participating entities including products (medical supplies), warehouse (medical centers) and drones. To this end, we utilize Ethereum platform for implementation of blockchain and smart contract and we present an analysis of different factors that influence the authentication process in terms of time and the number of transactions. Furthermore, to make the communication of a drone with command and control center more secure and robust, we use machine learning (ML)-based intrusion detection system.