Critical care specialists,the missing link in organ procurement for transplantation

The procurement process for organ donation begins with the identification of potential organ donors in emergency or critical care units(CCU),followed by their clinical evaluation,diagnostic procedures,and therapeutic interventions,mostly conducted in CCUs.It concludes with the request for organ donation and,if accepted,the retrieval of organs.Despite most interventions occurring in detection units,there has been a neglect of the strategic role played by critical care specialists(CCS)in managing and caring for brain-dead or near-brain-death patients.Questions arise:Are they willing to undertake this responsibility?Do they fully comprehend the nature of organ procurement?Are they aware of the specific interventions required to maintain possible organ donors in optimal physiological condition?Our objective is to examine the role of CCS in organ procurement and propose ways to enhance it,ultimately aiming to increase and enhance organ donation rates.

Research on the Status Quo and Optimization of Centralized Procurement of Coronary Stents

Objective To study the impact of the first national centralized procurement of coronary stents on patients’psychology,the economy of medical institutions,and the survival and development of the coronary stents industry,and to provide suggestions on perfecting procedures to better carry out the centralized procurement of coronary stents.Methods Literature analysis was used to investigate the results of the procurement of coronary stent in a province and a hospital.Results and Conclusion The centralized procurement of coronary stents has alleviated the economic pressure of patients,but it has many problems.Therefore,this paper proposes some suggestions,such as promoting the orderly development of the coronary stent industry,improving the evaluation link of the centralized procurement of coronary stents,adopting the accurate reporting model to ensure the quality of coronary stents and improving the standardization of clinical services.

Renal allograft procurement from living unrelated donors in Iran: What falls under the eclipse

Renal transplantation is the treatment of choice for end stage kidney disease.However,despite all the efforts to expand the donor pool,the shortage of donors is increasing and as a consequence,there has been a significant increase in the number of patients on transplant waiting lists globally.Societies worldwide have employed different methods to address this,each with specific ethical concerns surrounding them.Over three decades ago,a governmentally regulated program of kidney transplantation from living unrelated donors was introduced in Iran and since practiced which has been the subject of hot debate in the literature.Nevertheless,despite all these extensive discussions and publications,several key aspects of the program have still not been properly elucidated and addressed.In this article,the author aims to illuminate some dark corners related to this issue that have largely escaped the notice of ethicists.

Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

A Web-Based Approach for the Efficient Management of Massive Multi-source 3D Models

Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.

Research on choice of emergency procurement vendors

In order to make the supply chain select the best emergency procurement suppliers in case the procurement system encounters unexpected risks,six indices which can reflect the comprehensive abilities of alternative suppliers are selected,i.e.,the product yield,the toll standard,the on-time-delivery rate,the rate of turnover industry experience and customer satisfaction.Then standardized processing of evaluation is performed.For quantitative indices,vector normalization is adopted to perform standardized processing,and qualitative fuzziness indices are quantified through assignment.Finally,each index can be transformed into a standardized mark which has dimensionless 1 and the same magnitude.And an expert method is used to determine the index weights and then the linear weighted sum method is used to calculate the alternative supplier's comprehensive ability score and by comparing these scores,the optimal emergency procurement supplier is selected.

Issues and Challenges in Implementing China’sGreen Public Procurement Program

Sustainable development and environmental protection are the themes of the day. One popular policy tool that government uses to promote sustainable development and to protect the environment is green public procurement. Chinese government established its public procurement system in the late 1990s. It has moved to implement green procurement since 2004. In this paper, the authors will first trace the development of Chinese green public procurement program. The authors will then examine the issues involved in its implementation and make suggestions as to how to make green public procurement program more effective. This is one of the first papers examining China’s green public procurement program.

Aspects of International Public Procurement Regulation

The public procurement market is classified as one of the most important segments of the world economy with share of 7-18%in the world GDP.The importance of this segment leads to continuous liberalization.Despite the existing legislative framework,the share of cross-border public procurement remains limited due to several obstacles including discrimination in public procurement that was raised by governments as one of the non-tariff barriers to international trade.Surveys of the European Commission or the European Bank for Reconstruction and Development(EBRD)indicate that there are many other obstacles to entry to international public procurement markets.These obstacles are discussed in the first chapter of this article.The goal of this paper is to review aspects of international public procurement regulation and find ways of eliminating the obstacles through this regulation.Therefore,the second,third,and fourth chapters of the paper analyse the WTO Government Procurement Agreement(GPA),the United Nations Commission on International Trade Law(UNCITRAL)Model Law on Public Procurement,and the role of regional trade agreements.

Optimizing Bidders Selection of Multi-Round Procurement Problem in Software Project Management Using Parallel Max-Min Ant System Algorithm

This paper presents a Game-theoretic optimization via Parallel Min-Max Ant System(PMMAS)algorithm is used in practice to determine the Nash equilibrium value to resolve the confusion in choosing appropriate bidders of multi-round procurement problem in software project management.To this end,we introduce an approach that proposes:(i)A Game-theoretic model of multiround procurement problem(ii)A Nash equilibrium strategy corresponds to multi-round strategy bid(iii)An application of PSO for the determination of global Nash equilibrium.The balance point in Nash Equilibrium can help to maintain a sustainable structure not only in terms of project management but also in terms of future cooperation.As an alternative of procuring entities subjectively,a methodology to support decision making has been studied using Nash equilibrium to create a balance point on benefit in procurement where buyers and suppliers need multiple rounds of bidding.Our goal focus on the balance point in Nash Equilibrium to optimizing bidder selection in multi-round procurement which is the most beneficial for both investors and selected tenderers.Our PMMAS algorithm is implemented based on MPI(message passing interface)to find the approximate optimal solution for the question of how to choose bidders and ensure a path for a win-win relationship of all participants in the procurement process.We also evaluate the speedup ratio and parallel efficiency between our algorithm and other proposed algorithms.As the experiment results,the high feasibility and effectiveness of the PMMAS algorithm are verified.

Study on the Winning Probability for a Bid in Procurement Combinational Auction with Tree Structure

In this paper, the processes to determine winning probability for the corresponding bidder's deterministic bid are presented. The analysis of the winning probability is crucial for studying the bidding equilibria and designing the mechanism of procurement combinational auctions (CAs), and it also provides the decision making support for bidders who are in commercial synergies surrounding. Finally, an example is used to illustrate the feasibility and detailed processes of calculating winning probability.

Multi-source Data-driven Identification of Urban Functional Areas:A Case of Shenyang,China

Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.

Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.

Deceased donor organ procurement injuries in the United States

AIM: To determine the incidence of surgical injury during deceased donor organ procurements. METHODS: Organ damage was classified into three tiers, from 1-3, with the latter rendering the organ nontransplantable. For 12 consecutive months starting in January of 2014, 36 of 58 organ procurement organization's(OPO)'s prospectively submitted quality data regarding organ damage(as reported by the transplanting surgeon and confirmed by the OPO medical director) seen on the procured organ.RESULTS: These 36 OPOs recovered 5401 of the nations' s 8504 deceased donors for calendar year 2014.A total of 19043 organs procured were prospectively analyzed. Of this total, 59 organs sustained damage making them non-transplantable(0 intestines; 4 pancreata; 5 lungs; 6 livers; 43 kidneys). The class 3 damage was spread over 22(of 36) reporting OPO's.CONCLUSION: While damage to the procured organ is rare with organ loss being approximately 0.3% of procured organs, loss of potential transplantable organs does occur during procurement.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine,medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide.In particular,the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019(COVID-19)pandemic has attracted extensive attention globally.Medicinal plants have,therefore,become increasingly popular among the public.However,with increasing demand for and profit with medicinal plants,commercial fraudulent events such as adulteration or counterfeits sometimes occur,which poses a serious threat to the clinical outcomes and interests of consumers.With rapid advances in artificial intelligence,machine learning can be used to mine information on various medicinal plants to establish an ideal resource database.We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants.The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants.The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Procurement Method Selection for Building Maintenance Projects: The Case of Malaysian Public Universities

The purpose of this paper is to implement and validate a decision making framework for building maintenance projects. The growing importance of maintenance sector not only in Malaysia but al- so increasingly globalized, the difficulty in selecting an appropriate procurement method and lack of research in this area provide an impetus for this research. There were 19 procurement selection criteria identified. In addition, there were 4 types of procurements methods which include outsourcing by specialist term contract, outsourcing by tendered schedule term contract, out- sourcing by repair and maintenance contract and outsourcing by measured term contract identified to be the most popular procurement methods adapted by public universities currently and at the same time were considered most important. The decision making framework was developed based on AHP technique and principles. Expert Choice Software was employed as development tool where the criteria and alternatives were integrated into the framework. Finally, structured interview was conducted to validate the framework developed. The validation process was carried out through structured interview with selected public university.

Personal experience with the procurement of 32 liver allografts

AIM: To introduce the American Pittsburgh's method of rapid liver procurement under the condition of brain death and factors influencing the quality of donor liver.METHODS: To analyze 32 cases of allograft liver procurement retrospectively and observe the clinical outcome of orthotopic liver transplantation.RESULTS: Average age of donors was 38.24±12.78 years,with a male:female ratio of 23:9. The causes of brain death included 21 cases of trauma (65.63%) and nine cases of cerebrovascular accident (28.13%). Fourteen grafts (43.75%) had hepatic arterial anomalies, seven cases only right hepatic arterial anomalies (21.88%), five cases only left hepatic arterial anomalies (15.63%) and two cases of both right and left hepatic arterial anomalies (6.25%) among them. Eight cases (57.14%) of hepatic arterial anomalies required arterial reconstruction prior to transplantation. Of the 32 grafts evaluated for early function, 27 (84.38%) functioned well, whereas three (9.38%) functioned poorly and two (6.25%) failed to function at all. Only one recipient died after transplantation and thirty-one recipients recovered. Four recipients needed retransplantation. The variables associated with less than optimal function of the graft consisted of donor age (35.6±12.9 years vs 54.1±4.3 years, P＜0.05), duration of donor's stay in the intensive care unit (ICU) (3.5±2.4 d vs 7.4±2.1 d, P＜0.005), abnormal graft appearance (19.0% vs 100%, P＜0.05), and such recipient problems as vascular thromboses during or immediately following transplantation (89.3% vs 50.0%, P＜0.005).CONCLUSION: During liver procurement, complete heparization, perfusion in situ with localized low temperature and standard technique procedures are the basis ensuring the quality of the graft. The hepatic arterial anomalies should be taken care of to avoid injury. The donor age,duration of donor's staying in ICU, abnormal graft appearance and recipient problem are important factors influencing the quality of the liver graft.

Embedding Knowledge Management in Public Sector Procurement： Redesigning for the Knowledge Economy

There is a perception in the public, political, and trade discourse that private sector procurement performs ＂better＂ than does public sector procurement. This research considers whether this perception is justified. This paper proposes a conceptual framework for assessing issues that influence procurement performance. The framework takes into account the organization＇s business goals, its procurement principles, the design of its procurement capability, the intellectual capital or knowledge that is used to support procurement operations, and the use of knowledge management methods. To represent these factors, the framework adapts the conceptual framework proposed by McElroy （2002）, leverages Andriessen＇s （2005） characterization of intellectual capital, and adopts Bedford＇s （2012） description of the practice of knowledge management. The results, though preliminary and exploratory, suggest that factors which are more often practiced in the private sector than the public sector contribute to higher performance.