A detailed MILP formulation for the optimal design of advanced biofuel supply chains
یک فرمول دقیق MILP برای طراحی بهینه زنجیره های پیشرفته تأمین سوخت زیستی-2021
The optimal design of a biomass supply chain is a complex problem, which must take into account multiple interrelated factors (i.e the spatial distribution of the network nodes, the efﬁcient planning of logistics activities, etc.). Mixed Integer Linear Programming has proven to be an effective mathematical tool for the optimization of the design and the management strategy of Advanced Biofuel Supply Chains (ABSC). This work presents a MILP formulation of the economical optimization of ABSC design, comprising the deﬁnition of the associated weekly management plan. A general modeling approach is proposed with a network structure comprising two intermediate echelons (storage and conversion facilities) and accounts for train and truck freight transport. The model is declined for the case of a multi- feedstock ABSC for green methanol production tested on the Italian case study. Residual biomass feed- stocks considered are woodchips from primary forestry residues, grape pomace, and exhausted olive pomace. The calculated cost of methanol is equal to 418.7 V/t with conversion facility cost accounting for 50% of the fuel cost share while transportation and storage costs for around 15%. When considering only woodchips the price of methanol increases to 433.4 V/t outlining the advantages of multi-feedstock approach.© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Residual biomass | Advanced biofuels | Supply chain design | Logistics network | MILP | Optimization
Biomass supply chain equipment for renewable fuels production: A review
تجهیزات زنجیره تأمین زیست توده برای تولید سوخت های تجدیدپذیر: یک مروز-2021
The production of renewable fuels is a critical component of global strategies to reduce greenhouse gas (GHG) emissions. Moreover, the collection of raw materials for its production can provide added benefits such as reduction of wildfire risk, additional income for farmers, and decreased disposal costs. Although there is substantial literature on design and modeling of supply chains, the authors were unable to find a single reference with the information needed for the selection and cost estimation of each type of equipment involved in the supply chain. Therefore, the goal of this research is to gather information necessary for the construction and utilization of models that might drive the identification of a feasible supply chain to produce renewable fuels at a commercial scale. The primary objectives are to 1) understand the supply chain of critical feedstocks for renewable fuels production; 2) identify the equipment commercially available for collection and ad equation of feedstock; and 3) consolidate information regarding equipment cost, energy consumption, and efficiency, as well as feedstock storage and transportation systems. This paper provides a compilation for five feedstock types studied for sustainable aviation fuel production: 1) agricultural residues and grasses, 2) forest residues, 3) urban wood waste, 4) oilseeds, 5) fats, oils & greases. All the technologies involved from the field to the gate of the preprocessing or conversion unit were reviewed. The information on fats, oils & greases supply chains and equipment purposely designed for forest thinning and pruning was very limited.
Keywords: Feedstock | Collection and adequation | Biofuels | Renewable fuels | Sustainable aviation fuel | Supply chain configuration
An efficient biometric-based continuous authentication scheme with HMM prehensile movements modeling
یک طرح احراز هویت مداوم مبتنی بر بیومتریک با مدل سازی حرکات پیش ساخته HMM-2021
Biometric is an emerging technique for user authentication thanks to its efficiency compared to the traditional methods, such as passwords and access-cards. However, most existing biometric authentication systems require the cooperation of users and provide only a login time authentication. To address these drawbacks, we propose in this paper a new, efficient continuous authentication scheme based on the newly biometric trait that still under development: prehensile movements. In this work, we model the movements through Hidden Markov Model-Universal Background Model (HMM-UBM) with continuous observations based on Gaussian Mixture Model (GMM). Unlike the literature, the gravity signal is included. The results of the experiments conducted on a public database HMOG and on a proprietary database, collected under uncontrolled conditions, have shown that prehensile movements are very promising. This new biometric feature will allow users to be authenticated continuously, passively and in real time.
Keywords: Biometric | Authentication | Prehensile movement | HMM-UBM | GMM
Malaysia scenario of biomass supply chain-cogeneration system and optimization modeling development: A review
سناریوی مالزی سیستم تولید همزمان زنجیره تأمین زیست توده و توسعه مدل سازی بهینه سازی: یک مرور-2021
The development of biomass-based cogeneration energy systems in Malaysia is progressing to meet the circular economy concept and sustainability goal. This comprehensive review aims to report recent advancements in biomass-based cogeneration/biomass co-firing technology in Malaysia correlated with the optimization modeling role. First, this work presents the outlook and current scenario of cogeneration systems in Malaysia by observing performance and the challenges confronted by the technologies. Next, investigation of technical issues concerning the key players of the technologies and the biomass supply chain. This work had prepared using quantitative content-based analysis-meta-analysis. The practical implication of this review enables a complex optimization model that integrates biomass-based cogeneration and biomass supply chain considering economic and environmental viability. It will further enhance progress toward the Malaysian “Industry 4.0-driven” energy initiative. A novel optimization model grounded on Industry 4.0 parameters will foster new opportunities for researchers.
Keywords: Biomass-based cogeneration system | Biomass co-firing | Optimization modeling | Renewable energy | Economic and operational viability
Formalizing and analyzing security ceremonies with heterogeneous devices in ANP and PDL
رسمیت و تجزیه و تحلیل مراسم امنیتی با دستگاههای ناهمگن در ANP و PDL-2021
In today’s security protocols (also called “security ceremonies” when humans play a key role), different nodes may have different capabilities: computers can encrypt and decrypt messages, whereas humans cannot; a biometric device can capture biometric information, whereas a random number generator used in e-banking cannot; and so on. Furthermore, even if a node has the decryption capability, it must also know the encryption key to decrypt a message. Actor-network procedures (ANPs) are a well-known formal model of heterogeneous security protocols by Meadows and Pavlovic, and their procedure derivation logic (PDL) supports the logical reasoning about ANPs. However, ANPs do not support explicitly specifying node capabilities, and PDL does not support reasoning explicitly about the knowledge of the participants at different points in time. In this paper, we extend ANPs to deal with heterogeneous devices by explicitly specifying the nodes’ capabilities, as well as by adding new types of events. We also modify PDL to take into account the knowledge of participants at different points in time, and extend PDL to reason both from a “bird’s- eye” view of the system, as well from a “node’s-eye” view. All this allows us to reason about secrecy and authentication in security protocols/ceremonies with different kinds of devices and human users. We illustrate the use of our modeling notation ANP-C and our logics PDL-CK and PDL-CKL to specify and reason about a number of scenarios involving different kinds of devices, including: scenarios for updating someone’s data in a smart card reader; an SSL/TLS ceremony involving a user, a smartphone with a ﬁngerprint reader, anda remote computer/server; and scenarios involving the YubiKey authentication device used by companies such as Google, Facebook, and Bank of America. 2021 Elsevier Inc. All rights reserved.
Keywords: Formal methods | Security protocols | Security ceremonies | Actor-network procedures | Procedure derivation logic | Authentication devices
Biomass supply chain coordination for remote communities: A game-theoretic modeling and analysis approach
هماهنگی زنجیره تأمین زیست توده برای جوامع از راه دور: رویکرد مدل سازی و تحلیل نظری بازی-2021
Biomass, as one of the most available renewable energies, could reduce dependency on fossil fuels and the consequent environmental impacts. There is a need for biomass supply chain management, which is managing bioenergy production from harvesting feedstock to energy conversion facilities. In case of remote communities, bioenergy adoption requires dealing with dispersed geographies of suppliers and places of consumption with small scales of energy demand. As such, coordination plays a key role in increasing the efficiency of the biomass supply chain network through bundling of demand and thus improving the economy of scale. This paper employs a game-theoretic approach to formulate a coordinated biomass supply chain with three echelons including suppliers, hubs, and energy convertors. To investigate the strategic interactions of participants, three decision making structure scenarios have been considered under Stackelberg game providing insights into the impact of power distribution, the role of side payments in enforcing the flow of decisions, and the resulting efficiency and performance improvements. In doing so, a case study bioenergy supply chain for three northern Canadian communities is explored to demonstrate the application of the proposed formulation, solution methods, and the practicality and significance of the adopted approach and outcomes for remote communities.
Keywords: Bioenergy | Supply chains | Coordination | Remote communities | Game theory | Mathematical Program with Equilibrium | Constraints (MPEC)
Generative Deep Learning in Digital Pathology Workflows
یادگیری عمیق مولد در گردش کار آسیب شناسی دیجیتال-2021
Many modern histopathology laboratories are in the process of digitizing their workflows. Digitization of tissue images has made it feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern computer vision techniques, based on deep learning, promises systems that can identify pathologies in slide images with a high degree of accuracy. Generative modeling is an approach to machine learning and deep learning that can be used to transform and generate data. It can be applied to a broad range of tasks within digital pathology, including the removal of color and intensity artifacts, the adaption of images in one domain into those of another, and the generation of synthetic digital tissue samples. This review provides an introduction to the topic, considers these applications, and discusses future directions for generative models within histopathology.
Application of green supply chain management in the oil Industries: Modeling and performance analysis
کاربرد مدیریت زنجیره تامین سبز در صنایع نفت: مدل سازی و تحلیل عملکرد-2021
Environmental concerns relating to production affairs have made various organizations use green practices in different processes of supply chain, because the green supply chain management (GSCM) is considered as an important organizational philosophy to decrease environmental risks and as a preventive approach in order to increase environmental performance and achievement of competitive advantages for organizations. The purpose of the present article is to design an interactive model for the practices of GSCM and its application to clustering oil industries for analyzing their green performance. Therefore, the literature was studied and a total of fifteen practices were obtained using experts’ opinions in academic and oil industry professionals. In next, the fuzzy interpretative structural modeling (FISM) approach was utilized so as to determine the relationship between the practices through considering the linguistic ambiguities of judgments and designing the structural model. The existing relationships within the structural model were studied and tested by means of structural equation modeling (SEM). After that, the relative importance of each practice was calculated by applying fuzzy analysis network process (FANP). In the next, the oil industries were categorized in two clusters using the K-means algorithm aggregated to the particle swarm optimization algorithm. Results of the present study showed that ‘‘legal requirements and regulations”, ‘‘intra-organizational environmental management”, ‘‘green design” and ‘‘green technology” are of root and influential practices with relatively more importance than others; in addition, it was cleared that the first cluster industries have high performance whereas the second ones have medium performance from the viewpoint of considering the practices of GSCM. Finally, the discriminant function designed to forecasting environment performance of the oil industries and member- ship to clusters for each of them.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Web International Conference on Accelerating Innovations in Material Science – 2020.
Keywords: Green Supply Chain Management | Oil industries | Fuzzy interpretative structural modeling | Kmeans | Particle swarm optimization | Clustering | Discriminant analysis
Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks
پدیده های گیاهی در زمان واقعی تحت راه اندازی رباتیک کشاورزی: یک پلت فرم مبتنی بر دید برای کارهای پیچیده فنوتیپ سازی گیاهان-2021
Plant phenotyping in general refers to quantitative estimation of the plant’s anatomical, ontogenetical, physiological and biochemical properties. Analyzing big data is challenging, and non-trivial given the different complexities involved. Efficient processing and analysis pipelines are the need of the hour with the increasing popularity of phenotyping technologies and sensors. Through this work, we largely address the overlapping object segmentation & localization problem. Further, we dwell upon multi-plant pipelines that pose challenges as detection and multi-object tracking becomes critical for single frame/set of frames aimed towards uniform tagging & visual features extraction. A plant phenotyping tool named RTPP (Real-Time Plant Phenotyping) is presented that can aid in the detection of single/multi plant traits, modeling, and visualization for agricultural settings. We compare our system with the plantCV platform. The relationship of the digital estimations, and the measured plant traits are discussed that plays a vital roadmap towards precision farming and/or plant breeding.
Keywords: Phenotype | Image processing | Spectral | Robotics | Object localization | Precision agriculture | Plant science | Pattern recognition | Computer vision | Automation | Perception
The role of negative entropy within supply chain sustainability
نقش آنتروپی منفی در پایداری زنجیره تأمین-2021
With the COVID-19 pandemic, supply chains are today confronted with more uncertainties than ever before. In the face of unanticipated disruptions, being resilient and sustainable has been rewarding for supply chains in terms of competitive advantage. However, literature is still far from possessing an en compassing sustainable supply chain framework (SSCF). As a contribution to the extant literature, the present study expounds a prominent concept termed negative entropy and explores its role in the SSCF. To accomplish this goal, the effect of negative entropy on supply chain sustainability is tested. Following the open systems theory and drawing from the collaboration and information management aspects of the negative entropy, co-creation, open innovation and network governance concepts which are considered to be relevant in this context are selected to be the antecedents of negative entropy. The empirical research is conducted on prominent logistics service providers and ﬁrms from various sectors with approved research and development departments in Turkey. The obtained data were subjected to covariance-based structural equation modeling analysis via Lisrel program. According to results, negative entropy is found to be a robust element in explaining supply chain sustainability. Furthermore, whereas co-creation and network governance reﬂected signiﬁcant effects on negative entropy, surprisingly, open innovation demonstrated no substantial impact. This paper opens up a new front in sustainable supply chain management studies with a notable empirical study introducing negative entropy in the context of open systems theory.© 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Keywords: Supply chain sustainability | Resilience | Negative entropy | Co-creation | Open innovation | Network governance