Assessing strategies for reducing carbon emissions associated with wood products transportation

Suite à la ratification par le Canada de traités de réduction des émissions de gaz à effets de serre (GES), différents paliers de gouvernement ont mis en œuvre des politiques visant la réduction des émissions industrielles et liées au transport. Depuis 2013, le Québec, conjointement avec la Californie et l’Ontario, ont mis en place un marché du carbone pour encourager les entreprises à réduire leurs émissions. L’industrie forestière, s’appuyant sur le transport de marchandises, pourrait bénéficier de ce régime en termes de prise de décision sur la planification du transport. Cette étude vise à analyser le potentiel des stratégies de réduction des émissions de carbone et à proposer des suggestions appropriées sur la prise de décision en matière de la planification du transport. Quatre stratégies sont principalement envisagées : la réduction de la vitesse, la conduite écologique, le transport intermodal et les modes de chargement. Combinant les stratégies, des modèles d'optimisation dont l'objectif est de minimiser des coûts sont développés sous les contraintes des émissions. Ces modèles impliquent la planification de la distribution de la gestion de la chaîne d'approvisionnement et des problèmes de tournées de véhicules. Microsoft Excel, OpenSolver, Gurobi et LocalSolver sont principalement utilisés pour la modélisation et l’optimisation. Un front de Pareto est par la suite utilisé pour illustrer la relation entre le coût de transport et les émissions de carbone. Pour démontrer les méthodologies, une étude de cas est présentée en utilisant des données réelles. Il est constaté que l'éco-conduite présente un potentiel de réduction des émissions intéressant dans une gamme réaliste d'augmentation des prix. Le choix des stratégies varie selon les préférences du décideur et la difficulté de mise en œuvre des stratégies.With the ratification of greenhouse gas (GHG) reduction agreements by Canada, various levels of government implemented policies to reduce transport-related and other industrial emissions. Since 2013, Québec, together with California and Ontario, has established a carbon market to encourage firms to reduce their emissions. The forest industry could benefit from this scheme in terms of improving efficiency and lessening the environmental impact of wood product transport. This study aims to assess the potential of carbon emission reduction strategies and to provide recommendations on improving the logistics of transporting wood-based materials. There are four main strategies considered in this paper; namely low-speed driving, eco-driving, intermodal transportation, and optimizing loading pattern. By combining these strategies, optimization models are developed with the objective of cost minimization under the constraints of emissions. These models involve the distribution planning of supply chain management and routing problems. Microsoft Excel, OpenSolver, Gurobi, and LocalSolver are mainly used for modeling and optimization. Pareto Front is also used to illustrate the relationship between transportation cost and carbon emission. To demonstrate the methodologies, a case study is exhibited using real world data. It is found that eco-driving has considerable potential in reducing emissions under a feasible range of price increases. The selection of strategies is based on the decision makers’ preferences and the difficulty of strategy implementation

R&D offshoring and technology learning in emerging economies: Firm-level evidence from the ICT industry

This paper studies the impact of the R&D offshoring of multinational enterprises on the firms in host emerging economies. We develop a two-stage non-cooperative game to analyze the strategic interaction between multinational and host country enterprises engaged in R&D investment. An empirical analysis of 12,309 manufacturing firms in the ICT industry in China shows that R&D offshoring has a positive effect on the intensity of the R&D of host country firms. However, the magnitude of the impact depends on both the technological and geographical distance between the multinational and host country firms. The policy implications of these findings are that the governments of host country should be cautious about allowing advanced multinational R&D investment in under-developed sectors, but they should encourage such investment in developed sectors; and that local governments should be involved in R&D policy making because the positive impact of multinational R&D offshoring diminishes as the geographical distance between the multinational and host country firms increases.Research and Development, Offshoring, Spillovers, Emerging Economies

Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference

Reliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems only consider a single parameter in Gaussian noise. This paper develops a reliable identification scheme based on generalized multi-antenna time-frequency distribution (GMTFD) for MIMO systems with non-Gaussian interference and Gaussian noise. First, a new generalized correlation matrix is introduced to construct a generalized MTFD matrix. Then, the covariance matrix based on time-frequency distribution (CM-TF) is characterized by using the diagonal entries from the auto-source signal components and the non-diagonal entries from the cross-source signal components in the generalized MTFD matrix. Finally, by making use of the CM-TF, the Gerschgorin disk criterion is modified to estimate NTA, and the multiple signal classification (MUSIC) is exploited to estimate DOA for MIMO system. Simulation results indicate that the proposed scheme based on GMTFD has good robustness to non-Gaussian interference without prior information and that it can achieve high estimation accuracy and resolution at low and medium signal-to-noise ratios (SNRs)

Attacking Modulation Recognition With Adversarial Federated Learning in Cognitive Radio-Enabled IoT

Internet of Things (IoT) based on cognitive radio (CR) exhibits strong dynamic sensing and intelligent decision-making capabilities by effectively utilizing spectrum resources. The federal learning (FL) framework based modulation recognition (MR) is an essential component, but its use of uninterpretable deep learning (DL) introduces security risks. This paper combines traditional signal interference methods and data poisoning in FL to propose a new adversarial attack approach. The poisoning attack in distributed frameworks manipulates the global model by controlling malicious users, which is not only covert but also highly impactful. The carefully designed pseudo-noise in MR is also extremely difficult to detect. The combination of these two techniques can generate a greater security threat. We have further advanced our proposal with the introduction of the new adversarial attack method called "Chaotic Poisoning Attack" to reduce the recognition accuracy of the FL-based MR system. We establish effective attack conditions, and simulation results demonstrate that our method can cause a decrease of approximately 80% in the accuracy of the local model under weak perturbations and a decrease of around 20% in the accuracy of the global model. Compared to white-box attack methods, our method exhibits superior performance and transferability

Multi-Antenna Spectrum Sensing With Alpha-Stable Noise for Cognitive Radio-Enabled IoT

Cognitive radio-enabled Internet of Things (CR-IoT) is considered as a promising technology to handle spectrum scarcity for IoT applications. Spectrum sensing enables unlicensed secondary users to exploit spectrum holes under the condition of avoiding interference with primary users in CR-IoT networks. Previous studies often assume that the noise is Gaussian while ignoring the influence of non-Gaussian noise. Moreover, multi-antenna-based spectrum sensing algorithms only consider the partial information of covariance matrix. This paper develops two multi-antenna-based spectrum sensing schemes, using fractional low-order covariance matrices to address the issue of performance degradation in impulsive noise. Specifically, the first scheme, namely, diagonal element weighting detection, exploits the diagonal element weighting of the fractional low-order covariance matrix. The latter scheme is called off-diagonal element weighting detection, which adopts the diagonal matrix weighting strategy that exploits the off-diagonal elements of fractional low-order covariance matrices. The approximate analytical expressions of the false alarm probability and detection probability are derived. These developed schemes do not employ any priori knowledge of the primary user signal. Simulation results indicate that two proposed schemes achieve acceptable performance and are robust to the characteristic exponent of the alpha-stable noise, e.g., these proposed methods could achieve a detection probability of 90% with a false alarm probability of 0.1 at GSNR = -16dB, respectively

Automatic Identification of Space-Time Block Coding for MIMO-OFDM Systems in the Presence of Impulsive Interference

Signal identification, a vital task of intelligent communication radios, finds its applications in various military and civil communication systems. Previous works on identification for space-time block codes (STBC) of multiple-input multiple-output (MIMO) system employing orthogonal frequency division multiplexing (OFDM) are limited to additive white Gaussian noise. In this paper, we develop a novel automatic identification algorithm to exploit the generalized cross-correntropy function of the received signals to classify STBC-OFDM signals in the presence of Gaussian noise and impulsive interference. This algorithm first introduces the generalized cross-correntropy function to fully utilize the space-time redundancy of STBC-OFDM signals. The strongly-distinguishable discriminating matrix is then constructed by using the generalized cross-correntropy for multiple receive antennas. Finally, a decision tree identification algorithm is employed to identify the STBC-OFDM signals which is extended by the binary hypothesis test. The proposed algorithm avoids the traditionally required pre-processing tasks, such as channel coefficient estimation, noise and interference statistics prediction and modulation type recognition. Numerical results are presented to show that the proposed scheme provides good identification performance by exploiting the generalized cross-correntropy function of STBC-OFDM signals under impulsive interference circumstances

The development and characterization of a human mesothelioma in vitro 3D model to investigate immunotoxin therapy.

BackgroundTumor microenvironments present significant barriers to penetration by antibodies and immunoconjugates. Tumor microenvironments, however, are difficult to study in vitro. Cells cultured as monolayers exhibit less resistance to therapy than those grown in vivo and an alternative research model more representative of the in vivo tumor is more desirable. SS1P is an immunotoxin composed of the Fv portion of a mesothelin-specific antibody fused to a bacterial toxin that is presently undergoing clinical trials in mesothelioma.Methodology/principal findingsHere, we examined how the tumor microenvironment affects the penetration and killing activity of SS1P in a new three-dimensional (3D) spheroid model cultured in vitro using the human mesothelioma cell line (NCI-H226) and two primary cell lines isolated from the ascites of malignant mesothelioma patients. Mesothelioma cells grown as monolayers or as spheroids expressed comparable levels of mesothelin; however, spheroids were at least 100 times less affected by SS1P. To understand this disparity in cytotoxicity, we made fluorescence-labeled SS1P molecules and used confocal microscopy to examine the time course of SS1P penetration within spheroids. The penetration was limited after 4 hours. Interestingly, we found a significant increase in the number of tight junctions in the core area of spheroids by electron microscopy. Expression of E-Cadherin, a protein involved in the assembly and sealing of tight junctions and highly expressed in malignant mesothelioma, was found significantly increased in spheroids as compared to monolayers. Moreover, we found that siRNA silencing and antibody inhibition targeting E-Cadherin could enhance SS1P immunotoxin therapy in vitro.Conclusion/significanceThis work is one of the first to investigate immunotoxins in 3D tumor spheroids in vitro. This initial description of an in vitro tumor model may offer a simple and more representative model of in vivo tumors and will allow for further investigations of the microenvironmental effects on drug penetration and tumor cell killing. We believe that the methods developed here may apply to the studies of other tumor-targeting antibodies and immunoconjugates in vitro

Spectrum and energy efficient multi-antenna spectrum sensing for green UAV communication

Unmanned Aerial Vehicle (UAV) communication is a promising technology that provides swift and flexible on-demand wireless connectivity for devices without infrastructure support. With recent developments in UAVs, spectrum and energy efficient green UAV communication has become crucial. To deal with this issue, Spectrum Sharing Policy (SSP) is introduced to support green UAV communication. Spectrum sensing in SSP must be carefully formulated to control interference to the primary users and ground communications. In this paper, we propose spectrum sensing for opportunistic spectrum access in green UAV communication to improve the spectrum utilization efficiency. Different from most existing works, we focus on the problem of spectrum sensing of randomly arriving primary signals in the presence of non-Gaussian noise/interference. We propose a novel and improved p-norm-based spectrum sensing scheme to improve the spectrum utilization efficiency in green UAV communication. Firstly, we construct the p-norm decision statistic based on the assumption that the random arrivals of signals follow a Poisson process. Then, we analyze and derive the approximate analytical expressions of the false-alarm and detection probabilities by utilizing the central limit theorem. Simulation results illustrate the validity and superiority of the proposed scheme when the primary signals are corrupted by additive non-Gaussian noise and arrive randomly during spectrum sensing in the green UAV communication