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The Cost of Climate Change on Food Security
No full textToday extreme hot or cold days, drought and desertification of the land, change in rainfall pattern, increase in the forest fire and, pests and diseases epidemics and are widely experienced by the communities in an alarming rate. As a consequence, these greatly affect agriculture and food security, water resource, forest and biodiversity, public health and urban settlement and infrastructure. These areas are inter-linked and impacted millions of people. Whereas, in terms of food security it is a slow disaster which increases hunger and famine in the global south countries where hunger is already widespread. For the in-depth analysis of climate change and food security, the research is based on the four dimensions of food security: food availability, food accessibility, food utilization and food stability
Narrow, powerful, and public: The influence of brand breadth in the luxury market
No full textAlthough the current literature in brand extensions is replete with studies in both line and category extensions, the effect of brand breadth (magnitude of the category extensions) is still yet to be thoroughly examined. Few researchers have focused on brand breadth, to suggest when to choose a narrow (vs. broad) brand extension strategy. Accordingly, no theoretical explanations support the co-existence of both narrow brands (i.e., brands with extensions in similar categories) and broad brands (i.e., brands with extensions in dissimilar categories), particularly in the luxury market. In order to provide guidelines for luxury marketers to enhance overall brand equity, we investigate conditions under which narrow brands are more strongly preferred to broad brands in the luxury market, using a total of 389 respondents recruited via Amazon M-turks and 230 university volunteers in four experiments. Findings demonstrate that narrow brands are liked more than broad brands only with consumers who feel powerful and desire status, and especially when the consumption occurs in public. Highlighting the importance of brand breadth, the current research contributes to the literatures in brand extensions and luxury branding by supplying theoretical guidelines to formulate successful brand extension strategies for luxury marketers
Large-Scale Evolutionary Optimization Using Multi-Layer Strategy Differential Evolution
No full textDifferential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based meta-heuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. The Multi-Layer Strategies Differential Evolution (MLSDE) algorithm, which finds optimal solutions for large scale problems. To solve large scale problems were grouped different strategies together and applied them to date set. Furthermore, these strategies were applied to selected vectors to strengthen the exploration ability of the algorithm. Extensive computational analysis was also carried out to evaluate the performance of the proposed algorithm on a set of well-known CEC 2015 benchmark functions. This benchmark was utilized for the assessment and performance evaluation of the proposed algorithm
Targeting Tumors Using Invasive Assays Through Magnetosprillium Magneticum
No full textMagnetospirillum magneticum (AMB-1) are a species of magnetotactic bacteria that are capable of orienting along the earth’s magnetic field lines through their organelles called magnetosomes. Many studies have shown that certain engineered-bacteria can infect the tumor cells resulting in a controlled death of a tumor. This work deals with a technique utilizing AMB-1 along a predefined path through magnetotaxis, which can pave a way for selective doping as well as isolation of the tumor cells from a group of healthy cells through a magnetic invasive assay (MIA). For such a control, tiny mesh of vertical electrical coils each having a diameter of ~ 5 mm is fabricated, which establishes the path for the bacteria to move along the magnetic field lines. The molecular dynamics simulations at the interface of the bacterial cell surface proteins (MSP-1 & flagellin) and Chinese Hamster Ovary (CHO) cell surface containing cytoplasmic and extracellular proteins (BSG, B2M, SDC1, AIMP1, and FOS) will establish an association between the invading AMB-1 and the host CHO cells. The experimental demonstration involves the CHO invasion by the AMB-1 and isolation of selected CHO cells. Statistical analysis along with the relevant electron and force microscopy data will confirm the number of AMB-1 and CHO cells involved before and after invasion and the role of directional control
Optimizing Deep CNN Architectures for Face Liveness Detection
No full textFace recognition is a popular and efficient form of biometric authentication used in many software applications. One drawback of this technique is that it is prone to face spoofing attacks, where an impostor can gain access to the system by presenting a photograph of a valid user to the sensor. Thus, face liveness detection is a necessary step before granting authentication to the user. In this paper, we have developed deep architectures for face liveness detection that use a combination of texture analysis and a convolutional neural network (CNN) to classify the captured image as real or fake. Our development greatly improved upon a recent approach that applies nonlinear diffusion based on an additive operator splitting scheme and a tridiagonal matrix block-solver algorithm to the image, which enhances the edges and surface texture in the real image. We then fed the diffused image to a deep CNN to identify the complex and deep features for classification. We obtained 100% accuracy on the NUAA Photograph Impostor dataset for face liveness detection using one of our enhanced architectures. Further, we gained insight into the enhancement of the face liveness detection architecture by evaluating three different deep architectures, which included deep CNN, residual network, and the inception network version 4. We evaluated the performance of each of these architectures on the NUAA dataset and present here the experimental results showing under what conditions an architecture would be better suited for face liveness detection. While the residual network gave us competitive results, the inception network version 4 produced the optimal accuracy of 100% in liveness detection (with nonlinear anisotropic diffused images with a smoothness parameter of 15). Our approach outperformed all current state-of-the-art methods.http://dx.doi.org/10.3390/e2104042
SLEC: A Novel Serverless RFID Authentication Protocol Based on Elliptic Curve Cryptography
No full textInternet of Things (IoT) is a new paradigm that has been evolving into the wireless sensor networks to expand the scope of networked devices (or things). This evolution drives communication engineers to design secure and reliable communication at a low cost for many network applications such as radio frequency identification (RFID). In the RFID system, servers, readers, and tags communicate wirelessly. Therefore, mutual authentication is necessary to ensure secure communication. Normally, a central server supports the authentication of readers and tags by distributing and managing the credentials. Recent lightweight RFID authentication protocols have been proposed to satisfy the security features of RFID networks. Using a serverless RFID system is an alternative solution to using a central server. In this model, both the reader and the tag perform mutual authentication without the need for the central server. However, many security challenges arise from implementing lightweight authentication protocols in serverless RFID systems. We propose a new secure serverless RFID authentication protocol based on the famous elliptic curve cryptography (ECC). The protocol also maintains the confidentiality and privacy of the messages, tag information, and location. Although most of the current serverless protocols assume secure channels in the setup phase, we assume an insecure environment during the setup phase between the servers, readers, and tags. We ensure that the credentials can be renewed by any checkpoint server in the mobile RFID network. Thus, we implement ECC in the setup phase (renewal phase), to transmit and store the communication credentials of the server to multiple readers so that the tags can perform the mutual authentication successfully while far from the server. The proposed protocol is compared with other serverless frameworks proposed in the literature in terms of computation cost and attacks resistance.http://dx.doi.org/10.3390/electronics810116
Letter to the Editor: A Case of Posterior Inferior Cerebellar Artery Infarction after Cervical Chiropractic Manipulation
No full textStephen, Perle, HanSuk Jung, JooHyun Ham, and HwanTak Choi's letter responding to an article by Jeong and Hwang positing that chiropractic manipulation can injure the neck vessels.https://doi.org/10.13004/kjnt.2019.15.e
Variant Route of the Subclavian Artery Potential Cause of Thoracic Outlet Syndrome
Get PDFHolly Zimmermann, Emily Scholl, David Terfera, and Kevin Kelliher's poster on a possible variant route of the subclavian artery potential cause of thoracic outlet syndrome in a cadavar
Enhanced Machine Learning Engine Engineering Using Innovative Blending, Tuning, and Feature Optimization
No full textInvestigated into and motivated by Ensemble Machine Learning (ML) techniques, this thesis contributes to addressing performance, consistency, and integrity issues such as overfitting, underfitting, predictive errors, accuracy paradox, and poor generalization for the ML models. Ensemble ML methods have shown promising outcome when a single algorithm failed to approximate the true prediction function. Using meta-learning, a super learner is engineered by combining weak learners. Generally, several methods in Supervised Learning (SL) are evaluated to find the best fit to the underlying data and predictive analytics (i.e., “No Free Lunch” Theorem relevance). This thesis addresses three main challenges/problems, i) determining the optimum blend of algorithms/methods for enhanced SL ensemble models, ii) engineering the selection and grouping of features that aggregate to the highest possible predictive and non-redundant value in the training data set, and iii) addressing the performance integrity issues such as accuracy paradox. Therefore, an enhanced Machine Learning Engine Engineering (eMLEE) is inimitably constructed via built-in parallel processing and specially designed novel constructs for error and gain functions to optimally score the classifier elements for improved training experience and validation procedures. eMLEE, as based on stochastic thinking, is built on; i) one centralized unit as Logical Table unit (LT), ii) two explicit units as enhanced Algorithm Blend and Tuning (eABT) and enhanced Feature Engineering and Selection (eFES), and two implicit constructs as enhanced Weighted Performance Metric(eWPM) and enhanced Cross Validation and Split (eCVS). Hence, it proposes an enhancement to the internals of the SL ensemble approaches. Motivated by nature inspired metaheuristics algorithms (such as GA, PSO, ACO, etc.), feedback mechanisms are improved by introducing a specialized function as Learning from the Mistakes (LFM) to mimic the human learning experience. LFM has shown significant improvement towards refining the predictive accuracy on the testing data by utilizing the computational processing of wrong predictions to increase the weighting scoring of the weak classifiers and features. LFM further ensures the training layer experiences maximum mistakes (i.e., errors) for optimum tuning. With this designed in the engine, stochastic modeling/thinking is implicitly implemented. Motivated by OOP paradigm in the high-level programming, eMLEE provides interface infrastructure using LT objects for the main units (i.e., Unit A and Unit B) to use the functions on demand during the classifier learning process. This approach also assists the utilization of eMLEE API by the outer real-world usage for predictive modeling to further customize the classifier learning process and tuning elements trade-off, subject to the data type and end model in goal. Motivated by higher dimensional processing and Analysis (i.e., 3D) for improved analytics and learning mechanics, eMLEE incorporates 3D Modeling of fitness metrics such as x for overfit, y for underfit, and z for optimum fit, and then creates logical cubes using LT handles to locate the optimum space during ensemble process. This approach ensures the fine tuning of ensemble learning process with improved accuracy metric. To support the built and implementation of the proposed scheme, mathematical models (i.e., Definitions, Lemmas, Rules, and Procedures) along with the governing algorithms’ definitions (and pseudo-code), and necessary illustrations (to assist in elaborating the concepts) are provided. Diverse sets of data are used to improve the generalization of the engine and tune the underlying constructs during development-testing phases. To show the practicality and stability of the proposed scheme, several results are presented with a comprehensive analysis of the outcomes for the metrics (i.e., via integrity, corroboration, and quantification) of the engine. Two approaches are followed to corroborate the engine, i) testing inner layers (i.e., internal constructs) of the engine (i.e., Unit-A, Unit-B and C-Unit) to stabilize and test the fundamentals, and ii) testing outer layer (i.e., engine as a black box) for standard measuring metrics for the real-world endorsement. Comparison with various existing techniques in the state of the art are also reported. In conclusion of the extensive literature review, research undertaken, investigative approach, engine construction and tuning, validation approach, experimental study, and results visualization, the eMLEE is found to be outperforming the existing techniques most of the time, in terms of the classifier learning, generalization, metrics trade-off, optimum-fitness, feature engineering, and validation
Predictive Analytics for Quantitative Trade-in-to-Upgrade Decision Making in Intelligent Disassembly-to-Order Systems
No full textThe accelerated growth of technological advancements has triggered the expansion of customer demand leading to highly complex supply chain networks. One viable way original equipment manufacturers (OEMs) can respond to changing purchasing habits is to redesign their strategic and operational activities to build far-reaching information and resource avenues allied with effective marketing policies. These newly implemented policies need to comply with extended producer responsibility (EPR) guidelines that also well align with rising consumer awareness towards green consumption. To achieve this, manufacturers must create efficient end-of-life product (EOLP) return structures and ensure value creation through product recovery operations to dwindle the cascading waste of discarded products. From an environmental viewpoint, retrieving the value embedded in returned items through remanufacturing or recycling has been proven to be effective in reducing the amount of industrial solid waste. EOLP processing operations are heavily reliant on customers' participation in returning outdated devices making product collection a crucial step in point-to-point supply chains. To entice end-users, the OEMs need to design environmentally and economically benign product take-back strategies that would spark the volume of product returns. These constraints dictate two structural challenges: how manufacturers and consumers can become active participants of EOLP treatment activities, and how fast and efficiently OEMs can respond to the changing market and capital needs while preserving their sustainability levels. In terms of active participation, trade-in incentives can help stimulate additional revenue channels for OEMs through product remanufacturing while helping companies comply with the EPR legislations. Trade-in policies are set forth as part of long-term marketing strategies and include incentive programs that aim at enticing current and potential customers to trade-in their used products with newer generations at a discounted price or for instant credit. Within the context of purchasing behavior, trade-in programs positively impact customers' buying decisions by granting buyers the ability to claim the scrap value of their existing devices. Particularly in oversaturated industries such as electronics and automotive, take-back incentives are a pipeline for OEMs to generate significant residual value by reselling remanufactured products on secondary markets. Moreover, offering special discounts or credits in lieu of old devices fuels new product sales by creating an additional revenue stream. Still, in today’s fast-changing market dynamics, inept trade-in practices that fail to eliminate the ambiguity surrounding the prediction of the true quality of returned products bring functional and financial burdens to organizations. The conventional intransigent trade-in schemes fail to address this uncertainty leading to a number of unnecessary inspection, disassembly, and shipment steps resulting in increasing complexity and product recovery cost. Achieving an accurate trade-in scheme is a highly complex multi-dimensional problem requiring novel solutions that traditional manufacturing and supply chain technologies are incapable of offering by design. Such challenging task inevitably necessitates strategic initiatives that stem from the utilization of cutting-edge groundbreaking information technologies for rapid response to customer needs and reduced complexity across all operational layers. Despite the numerous methodologies investigating the potential value gain from remanufacturing and product acquisition pricing policies, there is no study in related literature that incorporates trade-in programs into an intelligent remanufacturing structure. A majority of previous studies propose preventive models with pre-determined and rule-based explicit model parameters hindering the practicability of the substantial volume of data generated by the increased use of technological tools. These models, inevitably, fall short in successfully incorporating long-term manufacturing goals into sustainable business strategies. With this motivation, the architectural framework this dissertation introduces addresses a predictive product recovery model for product returns to enable an autonomous, sensor-embedded, and decentralized disassembly and remanufacturing system. The main objective of this research is to investigate the feasibility of cost- and resource-effective end-of-life product management systems in a smart reverse logistics network where trade-in rebate decisions take place in an autonomous ecosystem. This research, while filling the emerging gap in the utilization of current digital technologies to determine quality-dependent acquisition strategies, also provides a novel quantitative analysis on the efficiency of trade-in policymaking. This model can be employed in manufacturing industries for precise assessment of value creation amid digital advancements in a future-oriented platform. Due to its highly saturated formation, the consumer electronics industry offers a more suitable platform for this study. Therefore, this study examines a trade-in model for a specific technological product, game console, with the help of a case study. First phase of the dissertation evaluates the performance degradation pattern of discarded electronics products in a ubiquitous manner through timestamp data enablers. To handle this highly complex large-volume data, a discrete-event simulation model is developed from the original equipment manufacturer viewpoint. The model aims to examine the behavior of returned devices as well as the expected overall cost of product recovery operations. Following this, a design of experiments study is utilized for the experimentation using Taguchi’s Orthogonal Arrays (OAs). Employing the findings obtained in the first phase, the second phase of the study deals with trade-in policymaking to determine an engaging quotation for varying quality of returned products from the perspectives of all parties involved in the transaction. To achieve this, an initial model for trade-in-to-upgrade incentives is established for discrete sets of quality standards in case where returned products are grouped into three quality classes based on their usage time. The model is then expanded to compare two product acquisition strategies, namely, trade-in-to-upgrade incentives and instant credits. To achieve a realistic strategy, two rebate models are constructed in a simulation-based game setting to mimic the customer behavior and to obtain the resulting payoffs for the OEM in a dynamic ecosystem. To handle the uncertainty in the customer's decision towards the incentive offer, logistic regression analysis is conducted to maximize the likelihood of the acceptance rate. Finally, trade-in policies are compared to obtain favorable strategies augment revenue streams