Search

There is a continuing interest in exploring the impact of operational excellence (OE) on operational performance. Several OE practices such as Lean, Agile, and Six Sigma are well recognized. However, not all OE deployments in firms have been successful, and a failure of such efforts has often been expensive. Past research reveals several critical failure factors (CFFs) that lead to OE deployment failures in firms. We present these CFFs as an integrated model, based on two dominant schools of OE that promote technical and behavioral factors independently. Further, we validate their direct and mediating relationships through an empirical examination of 663 responses collected from 153 manufacturing units across the USA, U.K., China, and India. This article contributes to the OE body of knowledge by examining the significant direct and mediating effects of behavioral and technical CFFs on the OE failures. Our findings reveal that the technical CFFs mediate the impact of behavioral CFFs on OE failures. Consequently, several managerial and theoretical implications are presented.

Summarization: Alternative tourism has gained considerable interest among consumers and enterprises in the tourism sector, creating new opportunities for providing high quality and innovative tourist services, while contributing to sustainable development. The Internet plays a key role as a tool for promoting alternative tourism. Motivated by this, this article presents an original research on the evaluation of the content features of the websites of enterprises involved with alternative tourism services in the region of Chania, Greece. The sample consists of 28 websites evaluated over 11 performance criteria through the ELECTRE III multicriteria method. The websites are ranked in a benchmarking context and the results of the evaluation are used to identify the most successful practices, as well as the website features that require improvements for the enterprises to take advantage of the opportunities that arise in the new era of technological innovation in the tourism sector. Presented on: IEEE Transactions on Engineering Management

Diagnostic theories are fundamental to information system (IS) practice and are represented as trees. While there are approaches for validating diagnostic trees, these validate the overall performance of the tree rather than identifying ways incorrect diagnoses can occur. It is important to fully validate diagnostic trees because even if the tree gives the correct decision “most of the time,” it is possible for incorrect decisions traveling down little-used branches of the tree to result in catastrophic decisions. In this article, we describe the process of using a variant of q-sorting to validate diagnostic trees. In this methodology, diagnostic trees that independent experts develop are transformed into a quantitative form, and that quantitative form is tested to determine the inter-rater reliability of the individual branches in the tree. The trees are then successively transformed to incrementally test if they branch in the same way. The results help researchers not only identify quality items for use in a diagnostic tree but also facilitate diagnoses of problems with those items and facilitate the reconciliation of discrepant trees by experts. The methodology validates not only the whole tree but also its subparts.

Millimeter wave components, such as circulators and isolators, frequently use magnetic fields to break symmetry of the signal propagation and provide unidirectional signal transmission. While effective, these components have not seen the level of miniaturization of other millimeter wave components, primarily due to the discrete nature of the magnets used in the components. Using circulators in the 40-50 GHz range as a motivating application, requirements arise for the deposited films, namely immunity to eddy currents, sufficient magnetization to act as self-biasing magnets, and out-of-plane orientation of the self-biasing field. Based on these required properties, hexaferrite materials are selected for their strong magnetocrystalline anisotropy (MCA) and low conductance. The difficulty of integrating these components monolithically with monolithic microwave integrated circuits (MMIC) originates from the incompatibility of crystal structure with standard semiconductor materials and process conditions. Additive manufacturing using a composite of strontium hexaferrite (SrFe12O19) particles and photoresist has been chosen as a method to overcome the difficulties of integrating hexaferrite material to semiconductor substrates. Due to their large internal anisotropy field, the particles of strontium hexaferrite tend to rotate to the field direction instead of changing magnetization direction under application of an external magnetic field (less than the anisotropy field). We have developed a method for 3D printing composites of high strontium hexaferrite concentration (up to 20% by volume) in a liquid photoresist, SU8. Rotation of hexaferrite particles in polymer matrix and thus magnetic anisotropy has been demonstrated in the composite, which is subsequently cured to hold the physical position and orientation of the particles. The anisotropy of the self-biasing field provided by the films has been experimentally characterized, and a ferromagnetic resonance (FMR) frequency ~43 GHz has been observed. We have also characterized the viscosity of the particle-laden polymer at different particle concentrations. 3D printing of this composite with poling will make it possible to directly print magnetic components that require out-of-plane anisotropic magnetization.

Magnetic circuit components have long been the largest and most difficult components to miniaturize. Printed circuit board (PCB) fabrication methods cannot achieve small diameter, high aspect-ratio vias for high winding turn numbers to achieve high inductance, and microelectronics methods cannot achieve the high-quality and thick ferrite core materials. In this paper, we present an alternative breakthrough method of printed circuit fabrication to significantly reduce the effective via diameter for embedded magnetic devices, boosting the inductance per unit area. To reduce the effective via diameter, high-density via plugs are ex-situ fabricated and shaped, and then embedded into PCB substrate in the same process as standard magnetic core embedding. The structure of the plug is such that precise placement within the substrate, or alignment to the plug with the winding pattern, is not necessary. Devices presented in this paper use via plugs with an effective diameter of 40 μm and a theoretically unlimited aspect ratio, though refinements in the plug fabrication process are expected to reduce the via diameter down to 10μm. The process is experimentally demonstrated with a 14-turn racetrack shaped transformer; novel diagnostic methods using magneto-optic garnet films to image current paths are also demonstrated.

Privacy concerns of users threaten the usage of online social networks (OSN). In this regard, privacy preserving of OSN emerged as a convincing solution for preserving the privacy of users and uncovering useful insights from the social network data. In this article, we propose a novel algorithm based on the fuzzy sets and rewiring algorithm for preserving the privacy of users. This article presents the algorithm called privacy-preserving rewiring algorithm (PPRA), which can be used for anonymizing the social network data. The algorithm is validated by showing its effectiveness on four real-world datasets across three major graph mining tasks. The proposed PPRA algorithm will help in preserving the privacy of users in the OSN graph while simultaneously maintaining the utility that can be generated from the OSN graph structure.

Providing a structured approach for complex product development (PD) is a challenging issue that necessitates utilizing sophisticated modeling tools and analysis techniques. One effective approach for optimizing the complex engineering system development is the cross application of modularization and sequencing analyses on the design structure matrix (DSM), which leads to a cost-effective and time-efficient PD process. In this regard, the current study presents an application-specific method for the simultaneous implementation of modularization and sequencing analyses on DSMs to find the most independent and excluded modules with the minimum feedback loops among them. To this end, we first formulate the total feedback length among the modules by introducing two new modules’ feedback criteria. Second, we propose a novel objective function combining these feedback criteria with the modularity index metric to simultaneously fulfill the sequencing and modularization goals. This objective function makes the proposed method application specific in the sense that the expert is able to adjust the importance weight of each criterion based on the project's characteristics. Third, an efficient metaheuristic algorithm named discrete particle swarm optimization simulated annealing (DPSOSA) is developed to find the optimal solution by minimizing the introduced objective function. Eventually, to evaluate the DPSOSA performance in different applications, we conduct comprehensive experiments on a total of 21 well-known real-world DSMs. The obtained results demonstrate the superiority of the proposed method over previous approaches proving its effectiveness in each of modularization, sequencing, and modularization-sequencing experiments.

Supply chain resilience assessment and strengthening has become a strategic topic in supply chain management. Extant literature on the subject of supply chain resilience assessment mainly focuses on evaluating the operational performance (e.g., inventory, capacity, lead time) while overlooking aspects of the financial performance. This article presents an event-based Bayesian approach as an effective tool for modeling the causal relationships among variables at different time intervals to evaluate resilience across operational and financial criteria. The model focuses on two key elements of resilience—reliability (the ability of the system to sustain an original state) and recoverability (the ability of a system to recover following a disruption), which allows us to investigate how different operational and financial factors affect the network reliability, recoverability, and resilience. This article contributes to the literature in three ways—first, we incorporate a financial perspective to the quantitative study of supply chain resilience; second, we evaluate both the financial and operational aspects from a longitudinal perspective; third, we show that the financial performance of individual supply chain entities has a strong influence on the overall supply chain resilience. From a practical standpoint, the article presents a model that can help managers identify the weakest nodes in their supply chain, considering operational and financial aspects over time, allowing them to design and operate more resilient supply networks.

Existing no-reference (NR) image quality assessment (IQA) metrics are still not convincing for evaluating the quality of the camera-captured images. Toward tackling this issue, we, in this article, establish a novel NR quality metric for quantifying the quality of the camera-captured images reliably. Since the image quality is hierarchically perceived from the low-level preliminary visual perception to the high-level semantic comprehension in the human brain, in our proposed metric, we characterize the image quality by exploiting both the low-level image properties and the high-level semantics of the image. Specifically, we extract a series of low-level features to characterize the fundamental image properties, including the brightness, saturation, contrast, noiseness, sharpness, and naturalness, which are highly indicative of the camera-captured image quality. Correspondingly, the high-level features are designed to characterize the semantics of the image. The low-level and high-level perceptual features play complementary roles in measuring the image quality. To infer the image quality, we employ the support vector regression (SVR) to map all the informative features to a single quality score. Thorough tests conducted on two standard camera-captured image databases demonstrate the effectiveness of the proposed quality metric in assessing the image quality and its superiority over the state-of-the-art NR quality metrics. The source code of the proposed metric for camera-captured images is released at https://github.com/YT2015?tab=repositories.

Public–private partnership (PPP) projects often end in early termination and the governments must compensate private-sector partners and take over project assets. Based on multicountry experience, this article proposes a systematic approach on when and how to compensate efficiently and fairly. A compensation model was first developed to quantify the amount paid to the private-sector partners when early termination occurs, and built upon that, an incentive model was proposed to analyze the incentives of both contracting parties. We then testified our propositions through two realistic cases. In line with the incomplete contract theory and the reference point theory, the findings demonstrate that the proposed compensation method increases the the private sector's incentives of relationship-specific investments, but discourages its efforts to prevent early termination in serious risk scenarios. This article not only contributes to the theoretical development of PPPs on contract design and incentive mechanisms, but also offers solutions for practitioners in the early termination of PPP projects.

The literature argues that a real digital transformation of firms requires holistic changes of the business model. Despite knowledge about this ambitious goal, understanding of how digital business model transformation can be achieved is still very limited. In this article, we explore how firms achieve digital business model transformation. We apply a case study design to investigate how incumbents have changed their respective business model dimensions during digital transformation. Our findings center on interview data and complementary archival records from 15 cases. We present a framework for digital business model transformation along the dimensions of value proposition, value creation, and value capture. Our results emphasize the importance of a preparatory phase in which the strategic course is set. Furthermore, our findings demonstrate that transforming a company's business model is most effective when a single person, namely the Chief Digital Officer, is responsible. Our findings contribute to the business model literature, by providing a more holistic view on how business model innovation can be utilized during digital transformation.

This brief proposes a low voltage ultra-low power 10-bit 100-MS/s successive approximation register (SAR) analog-to-digital converter (ADC). To overcome the performance degradation due to the signal-related current fluctuation under lower supply voltage, an adaptive-biasing comparator with current self-compensation (CSC) technique is proposed to obtain a constant working current, reducing the minimum supply voltage from 0.7V in conventional ADCs to 0.55V. Since full-scale range shrinks with the supply voltage, voltage undershoot in power rail caused by capacitor digital-to-analog converter (C-DAC) deteriorates effective-number-of-bits (ENOB). Conventional solutions involve enhanced regulators and large decoupling capacitors, which cost remarkable power dissipation and large chip area. This brief presents a pulse-injection undershoot compensation technique which reduces the DAC-related supply fluctuation from a typical 15mV to 1.8mV, and improves the ENOB by 0.4 bit. The prototype was fabricated in TSMC 28nm HPC CMOS technology. The proposed SAR ADC achieves an SNDR of 54.7dB and an SFDR of 68dB with the power consumption of 0.16mW under 0.55V supply voltage, a figure of merit (FoM) of 3.6-fJ/conversion-step is achieved. The chip area of the ADC core is 130μm×225μm.

This article aims to align business strategy and knowledge creation in banking industry through knowledge management (KM) approach by maximizing the tangible and intangible resources. Prior study offered more general context of KM approach while misplaced and less discussed practical process of knowledge sharing practices in the context of banking industry. We developed research model by focusing on banking organization, especially in a developing country. The authors use literature review and document analysis of business strategy in bank. This article integrated and analyzed knowledge gap and business strategy to develop a comprehensive approach for banking company. The gap between what a bank knows and needs to know is an internal knowledge gap, in a sense representing the strengths and weaknesses of a “the alignment between knowledge–business” analysis. The gap between what they know and what its competitors know is an external knowledge gap. It represents the opportunities and threats of a knowledge–business analysis. In addition, there is still no specific team, department, and strategy to manage knowledge assets. The results of this research may help finance companies and managers to initiate KM or to encourage knowledge sharing in banking organization. Therefore, managerial level is supposed to develop a strategy and regulation to encourage employee's participation in knowledge transfer schema. We combined numerous methods and theories namely Zack and Nonaka models as tools to solve the obstacle of alignment between business-knowledge strategic gap and knowledge sharing schema.

With the dramatic progress of wearable devices, continuous collection of single lead ECG wave is able to be implemented in a comfortable fashion. Data mining on single lead ECG wave is therefore attracting increasing attention, where atrial fibrillation (AF) detection is a hot topic. In this paper, we propose a dual-channel neural network for AF detection from a single lead ECG wave. Two primary phases are included, the data preprocessing part followed by a dual-channel neural network. A two-stage denoising procedure is developed for data preprocessing, so as to tackle the high noise and disturbance which generally resides in the ECG wave collected by wearable devices. Then the time-frequency spectrum and Poincare plot of the denoised ECG signal are imported into the developed dual-channel neural network for feature extraction and AF detection. On the 2017 PhysioNet/CinC Challenge database, the F1 values were 0.83, 0.90, and 0.75 for AF rhythm and normal rhythm, and other rhythm, respectively. The results well validate the effectiveness of the proposed method for AF detection from a single lead ECG wave, and also indicate its performance advantages over some state-of-the-art counterparts.

Thanks to the increasing number and massive coverage, delivery drones, equipped with various sensors, have demonstrated significant but unexplored potentials for large-scale and low-cost urban sensing during package delivery. In this paper, we propose novel studies on the reutilization of such delivery drone resources to fill this void in urban crowdsensing. Accounting for interdependency between flying/sensing and drone delivery weight, we jointly optimize route selection, sensing time, and delivery weight allocation, to maximize delivery and sensing utility under drones energy constraints. This problem is formulated as a non-convex mixed integer non-Linear programming problem, which is proved to be NP-hard. To address this intricate problem, we propose near-optimal algorithms that leverage the equivalent objective function construction, the local search scheme, and the alternating iteration technique. Theoretical analysis indicates that our algorithms can achieve the 1/(4+ -approximation ratio (where is an arbitrarily small positive parameter and the convergence guarantee in polynomial time, for the scenarios of fixed and adjustable delivery weights, respectively. Extensive trace-based simulations and field experiments demonstrate that ours can significantly improve the delivery & sensing utility by 124.7% and the energy utilization rate by 72.2% on average, compared with the drone delivery without reusing.

In this paper, a long-term workload management problem for multi-server edge computing with server collaboration is studied. In the considered model, mobile users computation-intensive tasks are generated dynamically over the time and offloaded to associated edge servers according to pre-determined subscription agreements. Upon receiving the subscribed workload, each edge server can then decide to whether participate in server collaboration for enabling workload re-allocation (i.e., workload exchange) with other heterogeneously configured edge servers. Unlike most of the existing work, this paper takes into account both competitions and collaborations among strategic edge servers in sharing their computing capacities. To achieve the equilibrium for each edge server in minimizing its expected cost (including energy consumption, delay, transmission, configuration and pricing costs), a joint optimization is formulated for determining i) its amount of workload to undertake, ii) compensation price charged from peers, and iii) computing speed to adopt. To efficiently solve this problem, we propose a novel cooperative queueing game approach, which integrates a convex optimization, a core cost sharing scheme and a mapping rule. Theoretical analyses and extensive simulations are conducted to evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

Millimeter wave (mmWave) spectrum promises unprecedented data rates in 5G New Radio (NR). However, mmWave links are susceptible to severe path and propagation losses. Implementation of directional antennas that can manifest multiple beams becomes necessary at 5G base stations (gNBs) and User Equipments (UEs). Random access procedure (RAP), which is the first stage to establish connections to gNB, in its current state would be inadequate for such systems. To support RAP, 5G NR introduced mapping of different subsets of preambles in Physical Random Access Channel (PRACH) to different beams. We explore changes in RAP due to preamble-beam mapping and present a novel online algorithm to dynamically distribute preambles amongst beams to achieve fair access opportunities. The proposed algorithm can also eliminate overload problem of the beams with large UE population especially for high arrival rates. We extend the proposed algorithm to be applicable for RAP with access class barring mechanism which enables UEs to transmit a preamble with an optimal probability and, therefore, reduce access congestion. Through extensive simulations, we validate the efficiency of our proposed algorithms in providing fair channel access opportunities, improving throughput (or stability region), and reducing access delay when the system has high rate of UE arrivals and has unbalanced UE arrivals over different beams.

The protection of original achievements including patents and copyrights is a significant and critical issue in today's knowledge-driven economy. In this article, we propose and develop a blockchain service architecture to enhance information traceability and protection effectiveness. Our proposed service architecture is enabled by the core functionalities of blockchain technology including distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Through this blockchain-based traceability system, we can record, secure, validate, and track original achievements’ registrations and other related transactions. The results of our simulation experiments further validate the effectiveness and robustness of the proposed traceability system design. Our experimental analysis shows that the proposed blockchain-based design can effectively enhance original achievements traceability with an embedded automatic incentive rewarding mechanism for both the creation and protection of original achievements. Moreover, our performance analysis and comparative analysis show that the proposed method has more advantages over several other methods. While our study provides an effective solution to address the existing challenges in intellectual property rights and achievements patents protection, the breadth of the blockchain concept and its applications require nuanced investigations in many different contexts. Thus, the proposed blockchain-based traceability system highlights the potential for broader applications of this disruptive technology in many other domains.

Modern consumer electronics included consumer robots, which are made by heterogeneous Internet of Things, and Internet of robotics things are integral members of the Internet of Everything (IoE . IoE plays an incumbent role in smart cities and generates big data. Moreover, smart city IoE maintains various levels of security provisions. However, those are still vulnerable to security and privacy breaches. Nonetheless, mobility increases the risk of security and privacy breaches. As a result, in the internet era, providing seamless security and privacy for inter and intra communication of heterogeneous things is a significant challenge. Furthermore, blockchain is one of the established communication techniques that ensure both security and privacy. On the other hand, blockchain inherits trivial constraints with respect to scalability, latency, and interoperability. Cross-chain is a blockchain concept that aims to alleviate the aforementioned concerns. Therefore, we proposed a multifactor aware (privacy, security, interoperability, and scalability model-driven reference architecture for the Internet of cross-chains, also known as cross-chain as a service platform.

The demand for distributed manufacturing systems (DMS) in the manufacturing sector has notably gained vast popularity as a suitable choice to accomplish sustainability benefits. Manufacturing companies are bound to face critical barriers in their pursuit of sustainability goals. However, the extent to which the DMS attributes relate to sustainable performance and impact on critical barriers to sustainability is considerably unknown. To help close this gap, this study proposes a methodology to determine the relative importance of sustainability barriers, the influence of DMS on these barriers, and the relationship between DMS attributes and sustainable performance. Drawing upon a rich data pool from the Chinese manufacturing industry, the Best-Worst Method (BWM) is used to investigate the relative importance of the sustainability barriers and determine how the DMS attributes influence these barriers and relate to sustainability. The study findings show that ‘organizational barriers’ are the most severe barriers and indicate that ‘reduced carbon emissions’ has the highest impact on ‘organizational’ and ‘socio-cultural barriers’ whereas ‘public approval’ has the highest impact on ‘organizational barriers. The results infer that ‘reduction of carbon emission’ is the DMS strategy strongly linked to improved sustainable performance. Hence, the results can offer in-depth insight to decision-makers, practitioners, and regulatory bodies on the criticality of the barriers and the influence of DMS attributes on the sustainability barriers and improve sustainable performance for increased global competitiveness. Moreover, our study offers a solid foundation for further studies on the link between DMS and sustainable performance.

The steep voltage slopes of today’s wide-bandgap (WBG), fast-switching power semiconductors — like SiC and GaN — lead amongst other challenges to reflection on long cables and thereby overvoltage at inductive loads. To enable the use of these inverters with motors without specially reinforced insulation the voltage slopes need to be slowed down. This article presents a new low-loss countermeasure, which involves the parallel connection of two half-bridges via an interphase transformer. For this one half-bridge is delayed relative to the other one by a delay tuned to a resonant circuit. Undesired DC components in the cross-current of the interphase transformer occur due to non-idealities in the voltage symmetry. The subsequent DC magnetization ultimately can cause the core to saturate. The paper describes the effects due to inaccurate timings and the aspects to keep in mind when dimensioning the magnetic core. Also, this article describes an approach to deal with the undesired current components using a closed-loop control. For that, a special differential-mode shunt is used which enables a dedicated measurement of the cross-current mean value and mitigates the influence of the switching frequency. Finally, the controller concept is presented. The controller is implemented and verified on a 500 kVA SiC inverter test bench.

The consensus problem is relevant to different areas ranging from biology, social psychology, and physics to power systems and robotics. Two crucial aspects of the design of a consensus system are the implementation issues that arise in densely connected networks and the presence of malicious agents that try to cause a deviation from a synchronization state. In this article, we introduce a formulation to design the topology of a consensus network to improve its resilience to attacks while remaining sparse and consistent with the a priori structural relations between the agents. Through mathematical analysis and simulations on artificial and real-world cases, we show the benefits and usefulness of using this strategy to design resilient and structurally sparse consensus networks.

Restrictive public health measures such as isolation and quarantine have been used to reduce the pandemic viruss transmission. With no proper treatment, older adults have been specifically advised to stay home, given their vulnerability to COVID-19. This pandemic has created an increasing need for new and innovative assistive technologies capable of easing the lives of people with special needs. Smart home systems have become widely popular in providing such assistive services to isolated older adults. These systems can provide better services to assist older people if it anticipates what activities inhabitants will perform ahead of time. For example, a smart home can prompt inhabitants to initiate essential activities like taking medicine using activity prediction. This paper proposes a multi- task activity prediction system that jointly predicts labels, lo- cations, and starting times of future activities. The observed sequence of previous activities characterizes future activities. We use body activity information from wearable sensors and motion information from passive environmental sensors to sense activities of daily living of older adults. The activity prediction system consists of recurrent neural networks to capture temporal dependencies. This work also carries out several experiments on collected and existing real datasets to evaluate the systems performance.

Driven by a wide range of essential applications, significant achievements are made to explore WiFi-based Human Activity Recognition (HAR) techniques that utilize the information collected by commercial off-the-shelf WiFi infrastructures to infer human activities without the need for subjects to carry any devices. Although existing WiFi-based HAR systems achieve satisfactory performance in some instances, they are faced with a severe challenge that the impacts of ubiquitous Co-channel Interference (CCI) on WiFi signals are inevitable. This downgrades the performance of these HAR systems significantly. To address this challenge, we propose PhaseAnti, a novel WiFi-based HAR system to exploit the CCI-independent phase component, Nonlinear Phase Error Variation (NLPEV), of WiFi Channel State Information to cope with the negative effects of CCI. The stability of NLPEV data and the sensibility of this component to motions are rigorously analyzed. Furthermore, validated by extensive properly designed experiments, this phase component across subcarriers is invariant under various CCI scenarios while sufficiently distinct for different motions. Therefore, the NLPEV data can be used and processed effectively to perform HAR in CCI scenarios. Extensive experiments with various daily activities in different indoor rooms demonstrate the superior effectiveness and generalizability of the proposed PhaseAnti system under various CCI scenarios.

With the application of wireless sensor network (WSN) in healthcare field, online sharing of medical data has attracted more and more attention. However, wearable sensor nodes in WSN are limited in energy, storage space and data processing capacity, which largely restricts their deployment in resource demand application scenarios. Fortunately, cloud storage services can enrich the capabilities of wearable sensors and provide an effective method for people to share data within a group. However, as medical data directly relates to patients' health and privacy information, ensuring the integrity and privacy of medical records stored in cloud servers becomes a key issue to be urgently solved. Many public data auditing schemes have been put forward to address the above issues. Unfortunately, most of them have security vulnerabilities or poor functionality and performance. In this paper, we come up with a secure and efficient certificateless public auditing scheme for cloud-assisted medical WSNs, which not only supports dynamic data sharing and privacy protection, but also achieves efficient group user revocation. Security analysis and performance evaluation demonstrate that our scheme significantly reduce the total computation cost while achieving a higher security level. Compared with other related schemes, our new proposal is more suitable for group user data sharing in cloud-assisted medical WSNs.

WiFi-based passive gesture tracking plays a crucial role in promoting human-computer interface, due to its pervasive availability and cost-effectiveness. Prior works focus on tracking gestures on 2D sensing plane by aggregating multiple WiFi links (typically 2), with Uniform Linear Arrays (ULAs). However, gestures actually contain 3D spatial information instead of just 2D, thus interpreting the 3D traces as 2D may lead to enormous tracking errors. This paper aims at exploring the possibility of passively tracking 3D hand traces likewise leveraging two WiFi links with standard 3-element ULAs, and presents a generic 3D centimeter-level passive gesture tracking system, called CentiTrack-3D. To this end, we make two key observations: (1) The ULA-resolved angle contains the integrated information of the azimuth and elevation in 3D space, despite its inability to estimate azimuth and elevation separately. (2) The radial motion deviating from the sensing plane also leads to length variations of paths. Motivated by the observations, a 3D tracking model named Chaos is elaborately designed to deduce the hand 3D coordinates, so as to track the traces. Extensive experiments yield that CentiTrack-3D achieves an overall median tracking granularity of 2.5 cm in 3D space in case of diverse users and environment conditions.

Urban solid waste (USW) is a major environmental problem for all countries. The rapid acceleration of economic growth, industrialization, urbanization, and population growth is correlated with the production of solid waste. Solid waste is usually disposed of in solid waste collection bins, so estimating the potential number of solid waste collection bins is necessary to plan an efficient USW management system. In this article, the purpose is to forecast the solid waste generation based on the monthly recorded amount of waste to determine the appropriate number of waste bins. Several artificial intelligence regression and classification approaches have been evaluated for their efficiency to estimate the number of bins. We highlight the effectiveness of sequential models, namely, long short-term memory (LSTM) and bidirectional LSTM (BLSTM), as waste data commonly consist of real-valued time series. Our experiments demonstrate the performance of the LSTM and BLSTM models, in terms of the number of waste bins prediction, when compared with other methods.

This article focused on two main topics currently on among many agendas: smart cities and Artificial Intelligence (AI). The growing interest in the former is due to these cities’ multidimensionality and adaptability in terms of residents’ needs and the requirements of each municipality's reality. AI, in turn, currently plays a transformative, disruptive role in various areas by performing “smart” tasks, thereby facilitating the automation of processes and differentiation initiatives. Smart city strategic planning, especially in the long term, will most likely have to deal with adaptations to AI. Thus, research is needed that can contribute to a more holistic view of these topics and support decision-making processes in these areas. Based on the epistemological principles of the multiple-criteria decision analysis approach (MCDA), this article developed and tested a dynamic analysis system that allows smart city initiatives to address the challenges of adapting to AI. The proposed system highlights the cause-and-effect relationships in this context. The article included an application of the decision-making trial and evaluation laboratory (DEMATEL) technique. The procedural steps followed to implement this methodology were enhanced by close collaboration with an experienced decision maker, who has coordinated various projects in this research context. The proposed system's contributions and limitations were also analyzed in this article.

IEEE 802.11ax is the standard for the new generation WiFi networks. In this paper, we formulate the problem of joint access point (AP) placement and power-channel-resource unit assignment for 802.11ax-based dense WiFi. The objective is to minimize the number of APs. Two quality-of-service (QoS) requirements are to be fulfilled: (1) a two-tier throughput requirement which ensures that the throughput of each station is good enough, and (2) a fault tolerance requirement which ensures that the stations could still use WiFi even when some APs fail. We prove that this problem is NP-hard. To tackle this problem, we first develop an analytic model to derive the throughput of each station under the OFDMA mechanism and a widely used interference model. We then design a heuristic algorithm to find high-quality solutions with polynomial time complexity. Simulation results under both fixed-user and mobile-user cases show that: (1) when the area is small (50 x 50 m2), our algorithm gives the optimal solutions; when the area is larger (80 x 60 m2), our algorithm can reduce the number of APs by 34.9-87.7% as compared to the Random and Greedy algorithms. (2) Our algorithm can always get feasible solutions that fulfill the QoS requirements.

The imbalanced issue among data is common in many machine-learning applications, where samples from one or more classes are rare. To address this issue, many imbalanced machine-learning methods have been proposed. Most of these methods rely on cost-sensitive learning. However, we note that it is infeasible to determine the precise cost values even with great domain knowledge for those cost-sensitive machine-learning methods. So in this method, due to the superiority of F-measure on evaluating the performance of imbalanced data classification, we employ F-measure to calculate the cost information and propose a cost-sensitive hypergraph learning method with F-measure optimization to solve the imbalanced issue. In this method, we employ the hypergraph structure to explore the high-order relationships among the imbalanced data. Based on the constructed hypergraph structure, we optimize the cost value with F-measure and further conduct cost-sensitive hypergraph learning with the optimized cost information. The comprehensive experiments validate the effectiveness of the proposed method.

Big Data has received great attention in academic literature and industry papers. Most of the experiments and studies focused on publishing results of big data technologies development, machine learning algorithms, and data analytics. To the best of our knowledge, there is not yet any comprehensive empirical study in the academic literature on big data technology acceptance. The statistical results of this model provide a compelling explanation of the relationships among the antecedent variables and the dependent variables. The analysis of the structural model reveals that the hypothesis tests are significant for 8 out of 12 path relationships.

Mobile Edge Computing (MEC) has emerged as a promising paradigm catering to overwhelming explosions of mobile applications, by offloading the compute-intensive tasks to an MEC network for processing. The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for users. In this paper, we study a novel delay-aware DNN inference throughput maximization problem by accelerating each DNN inference through jointly exploring DNN partitioning and multi-thread parallelism. Specifically, we consider the problem under both offline and online request arrival settings: a set of DNN inference requests is given in advance, and a sequence of DNN inference requests arrives one by one without the knowledge of future arrivals, respectively. We first show that the defined problems are NP-hard. We then devise a novel constant approximation algorithm for the problem under the offline setting. We also propose an online algorithm with a provable competitive ratio for the problem under the online setting. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.

Existing research has produced mixed, inconclusive, and even conflicting findings on the impact of open innovation (OI) on innovation output. This article takes a meta-analysis approach, systematically reviews the existent literature to identify the main contextual factors, and statistically tests the significance of these factors in influencing the effect of OI on innovation output. Based on the data of 139 studies published by 2020, this article demonstrates that 1) OI has a stronger effect on innovation output in coupled OI (a combination of inbound and outbound OI) than the inbound or outbound OI alone; 2) OI has a stronger effect on innovation output in developing than developed countries; 3) OI has a stronger effect on innovation output at subfirm than firm level; and 4) OI has a stronger effect on innovation output in service than manufacturing industries. The findings have important implications for managers, researchers, and policy-makers.

This article presents an analysis of data extraction for classification using correlation coefficient and fuzzy model. Several traditional methods of data extraction are used for classification that could not provide sufficient information for further step of data analysis on class. It needs refinement of features data to distinguish a class that differs from a traditional class. Thus, it proposes the feature tiny data (subfeature data) to find distinguish class from a traditional class using two methods such as correlation coefficient and fuzzy model to select features as well as subfeature for distinguishing class. In the first approach, the correlation coefficient methods with gradient descent technique are used to select features from the dataset and in the second approach, the fuzzy model with supreme of minimum value is considered to get subfeature data. As per the proposed model, some features (i.e., three features from the acoustic dataset, two features from the QCM dataset, and eight features from the audit dataset, etc.) and subfeatures (as per threshold value like 20 for acoustic; 10 for QCM, and 20 for audit, etc.) are selected based on correlation coefficient as well as fuzzy methods, respectively. Further, the probability approach is used to find the association and availability of subfeature data from the dimensional reduced database. The experimental results show the proposed framework identifies and selects both feature and subfeature data with the effectiveness of the new class. The comparison results of several classifiers on several datasets are explained in the experimental section.

Programmable metasurfaces constitute an emerging paradigm, envisaged to become a key enabling technology for Reconfigurable Intelligent Surfaces (RIS) due to their powerful control over electromagnetic waves. The HyperSurface (HSF) paradigm takes one step further by embedding a network of customized integrated circuit (IC) controllers within the device with the aim of adding intelligence, connectivity, and autonomy. However, little is known about the traffic that the network needs to support as the target electromagnetic function or boundary conditions change. In this paper, the framework of a methodology is introduced to characterize the workload of programmable metasurfaces which is then used to analyze the beam steering HSFs. The workload characterization leads to many useful insights into traffic behavior, including the spatio-temporal load incurred and the HSF limitations in terms of fine-grained tracking of moving targets. It is observed that the traffic is inherently bursty with an uneven spatial distribution of load and that finer resolution comes at the cost of an increased but less bursty load. An indoor mobility model indicates reasonable signaling load on the deployed surfaces. Finally, a statistical analysis on the traffic patterns is performed, showing that the incoming traffic can be well represented by an ON-OFF model.

This paper presents a reliable microgrid for residential society with control methods to design grid forming inverter (GFI) to emulate the seamless-synchronization features to deal with the dynamic concerns of the grid. In this microgrid a photovoltaic (PV)-based GFI with a control scheme in parallel with a PV-battery supported inverter with an enhanced current control is considered to operate under non-ideal grid voltage conditions. The PV-GFI is adaptable to operate either in islanded mode (IAM) or grid integrated mode (GIM) depending under the grid conditions. An adapted power control technique is implemented such that power demand of local loads and harmonic currents are compensated by respective distributed generation. Moreover, the active power demand of all local loads is shared between the main grid and respective local DG. The adapted power controller achieves transient free operation even under the load disturbances. The proposed control methods of microgrid are validated in simulation under various operating conditions.

Power components such as High Electron Mobility Transistors (HEMTs) are used for high power and high frequency applications. These tend to overheat and destroy their packaging and connections to the wire bondings. This paper presents planar micro-thermoelectric sensors (μTESs) developed to measure partial heat flow dissipated by the HEMTs to avoid the HEMTs from reaching critical temperatures. These sensors use the same active materials as well as the same fabrication process as the HEMTs, enabling them to be fabricated simultaneously for a simple integration. The HEMTs’ active layers consist in AlGaN/GaN heterostructures resulting in the formation of a 2D Electron Gas (2DEG) at the interface. Tension factors of 47 mV/(K.mm2) were obtained with these sensors and heat flows of 0.2 W to 7 W were measured.

This article conducts a comparative analysis to investigate the effects of different classification algorithms and structural proximity indexes on the performance of the supervised link prediction approach to anticipating technology convergence at different forecast horizons. For this, we identify relationships between technologies of interest for different time periods and compute 10 structural proximity indexes among unconnected technologies at each period. We develop a set of classification models that identify potential convergence among unconnected technologies where each model is configured differently by a classification algorithm and a combination of the proximity indexes. We compare the performance of the classification models to investigate effective combinations of classification algorithms and proximity indexes at different forecast horizons. The empirical analysis on Wikipedia articles about artificial intelligence technology indicates that random forest outperforms others in short-term forecasting while support vector machine outperforms others in mid-term forecasting. We also identify structural proximity indexes that produce higher performance when combined with the most effective algorithm at each forecast horizon. The results of this article are expected to offer guidelines for choosing classification algorithms and indexes when applying the supervised link prediction approach in anticipating technology convergence.

With the proliferation of versatile mobile applications, offloading compute-intensive tasks to the MEC/Cloud becomes a dramatic technique due to the limited resources and high user experience requirements at mobile devices. However, most existing works design their task offloading schemes without considering the dependence of tasks and the orchestration of the MEC and Cloud, and thus may limit the system performance. In this paper, we propose a dependent task offloading framework for multiple mobile applications, named COFE, where mobile devices can offload their compute-intensive tasks with dependent constraints to the MEC-Cloud system. It can assign the offloaded tasks to the MEC and Cloud adaptively to improve the user experience. Based on COFE, we formulate the task offloading problem as an average makespan minimization problem, which is proved to be NP-hard. Then, we propose a heuristic ranking-based algorithm to assign the offloaded tasks according to their bottom levels. Theoretical analysis proves the stability of the system under the proposed algorithm and extensive simulations validate that the proposed algorithm can significantly reduce the average makespan and deadline violation probabilities of offloaded applications.

Relay selection solutions for underwater acoustic cooperative networks suffer significant performance degradation as they fail to adapt to incomplete information, noisy interference and overwhelming dynamics. To address this challenge, a hierarchical adversarial multi-armed bandit learning framework by proposing an online reward estimation layer is designed to improve adaptive relay decision control. In online reward estimation layer, adaptive Kalman filter estimator is developed to properly handle noisy observation to support accurate reward. Meanwhile an online predict mechanism is projected for all relays to enrich learning information. Furthermore, based on estimate error variance, an adaptive exploration structure is developed to accelerate the balance between exploration and exploitation. All gathered information are exploited to learn relay quality for the decision-making. Accordingly, we present a Hierarchical Adversarial Bandit Learning (HABL) algorithm to fully exploit the heuristic interaction between the hierarchical framework. HABL integrates reward estimation, information prediction, adaptive exploration and decision making carefully in a holistic algorithm to maximize the learning efficiency. Thereby, the HABL-based relay selection algorithm has higher system throughput and lower communication cost. Further, we rigorously analyze the convergence of HABL algorithm and give its upper bound on the cumulative regret. Finally, extensive simulations elucidate the effectiveness of the HABL.

In our model, a venture seeks capital through an initial coin offering (ICO). The ICO enables the venture to collect demand information from decentralized investors. The venture makes a tradeoff between ensuring the project’s success and forecasting market demand through token size and token price. We find that the higher the demand uncertainty and production cost, the greater the venture’s incentive to learn from its investors. To identify when the venture has an incentive to raise funds through an ICO, we compare it with traditional bank financing and analyze the venture’s preference between the two financing options. The results show that when demand uncertainty is high, an ICO can provide both financing and information benefits, whereas when demand uncertainty is low, although the venture does not collect information through an ICO, it can still get financing benefit. Only when demand uncertainty is intermediate, the venture prefers bank financing. In addition, we find that although the revenue sharing effect of the ICO results in an underinvestment issue, it can also alleviate the loss caused by the increased cost of production. Therefore, with high information accuracy, as production cost goes up, ICOs become more attractive than bank financing for the venture.

With the widespread prevalence of smart devices, mobile crowdsensing (MCS) becomes a new trend to encourage mobile nodes to participate in cooperative data collection in various Internet of Things (IoT) applications. In location-dependent MCS, location information of mobile nodes is collected and analyzed by service provider to assist in task allocation. If the service provider is not fully trusted, mobile nodes privacy is leaked and accessed by unauthorized parties. How to preserve privacy while maintaining task allocation accuracy and efficiency becomes challenging. To this end, we propose a learning-based mechanism that involves two parts: 1) privacy-preserving task release and task allocation; 2) accurate and efficient task allocation. In the first part, we design a location-based symmetric key generator, which enables two parties to self-generate a symmetric key without depending on fully trusted authorities. By utilizing this key generator and Proxy Re-encryption, we propose a privacy preserving protocol to protect location information in task release and task allocation. In the second part, we design a reinforcement learning based task allocation algorithm to optimize the winners selection, which obtains high accuracy and efficiency. Performance analysis reveals that our proposed mechanism achieves accurate and efficient task allocation while preserving privacy in location-dependent MCS.

In the fashion industry, garment sizing is a challenging issue and involves different problems resulting in lost profits. The design of a sizing system is a complex task involving both anthropometric data and subjective data, such as comfort or fit. With the emergence of big data, companies can collect valuable information about their consumers and, thus, enhance their sizing systems. Although several methods have been proposed in the literature, they do not integrate consumer comfort and fit. The method proposed in this article aims to design a suitable sizing system, while taking into account both morphological features and consumer preferences. Consumer preferences are extracted from a satisfaction survey, and are then integrated into an optimization process. The main contribution of the proposed model is twofold: a comprehensive evaluation of sizing systems that are based on consumer preferences is proposed and an optimization algorithm is developed to perfectly fit the problem. The proposed system is implemented and evaluated on the measurement database of the CAESAR project and a satisfaction survey conducted by a French fashion retailer. A comparison with up to date techniques and the existing sizing systems demonstrates that our model provides significant improvements for consumer fit and coverage.