Search

Introduction/Aims Mean retirement age for UK doctors is 59.6 years giving the average OMFS consultant approximately 20 years of practice. Current pension tax regulations, new consultant posts having 10 sessions (40 hours), increasing proportions of consultants working less than full time (LTFT) all combined with the backlog of elective care created by COVID-19 will create a significant gap between workforce capacity and clinical demand. Materials and Methods The age of current OMFS consultants was estimated using the date of their primary medical/dental qualification. Changes in job plans were estimated using data from the BAOMS Workforce Census and from recently advertised posts. Reports of unfilled posts were collated by OMFS Regional Specialty Professional Advisors (RSPAs). Results First degree dates were identified for 476 OMFS substantive consultant posts. Estimated current average age was 52.7 years (minimum 35.9, maximum 72.1), 75th centile age 59.0 with 23% of the current consultant workforce above the average retirement age for doctors. New consultant posts consisted of 10 sessions (40 hours), whereas existing consultants average 12.1 sessions (48.4 hours). Unfilled consultant posts in Great Britain are 13 % of total compared to 20% in Northern Ireland and Ireland. Conclusions Many (23%) of the OMFS consultant workforce are above average retirement age. Forty-hour contracts; new consultants working LTFT; and early loss of senior colleagues because of pension pressure will reduce NHS’ capacity to treat OMFS disorders and injuries. This paper suggests increasing consultant posts, increasing trainee numbers and actively retaining senior surgeons to maintain capacity.

In this paper, we report on the economic and environmental issues associated with a pyrolysis plant for processing sugarcane residues. The analyses consider four scenarios: (i) raw sugarcane bagasse (SCB), (ii) raw sugarcane trash (SCT) and acid-leached (iii) sugarcane bagasse (L-SCB) and (iv) sugarcane trash (L-SCT). The systems were studied by integrating a comprehensive mathematical model and experimental data. The environmental impact assessment was performed using the Life Cycle Assessment (LCA) methodology, including agriculture and sugar industry stages into the system boundaries. The results demonstrate that the process economy heavily depends on the bio-oil composition, yield, feedstock cost, and leaching conditions. Bio-oil from L-SCT and L-SCB contained more than 30 wt%. levoglucosan, which made it a candidate to produce fermentable sugars or other valuable chemicals instead of being burnt in low-efficiency boilers. The unitary cost of bio-oil produced from L-SCT (0.89 USD/L) and L-SCB (0.75 USD/L) was below that obtained for raw biomass, even when the Total Investment Costs behaved differently. The environmental impacts, expressed in damage categories, associated with L-SCB and L-SCT, were nearly 86% higher than for SCB and SCT. The highest impact could be attributed to the citric acid (CA) production chain, biomass transport, and harvesting operations.

In this paper, a frame structural topology optimization methodology for the generation of moment resisting braced frames for tall buildings, considering dynamic seismic loading, is presented. Real-world loading conditions along with standardized steel profiles of Euronorm are employed. In the literature, the studies that employ the ground structure topology optimization method are limited mostly to truss structures under static loading conditions. The dynamic response in the framework of structural topology optimization remains a challenging problem, especially when the ground structure method is used for deriving the structural system of tall buildings. The contribution of this study to the state of the art, relies on the implementation of the proposed methodology in the conceptual design phase of civil engineering frame structures, where both axial and flexural stiffness is considered. Direct time integration methods are used, to implement forced vibrations and real recorded earthquake data in the topology optimization procedure, resulting to novel layouts for lateral resisting systems of tall buildings. Aiming to aid the engineer to the final design phase, the response spectrum modal analysis is employed and the seismic loading is applied according to the Eurocode 8.

In order to solve the problem of measuring the spatial position of each projectile that are fired using multi-barrel weapons in high-frequency continuous firing modes, this paper proposes a new method to measure the spatial position of multiple projectiles with dual light field intersection imaging mechanism. According to the four-dimensional light field imaging information of light field camera and its micro-lens refocusing principle, we establish the calculation model of the spatial position of multiple projectiles; research the spatial geometric relationship of multiple projectiles in the dual light field intersection imaging system, analyze the light transfer mechanism of projectiles image by using the relation of main lens, micro-lens array and image sensor, and derive density distribution function of multiple projectiles; give the error calculation function of projectiles spatial position by differential method. Through calculation and comparative tests, the results verify the scientificity and rationality of the proposed calculation model.

Increasing energy demands and commitments in relation to climate change have accelerated the deployment of solar power globally, especially in India. Grid-connected solar capacity in the country has increased ∼11 times in just five years, from 2.6 GW in March 2014 to 28.18 GW in March 2019. However, this development has inevitably also led to the emergence of significant volumes of solar photovoltaic (PV) waste, which will only increase in the upcoming years, a considerable challenge for its waste management system. The environmental and human health risks associated with the unscientific dumping of solar PV waste have been well established in the existing literature, presenting the need to develop an effective strategy to manage this emerging waste stream. This paper presents a review of literature about India's solar PV waste management sector with a view to understanding the ground realities and identifying challenges and barriers that hinder the adoption of a regularised strategy for its management using the DPSIR framework approach. It goes on to propose a regulatory framework aimed at mainstreaming the end-of-life (EOL) management of solar PV waste in India after evaluating strategies that have already been used worldwide. In line with the Extended Producer Responsibility (EPR) concept, a multistakeholder, multi-sectoral and systematic approach has been adopted to develop a specific regulatory framework for India. The framework was subjected to a SWOT analysis to evaluate its functionality. The SWOT analysis indicates that one of the critical strengths of the framework is that it is based on a participatory approach to be adopted by all stakeholders for managing this emerging waste stream.

This paper presents a numerical investigation of the ultimate limit state of imperfect columns under axial compression; the columns are made of stainless steel with a circular hollow cross-section (CHS). The subject of interest is the statistical analysis of the ultimate resistance. Statistical characteristics of input material and geometric imperfections are taken from the results of experimental research published in the literature. The first-order reliability method (FORM) along with geometrically and materially nonlinear imperfect numerical analysis (GMNIA) is conducted. The ultimate resistances of CHS columns in flexural buckling obtained from the finite element analyses are compared with the design resistances based on the corresponding European standard, considering various slenderness values. Two approaches of the initial geometrical imperfection amplitude consideration are presented. The influence of correlation between the ultimate resistance of stainless steel columns in compression and input parameters is discussed, with a focus on the geometrical imperfection influence. The results may be useful for the verification of the European standard where the flexural buckling curves of stainless steel columns are relatively new and thus are based on a relatively weaker theoretical and experimental basis compared to carbon steel.

Barge hinterland transport is playing an increasingly important role in freight transport industry through reducing carbon emission, relieving traffic congestion, and providing high capacity and reliable transport service. This paper studies the barge service pricing and scheduling problem in a hinterland transport system under multiple planning horizons. The problem is modeled as a Stackelberg game with the hinterland container operator as the leader and consignees as followers. Counter intuitively, we find that it is optimal for the hinterland container operator to charge the barge service higher than the trucking service when the sea terminal’s free storage time is short. High barge service price could still attract consignees because long transportation time by barge benefits consignees by reducing their storage costs at the inland terminal. We also find that increasing carbon emission price could not reduce the emission quantity when the shipping quantity is high.

Pilot ignited high pressure direct injection natural gas engines have a lower tendency of end-wall gas formation owing to the fuel introduction method. Thus, this type of engines are adaptable to high compression ratios and have the potential to achieve low emission levels. In this paper, numerical investigations concerning underexpanded gas jets and in-cylinder working process of pilot ignited high pressure direct injection natural gas engines are involved. The different numerical models adopted in the previous studies are systematically evaluated, giving guidance for the selection of numerical methods during the development of pilot ignited natural gas engines. The results indicate that analytical models could predict jet penetration with high accuracy while Mach disk height and diameter could be well reproduced by empirical correlations. Computational fluid dynamic models could provide more detailed information of jet flow fluid compared to analytical models and empirical correlations. Among all the computational fluid dynamic models, RANS models are considered as the most computational efficient ones while DNS is the most time consuming choice. If only the macro parameters are concerned, RANS models are the best choices. However, if near-field detailed structures are emphasized, using LES models is a better solution. When conducting three-dimensional simulations of the engine working process, RANS models are the most efficient choices for the modeling of in-cylinder flow field while reduced dual fuel mechanism coupled with phenomenological soot models could capture the in-cylinder combustion and emission formation processes with high-precision.

Nowadays, agriculture generates huge data volumes through different sources. The analysis of this big data enables extraction and deduction of useful knowledge that can be exploited at various levels of the farming process and by different actors (farmers, companies, and agronomists). Nevertheless, to analyse this data effectually, various data sources need to be standardized and integrated into a unified dataset. In this paper, we propose an electronic farming record model using a fact constellation schema that is flexible enough to incorporate various farming datasets and big data models. We also apply some analysis techniques to extract knowledge with the view to optimise crop yield, reduce cost and pesticide resistance, and protect the environment. This can be done by finding suitable quantities of soil properties (texture and pH), soil nutrients, seed rate, herbicides, insecticides, fungicides and adjuvants, which are individually assessed on the most popular crops in Europe. Through experimentation, we show that our models are efficient and very promising.

Background A skilled health workforce is instrumental for the delivery of multidisciplinary cancer care and in turn a critical component of the health systems. There is, however, a paucity of data on the vast inequalities in cancer workforce distribution, globally. The aim of this study is to describe the global distribution and density of the health care workforce involved in multidisciplinary cancer management. Methods We carried out a systematic review of the literature to determine ratios of health workers in each occupation involved in cancer care per 100 000 population and per 100 cancer patients (PROSPERO: protocol CRD42018095414). Results We identified 33 eligible papers; a majority were cross-sectional surveys (n = 16). The analysis of the ratios of health providers per population and per patients revealed deep gaps across the income areas, with gradients of workforce density, highest in high-income countries versus low-income areas. Benchmark estimates of optimal workforce availability were provided in a secondary research analysis: mainly high-income countries reported workforce capacities closer to benchmark estimates. A paucity of literature was defined for critical health providers, including for pediatric oncology, surgical oncology, and cancer nurses. Conclusion The availability and distribution of the cancer workforce is heterogeneous, and wide gaps are described worldwide. This is the first systematic review on this topic. These results can inform policy formulation and modelling for capacity building and scaleup., Highlights • Workforce is an essential component of the health systems. • Stark inequalities are reported for the distribution of health workforce worldwide, but data are limited. • We aimed at portraying the first global figure of the comprehensive cancer workforce for cancer management. • Inequalities in density and distribution of the workforce regard all the key health personnel involved in cancer management. • These data will inform the development of evidence-informed policies for the workforce in low- and middle-income countries.

First-person video summarization has emerged as an important research problem for computer vision and multimedia communities. In this paper, we show how different graph representations can be developed for accurately summarizing first-person (egocentric) videos in a computationally efficient manner. Each frame in a video is first represented as a weighted graph. A shot boundary detection method using graph based mutual information is developed. We next construct a weighted graph for each shot. A representative frame from each shot is selected using a graph centrality measure. A new way of characterizing egocentric video frames using a graph based center-surround model is shown next. Here, each representative frame is modeled as a union of a center region (graph) and a surround region (graph). By exploiting spectral measures of dissimilarity between the two (center and surround) graphs, optimal center and surround regions are determined. Optimal regions for all frames within a shot are kept the same as that of the representative frame. Center-surround differences in entropy and optical flow values along with PHOG (Pyramidal HOG) features are extracted from each frame. All frames in a video are finally represented by another weighted graph, termed as a Video Similarity Graph (VSG). The frames are clustered by applying a Minimum Spanning Tree (MST) based approach with a new measure for inadmissible edges. Frames closest to the centroid of each cluster are captured to build the summary. Experimental evaluation on two benchmark datasets indicate the advantage of the proposed formulation.

Deep learning has shown effectiveness in binary software vulnerability detection due to its outstanding feature extraction capability independent of human expert experience. However, detection approaches such as Instruction2vec still have the following defects: (1) the context between an instruction’s elements (opcode, registers, etc.) is not fully incorporated when embedding a single instruction into its vector representation; (2) the crucial regions that related to vulnerability are not highlighted when extracting features of the vulnerable code. In this paper, we propose a hierarchical attention network for binary software vulnerability detection (HAN-BSVD). Through HAN-BSVD, the contextual information is first enriched by the preprocessor with unifying jump address and normalizing instruction, and then preserved by the instruction embedding network that composed of Bi-GRU and word-attention module; the local features are captured and the crucial regions are highlighted by the feature extraction network that composed of Text-CNN and spatial-attention module. The proposed approach is evaluated on the Juliet Test Suite dataset and the ICLR19 dataset, detection result performs better than the other compared approaches. Extensive ablation studies are also conducted to further prove the effectiveness of each design choice.

Protecting web applications is becoming challenging every passing day, primarily because of attack sophistication, omnipresence of web applications and over-reliance on traditional Web Application Firewalls (WAFs). Advanced Persistent Threats (APTs) make overwhelming use of web attacks during infiltration and expansion phase. Noteworthy research has been carried out to detect web attacks using deep learning because traditional approaches fail against complicated attacks having crafted payloads, scripts and cookie manipulations. This paper proposes a framework based on an enhanced hybrid approach where Deep Learning model is nested with a Cookie Analysis Engine for web attacks detection, mitigation and attacker profiling in real time. We first generated a huge dataset over a period of time and trained our Convolution Neural Network (CNN) based deep learning model using HTTP request parameters like Type, Content length, Data and Requested URL etc. We also developed a Cookie Analysis Engine that checks all incoming cookie(s) for integrity, mutations and failed sanitization checks and informs the user about probable privacy infringement by third party cookies. The framework analyzes the cascading output from the classifier and cookie analysis engine and takes the final decision. We performed rigorous testing of the proposed framework wherein it was first validated on our own custom dataset giving an accuracy of 99.94%. It was also validated on a publicly available benchmark dataset and gave an accuracy of 98.74%. When deployed in a controlled real time environment, the attacker profiling feature enabled the framework to save useful processing time as the deep learning classifier does not triggered for every incoming request. This makes it easy to deploy in any environment to protect web applications in real time.

Biclustering (co-clustering, two-mode clustering), as one of the classical unsupervised learning methods, has been applied in many different fields in recent years. Different types of biclustering methods have been developed such as probabilistic methods, two-way clustering methods, variance minimization methods, and so on. However, few regression-based methods have been proposed to the best of our knowledge. Such methods have been applied in traditional clustering, which can improve both the computational efficiency and the clustering accuracy. In this paper, we present a penalized regression-based method for localizing the biclusters (PRbiclust). By imposing Truncated LASSO Penalty (TLP) and group TLP terms to penalize the column vectors and the row vectors in the regression model, the structure of biclusters in the data matrix is recovered. The model is formulated as an optimization problem with nonconvex penalties, and a computationally efficient algorithm is proposed to solve it. Convergence of the algorithm is proved. To extract the biclusters from the recovered data matrix, we propose a graph-based localization method. An evaluation criterion is also proposed to measure the efficiency of bicluster localization when noise entries exist. We apply the proposed method to both simulated datasets with different setups and a real dataset. Experiments show that this method can well capture the bicluster structure, and performs better than the existing works.

Along with the rapid development of network technologies, the server-side of an e-commerce website is becoming more and more untrustworthy. Thus, how to prevent the disclosure of users’ behavior privacy in online business activities has attracted people’s wide attention. Aiming at the protection of users’ commodity viewing privacy in a commercial website, this paper proposes to construct a group of dummy requests on a trusted client, then, which are submitted together with a user commodity viewing request to the untrusted server-side, so as to confuse and cover up the user preferences. First, we define a privacy model for a user commodity viewing service, in which we introduce a concept called entropy for commodity viewing probability to measure the confusion effect of dummy requests on user requests, and we introduce a concept called regional distance among commodity categories to measure the cover-up effect of dummy requests on users’ commodity viewing preferences. Second, we design an implementation algorithm to generate a group of ideal dummy requests that can meet the constraints formulated in the privacy model. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed approach, i.e., which can improve the security of users’ commodity viewing privacy on the untrusted server-side, without compromising the availability of an e-commerce website. In this paper, we present a valuable research attempt to the protection of users’ behavior privacy in a commercial website, which is of positive significance for building a privacy-preserving e-commerce platform.

In the editorial, an overview of the present-day research agenda in the digital transformation of the cities is given, emphasising the gaps the special issue addresses explicitly. Then a brief outline of the con- tent of the four papers in the issue is provided with several concluding remarks.

Community engagement for disaster risk reduction has become central to participatory emergency management. In neoliberal contexts, publics are increasingly portrayed as responsible for preparing and responding to disasters, while at the same time and contradictorily, they are engaged by the state to encourage compliance with top-down policies and directives. This is happening while incremental budget cuts reinforce the operationalisation of community engagement as information dissemination and service delivery. In this paper we scrutinize the ways in which community engagement for disaster risk reduction has been governed and translated into practice in Australia, focusing on the experiences of the practitioners and community representatives doing community engagement in a peri-urban and multi-hazard area of Victoria. We identify and discuss the role of connectors—individuals fostering connections within and among state-led emergency services, local government, and publics—in negotiating change and building relationships. Our analysis shows how the political economy of state-led emergency management hinders the efforts of connectors, contributing to disconnection between publics, community representatives, and emergency agencies. In navigating the bureaucratic, temporal, and financial constraints of state-led community engagement, the emergency sector is missing opportunities to listen, learn, and work with connectors. The result is missed opportunities to build meaningful connections with publics for disaster risk reduction.

The objective of this paper is to establish the management accounting role of finance managers at international development non-governmental organisations and their role as institutional agents. Through empirical evidence from three case organisations, the management accounting role of finance managers is shown as primarily reporting on spending of specific donor grants, rather than providing insights which could help maximise the overall social impact of their organisation. This dynamic potentially limits the social impact of accounting in such organisations, and potentially in the wider sector. The findings suggest finance managers, by focusing on donor-compliance, do not act as ‘definers’ of institutional elements of management accounting.

The global pandemic caused by the 2019 coronavirus (COVID-19) has led to a dramatic increase in medical waste worldwide. This tremendous increase in medical waste is an important transmission medium for the virus and thus poses new and serious challenges to urban medical waste management. This study investigates the response of medical waste management to the COVID-19 pandemic and subsequent changes in Wuhan City based on the most detailed data available, including waste generation, storage, transportation, and disposal. The results show that despite a 5-fold increase in the demand for daily medical waste disposal in the peak period, the quick responses in the storage, transportation, and disposal sectors during the pandemic ensured that all medical waste was disposed of within 24 hours of generation. Furthermore, this paper discusses medical waste management during future emergencies in Wuhan. The ability of the medical waste management system in Wuhan to successfully cope with the rapid increase in medical waste caused by major public health emergencies has important implications for other cities suffering from the pandemic and demonstrates the need to establish resilient medical emergency systems in urban areas.

Microprocessors are widely used in space applications. ARM processors fabricated with FDSOI technologies are promising for future space programs. However, radiation-hardening approaches can greatly affect the resource overhead and performance. These hardening techniques need to be evaluated at system level to better understand their effectiveness, especially for modern CMOS technologies. In this paper, two ARM® Cortex® M0 cores, a reference core and a hardened core were implemented on the same die in a 28-nm FDSOI technology. The reference core was designed with components from the standard cell library, while the other one was implemented with hardened DICE flip-flops. Both of the cores shared the same on-chip TMR-protected SRAM. Heavy ion experiments showed that the error cross section of the core with DICE flip-flops was about 2 times smaller at LET of 40 MeV·cm2·mg−1. This suggests that the contribution from logic circuits in the microprocessor cores also need to be considered for better error reduction.

As the global startup ecosystem matures its constituent actors are co-evolving in interrelated ways, producing localised entrepreneurial ecosystems around the world. The entrepreneurial ecosystem literature has identified the shared attributes of these systems, but understandings of their inner-workings, interconnectedness, and variance across such systems is lacking. This paper explores the emergence of FinTech entrepreneurial ecosystems in London and Singapore, finding that a single accelerator actor has played a significant role in driving their emergence out of the existing, broader, entrepreneurial ecosystems. It argues that while the entrepreneurial ecosystem literature benefits from a conceptualisation of these ecosystems as complex adaptive systems, there must be a consideration of the potential for transformative agency by actors in the evolutionary dynamics of entrepreneurial ecosystems.

Benefiting from the merits of low cost, high accuracy and high resolution, fringe projection profilometry has been developing rapidly over the past decades. However, recovering the absolute phase with high accuracy and robustness effectively has always been significant challenge in fringe projection profilometry. In this paper, an intelligent Multi-code Deep Learning (MCDL) technique is developed to solve the high-slope absolute phase from only two patterns with high accuracy and robustness. Two sub-networks are designed for obtaining the wrapped phase and the fringe order. Specially, the proposed MCDL method can solve the high-level fringe orders by only a special multi-code pattern itself through a cooperative multi-connected convolutional neural network. By training the deep network with numerous datasets, the principle of unwrapping phase can be learned by the MCDL approach. Experiments demonstrate that the proposed method has the abilities of high robustness, efficiency and accuracy (measurement error: 0.0189mm, FOV: 250 mm × 200 mm), indicating potential applications for high-speed and high-accuracy three dimensional optical measurement.

The recent technological advancements and innovations in sensor development, Internet of Things (IoT) and Information and Communication Technologies (ICT) have been instrumental for the inclusive growth of industry 4.0, development of national digital quality infrastructure and the economic growth of the country. The digital transformation in metrology, quantum-based measurement technologies and measurement information infrastructure shall be pivotal for Industry 4.0, Smart Cities, Digital India, and AtmaNirbhar Bharat missions of the government of India. The present paper reports the paradigms of metrology cloud in Indian perspectives for the establishment of national digital quality infrastructure thus strengthening the three main pillars: metrology accreditation and standardization. The study describes the SWOT analysis carried out on the digital transformation in metrology for the development and the establishment of a metrology cloud and national digital quality infrastructure in India in the next few years.

Structural repairs alter the physical properties of a structure, changing its responses, both in terms of its normal condition and of its different damage states. This difference in responses manifests itself as a shift between the pre- and post-repair data distributions, which can be problematic for conventional data-driven approaches to structural health monitoring (SHM), and limits their effectiveness in industrial applications. This limitation occurs typically because approaches assume that the data distribution is the same in training as appears in testing; with an algorithm failing to generalise when this assumption is not true; that is, pre-repair labels no longer apply to the post-repair data. Transfer learning, in the form of domain adaptation, proposes a solution to this issue, by mapping the pre- and post-repair data distributions onto a shared latent space where their distributions are approximately equal, allowing pre-repair label knowledge to be used to classify the post-repair data. This paper demonstrates the applicability of domain adaptation as a method for overcoming the problem of repair on a dataset from a Gnat trainer aircraft. In addition, a novel modification to an existing domain adaptation technique – joint distribution adaptation – is proposed, which seeks to improve the semi-supervised learning phase of the algorithm by considering a metric-informed procedure. The metric-informed joint distribution adaptation algorithm is benchmarked against, and shown to outperform, both conventional data-based approaches and other domain adaptation techniques.

Smart cities are part of the ongoing advances in technology to provide a better life quality to its inhabitants. Urban mobility is one of the most important components of smart cities. Due to the growing number of vehicles in these cities, urban traffic congestion is becoming more common. In addition, finding places to park even in car parks is not easy for drivers who run in circles. Studies have shown that drivers looking for parking spaces contribute up to 30% to traffic congestion. In this context, it is necessary to predict the spaces available to drivers in parking lots where they want to park. We propose in this paper a new system that integrates the IoT and a predictive model based on ensemble methods to optimize the prediction of the availability of parking spaces in smart parking. The tests that we carried out on the Birmingham parking data set allowed to reach a Mean Absolute Error (MAE) of 0.06% on average with the algorithm of Bagging Regression (BR). This results have thus improved the best existing performance by over 6.6% while dramatically reducing system complexity.

The technical transformation and transfer of most services to digital platforms require that everyone has an electronic device connected to the Internet to assist them accomplish their tasks. Smartphones are one of the best options for everyone because of their small size and ease of transport, in addition to their high capabilities equivalent to a personal computer. It is necessary to identify these devices, usually by checking their IMEI (International Mobile Equipment Identity), to provide and manage several services like the cellular network service. On the other hand, criminals and counterfeiters can manipulate this identity to hide the device and prevent it from being tracked or to make high profits from selling substandard devices. Therefore, several recent proposals have emerged to create a strong fingerprint for use in device identification purposes. This paper reviews and discusses the existing methods to generate a device identity and defines their gaps. Also, it classifies the methods into four categories based on the used technique, namely PUF, machine learning, comparison approach, and sensor calibration. Additionally, it introduces the factors to consider when choosing the technique of device identification. It provides a list of possible attacks on each technology used in device identification methods.

Hollow Pultruded Fibre-Reinforced Polymer (PFRP) profiles, as novel construction material, require further development of design tool to broaden the applications. This paper proposes a combined experimental and numerical methodology as a design tool to investigate the failure modes of these profiles under four-point bending. Two different profiles, each with 10 samples, were tested until failure and were used to validate the numerical model. A finite element model was built based on a fast-convergence incremental approach that suits flexural loading and reduces the computational cost. The validated model was used to study the failure sequence thoroughly and perform an extensive parametric study on the design parameters. Each geometric parameter was studied individually first to determine the relevant levels for each parameter in the full factorial study. A full factorial design of experiment was used to capture the critical parametric interactions with over 81 numerical models. The design rules and recommendation were established for the optimal flexural behaviour of hollow box PFRP profiles to withstand local buckling of the top flange .

Cable-driven parallel robots (CDPRs) are gaining increasing attention due to their low weight, cost, and power consumption characteristics. Adaptive CDPRs are special cable-driven systems in which the locations of the pulley blocks can be modified. In this paper, we propose a new type of adaptive CDPR that can passively modify the attachment points on the end-effector (moving platform) without augmented kinematic redundancy, but this depends on the cable wrapping over these adaptive attachment points. We demonstrate the proposed approach on some practical cases of planar adaptive CDPRs to expand the workspace and dexterity of the robot especially for the large rotation capability. The proposed robot can be passively reconfigured to several configurations. The kinematics modeling and static equilibrium of the adaptive CDPRs are established. Scale optimization of the adaptive attachment point is performed. The results show that this design can effectively increase the workspace, especially the rotation workspace, without increasing the actuation redundancy and kinematic redundancy.

While operations strategy is critical in competitiveness, research gaps exist in applying performance frontier methodologies to identify competitive priorities in operations strategy. The concept of performance efficiency frontier can identify the competitiveness drivers specifying the maximum performance that can be achieved by a given set of inputs. This paper proposes and tests a process to measure, assess, and address improvements in manufacturing performance by operations strategy. A five-step procedure, based on the super-efficiency data envelopment analysis (DEA), supports the improvement recommendations. Secondary data of the fourth round of the High-Performance Manufacturing (HPM) survey grounds the benchmarking analysis and assessment. The proposed framework is tested using the in-depth case of a company from the Brazilian automotive industry. The results identified the main gaps of the company to become a market leader through the enhancement of its operations strategic positioning. Improvement recommendations are developed based on a gap analysis of the input variables. By integrating the operations strategy definition within the performance frontier analysis, we guided the competitiveness improvement of the case company.

This paper proposes to utilize the waste heat of a solar-driven Organic Rankine cycle (ORC) to support the regeneration process of the carbon capture by adsorption (CCA) subsystem. The waste heat is utilized in cascaded form, where the higher temperature component is supplied to the CCA device, and the lower temperature component is internally exchanged to increase the ORC efficiency. The proposed system is studied for the microgrid application, where a thermodynamic analysis is conducted to evaluate the electrical and exergy performance of the proposed system under 1 day of solar irradiance. The analysis is achieved by coupling the simulation models of each subsystem, where the influences of the ORC pressure ratio, turbine inlet temperature, regeneration temperature, and the CO2 input source are investigated. Results show that the proposed system is optimal when combined with CO2 capture of the animal farm; When a 50 m2 and 100 solar concentrations solar thermal plant is used, an optimal regeneration temperature of 75 °C existed to yield 4375.6 kg of CO2 per day and 0.3 MW electricity. The corresponding ORC and C O 2 capture exergy efficiencies are 0.25 and 0.012, respectively, which sum to be higher than the basic solar-ORC under the same conditions.

An updated parametric hysteretic model for steel tubular members, in which the member compressive buckling is considered, is proposed in this paper. The model can be used for simulating the complex inelastic force-displacement hysteresis behaviour of steel tubular members. Based on the general aspects of hysteretic behaviour and the traditional Marshall model, the modified Marshall model is developed to improve the simulation accuracy, whose control parameters are determined by batch parameter analysis results. For the applicability in multiple hysteresis loop process, the initial member curvature under compression-tension during hysteretic loop process is studied and the computation methods under compression and tension are given individually based on the finite element analysis results and theoretical derivation. On this foundation, the model control parameter correction and axial compression capacity redefined with large initial member curvature during hysteretic loop process are executed in the updated parametric hysteretic model proposed later in this paper. Finally, in addition to the comparison with the finite element results, the rationality and applicability of the proposed model are also verified by several experiments and the good agreement in loop curve trend and hysteretic energy consumption are found between the simulated and experimental results.

Visual odometry aims to estimate the relative pose between frames, which is a fundamental task for visual SLAM. In this paper, we present a novel line-based visual odometry (VO) algorithm that fully utilizes the characteristic of line to estimate the projected line of adjacent frame by minimizing the local gradient fitness evaluation. In contrast to the current feature-based or line-based visual odometry, we don ′ t need to explicitly match points or lines of two frames, which is non-trivial and inaccurate in challenging scenarios such as texture-less scenes. In our method, the projected line is calculated simultaneously with the local gradient fitting function of pose estimation based on the constraint that the orientation of the projected line should be perpendicular to the gradient orientation of pixels of its local regions. The proposed method is more robust and reliable than other line-based VO since it fully uses the pixel orientations in the local regions to estimate the projected line and relative pose. We evaluate our method on the real-world RGB-D dataset and synthetic benchmark dataset. Experimental results show that our method achieves the state-of-the-art algorithms in indoors scenes, especially in texture-less scenes.

Foreground segmentation of moving objects is widely used in different computer vision applications; however, existing deep learning-based methods generally suffer from overall degraded F-measure performance. The two main sources that degrade the F-measure are under-segmentation and catastrophic forgetting. Under-segmentation is the problem of misdetecting objects' fine details. The catastrophic forgetting problem occurs when training on a large number of video sequences that leads to forgetting information learned from early video sequences. This paper proposes a novel multi-scale region and edges fusion network with task-based parameter isolation (REFNet-TBPI) to overcome these two problems. The proposed method consists of a novel multi-scale region and edges fusion network (REFNet) to capture the moving objects' boundary details by extracting regions and boundary edges of each object at different feature scales and fusing them to produce high-detailed segmented objects. REFNet is trained using a novel continual learning technique called task-based parameter isolation (TBPI) to overcome the catastrophic forgetting problem. The proposed method (REFNet-TBPI) is extensively evaluated on three benchmarks, namely CDnet2014, DAVIS2016, and SegTrack. By comparing REFNet-TBPI with current state-of-the-art methods, the proposed method outperforms the best-reported state-of-the-art by 4.4% on average.

This paper provides retrospective firm-level evidence on the effectiveness of China’s carbon market pilots in reducing emissions in the electricity sector. We show that the carbon emission trading system (ETS) has no effect on changing coal efficiency of regulated coal-fired power plants. Although we find a significant reduction in coal consumption associated with ETS participation, this reduction was achieved by reducing electricity production. The output contraction in the treated plants is not due to their optimizing behavior but is likely driven by government decisions, because the impacts of emission permits on marginal costs are small relative to the controlled electricity prices and the reduction is associated with financial losses. In addition, we find no evidence of carbon leakage to other provinces, but a significant increase in the production of non-coal-fired power plants in the ETS regions.

This paper is a first attempt to establish how technology roadmapping, commonly reserved for established organizations, can be adapted for the characteristics and limitations of a startup company. This work was performed in collaboration with a newly founded company to create a technology roadmap for devising a 3D cell culture solution for the fields drug discovery and in vitro disease models. Currently, published literature focuses mainly on the methodology and the application in mature companies and industries and overlooking novel technological multinationals. This paper on the other hand, provides a case of implementing a technology roadmap for a start up technology building product based on an emerging technology in an emerging industry. The paper outlines how it was adopted and implemented successfully in a startup company. The roadmapping process was intended to create a strategic plan that can illustrate to the investors how the acquired resources will be used to develop the product. Additionally, it aimed to assist the company in disclosing diverse gaps and prioritizing efforts and resources to face uncertainty while developing the product and the company. The widespread use of advanced manufacturing has reached the drug discovery and research domain. There is an emerging market with no dominant design; currently, no solution addresses the requirements of the research and industry segments.

In this paper, we address one specific video retrieval problem in terms of human face. Given one query in forms of either a frame or a sequence from a person, we search the database and return the most relevant face videos, i.e., ones have the same class label with the query. Such problem is very challenging due to the large intra-class variations and the high request on the efficiency of video representations in terms of both time and space. To handle such challenges, this paper proposes a novel Deep Video Code (DVC) method which encodes video faces into compact binary codes. Specifically, we devise an end-to-end convolutional neural network (CNN) framework that takes face videos as training inputs, models each of them as a unified representation by temporal feature pooling operation, and finally projects the high-dimensional representations of both frames and videos into Hamming space to generate binary codes. In such Hamming space, distance of dissimilar pairs is larger than that of similar pairs by a margin. To this end, a novel bounded triplet hashing loss is elaborately designed, which takes all dissimilar pairs into consideration for each anchor point in a mini-batch, and the optimization of the loss function is smoother and more stable. Extensive experiments on challenging video face databases and general image/video datasets with comparison to the state-of-the-arts verify the effectiveness of our method in different kinds of retrieval scenarios.

National parks shoulder two essential responsibilities: environmental protection and community development, so the development of national parks has attracted much attention. To explore the effectiveness of China's national park system in promoting community economic development, we investigated Nanshan National Park (NNP). Based on the micro income data of 341 households in the NNP in 2018 and 2019, this paper studied the impact of the concession policy and the forest land transfer policy of NNP on family income. The results show that implementing both policies together can increase the total income of farmers. Implementing the concession policy alone will increase the operating income of the residents; implementing the forest land transfer policy will increase their property income. For the former group of farmers, the source of their family income is the operating income; for the latter group, the wage is their most basic and most important source of income, which is true to most farmers in the national park. Although the national park system does increase farmers' income, it will widen the income gap among farmers in the park. The regression results show that whether they have served as village leaders or not and whether they have received vocational training and other factors will affect farmers' policy participation. Therefore, the construction of the national park system needs to enhance the implementing awareness of non-participating residents and pay attention to their needs.

Many industrial companies face demands for increases in product variety in order to accommodate customers’ requirements for additional individual products. With this increasing product variety come increasing costs, poor delivery performance and quality problems. Thus, many companies attempt to reduce their product variety but often fail to do so. To understand the causes of this problem, we identify the most important barriers companies face when trying to reduce their product variety. We review the existing literature and highlight the identified barriers. Then, we carry out a single case study, which identifies a type of barrier not previously described, namely a barrier related to the culture and the personality of the people involved. The paper adds to the literature by extending the perspective on barriers to product variety reduction. The overview of barriers to product variety reduction may help managers focus their attention and resources.

Structural Health Monitoring (SHM) is a promising approach to overcome the unpredictable failure behaviour of composite materials and further foster their use in aerospace industry with increased confidence. SHM may require a complex system, including sensors, wiring and cabling, data acquisition devices and software, data storage equipment, power equipment and algorithms for signal processing, involving a multidisciplinary team for its adequate development considering the operational environment and requirements of a certain application. This review paper focuses on the most promising type of sensors, laboratory made and commercially available, for SHM of aerospace composites. Sensing principles, characteristics, embedding procedures, sensor/ host materials interactions and acquired sensor data/ material behaviour are discussed. The use of sensors for in-situ process monitoring, specifically for curing and mould filling monitoring in liquid composite moulding processes are discussed. General considerations for the development of SHM systems for the aerospace environment are also briefly mentioned.

The high-temperature heat pump steam system (HTHPS) is an effective way to replace the calcining boiler's steam. However, the large temperature span of the system leads to colossal exergy loss of refrigerant in expansion valves and other components. Therefore, it is hoped to adopt the dual-flash vaporization process to improve the system's steam production performance. In this paper, three thermodynamic models and exergy models of HTHPS with a double flash evaporation process are established. Furthermore, this paper uses the multivariate simulated annealing algorithm to calculate the optimal COP of the system. Then, by comparing the single-stage compression (SC) system and the quasi-two-stage compression (QTC) system, the influence of the dual-flash vaporization process on HTHPS is analyzed. The results show that under the same environment, the COP of the dual-flash vaporization process compression system is 23.8% 44.54% higher than that of the SC system. With the increase of the system temperature span, the flash steam supplemental process brings the system to improved thermodynamic performance. Moreover, the exergy model of the dual-flash system is closest to the reversed Carnot cycle, when the evaporation temperature is 50 °C and the condensing temperature is 110 °C.

Variation in material properties leads to variation in stiffness, or displacement response, of structural systems. This paper presents an efficient method to calculate the variability in stiffness of a three-layer Cross Laminated Timber (CLT) panel as a result of heterogeneous elastic properties in the constituent lumber. Typically, problems involving stochastic material properties are solved by Monte Carlo simulations, which is computationally expensive for complex systems. The Variability Response Function is a well-established concept for efficient evaluation of the variance of stochastic systems where properties are modeled by random fields. A new method to study the variation in CLT panel stiffness is developed by applying the Variability Response Function to CLT geometry. Using this method, novel CLT panel layups can efficiently be studied, increasing opportunities for traditionally low-value species to be used in CLT panels. Three case studies are provided to demonstrate the application and utility of the developed method.

This paper examines the impact of pre-2020 information technology (IT) adoption on bank performance during the initial stages of the Covid-19 pandemic. We show that high IT adopters performed better in terms of market returns, Tobin’s q and lending. Our findings also suggest that higher pre-crisis IT investments are associated with more loans issued under the US Paycheck Protection Program, and lower rates of loan modifications due to Covid-19. This study corroborates other recent findings that technology adoption fosters banks’ resilience during crises, ultimately enhancing financial stability.

Background As humanity plans for crewed long-duration missions to Moon bases, Mars, and beyond, astronauts will need more autonomy via training and hardware to deal with medical emergencies. Significant communication delays and long evacuation distances mean a surgical workstation will be a necessity on a spacecraft and could save lives. The aim of this study was to assess the feasibility and design of such a microgravity surgical workstation. Methods This research was conducted using two main approaches: a thorough literature review to summarise current knowledge and inform the enclosure design; and concurrently an iterative process to engineer a workstation design proposal. To the best of our knowledge, a similar surgical enclosure has never been investigated and few containment solutions have been tested in parabolic flight. This study proposes the design of a Crew Operating Microgravity Theatre Enclosure (COMTE), which has been shaped by conclusions from parabolic animal surgery experiments and direct feedback from space medicine experts and astronauts. Results A full technical characterisation of the proposed design is included in this paper, and a prototype was constructed. The defining principle of the COMTE ‘glovebox’ or sealed enclosure was to use unique fluid capillary edge-effects in microgravity to contain surgical fluids and blood during an operation. This aims to improve operator visualisation of the surgical field, whilst ensuring safety through maintaining a sterile surgical site with clear plastic and preventing contamination of the closed-loop spacecraft atmosphere. Additionally, the proposed design includes extensive research on weight, volume, and power requirements, and has been reviewed by external experts. Conclusions This engineering research not only safeguards astronauts but could present a unique solution to terrestrial surgery in remote and extreme environments. In conclusion, this paper proposes a novel engineering solution to the problem of safe and efficient surgery in space, and further work on the design will lead to both ground and parabolic testing.

Auto-scaling systems can dynamically scale the required resources for cloud-based services at runtime. This is an effective mechanism, enabling services to adapt to environmental changes. These systems establish the foundation for achieving elasticity in the modern cloud computing paradigm. Given the dynamic and uncertain nature of the shared cloud infrastructure, cloud auto-scaling systems are one of the most complex and sophisticated created artifacts, aiming to achieve self-aware, self-adaptive, and dependable runtime scaling. To find an effective solution to this problem, an accurate prediction of the required amount of workload as well as the system metrics for future time periods are needed. Various solutions have already been proposed to tackle this problem. Many solutions make use of machine learning, statistical, and ensemble methods. In this paper, we view the auto-scaling problem as a sequence model and apply the convolutional neural networks to predict the future workload of cloud services. Also, by using neural networks, we obtain a mapping between the predicted workload as well as the real-time and future amounts of the required resources. We have also proposed a decision-making mechanism that takes into account different and sometimes conflicting user criteria resulting in the best-compromised decision. To this aim, we have used TOPSIS as a multi-criteria decision-making method for the decision-making component. In the evaluation section, we have examined the amount of prediction error, the amount of service level agreement violations, as well as the amount of resources' under-utilization. Evaluations demonstrate that the proposed approach for predicting the workload shows a 4 percent improvement over the existing approaches.

Revenues from oil and gas development play a key role in the economies of many states in the United States, but there are environmental concerns such as local air and water pollution, land cover changes and fragmentation, and negative effects on wildlife. These concerns were exacerbated as oil and gas production reached historic highs due to the expanded use of hydraulic fracturing technology beginning in the mid-2000s. This paper examines the influence of oil and gas development on breeding bird communities in the High Plains ecoregion of Colorado between 2003 and 2018. Specifically, we investigated whether oil and gas drilling or production affected species richness or evenness of bird communities. Our findings indicate that a decrease in producing well density or the number of producing fracking wells were associated with an increase in evenness of the overall bird community and grassland species richness, but disturbances from well drilling generally did not have a statistically significant influence on bird communities. We also found that a decrease in tree cover was associated with an increase in species richness and evenness of the overall bird community.

This paper considers a green closed-supply chain consisting of a manufacturer and a retailer. A Stackelberg game model of centralized decision-making and decentralized decision-making with manufacturer’s fairness concern was constructed based on the consideration of retailer’s sales effort. The decision-making of supply chain members under the above two situations and their reasons are analyzed in depth. According to the model, a green closed-loop supply chain with profit sharing contract coordination fairness is designed. Finally, the correctness of the model is verified by numerical simulation. We generate our findings from three aspects, as follows: when the manufacturer has fair concern behavior, it is not conducive to the environmental performance of green products, resulting in waste of resources, but also forcing retailers to reduce sales efforts and increase the retail price of products. Finally, the benefits of green closed-loop supply chain are seriously damaged. The profit-sharing contract could improve the relationship between members of the supply chain to achieve sustainable economic and environmental development.

Searching for information about a specific person is a frequent online activity. In most cases, users are aided in the search process by queries containing a name in Web search engines. Typically, Web search engines provide just a few accurate results associated with a name-containing query. Most existing solutions for suggesting synonyms in online search are based on pattern matching and phonetic encoding, however very often, the performance of such solutions is less than optimal. In this paper, we propose SpokenName2Vec, a novel and generic algorithm which addresses the synonym suggestion problem by utilizing automated speech generation, and deep learning to produce novel spoken name embeddings. These embeddings capture the way people pronounce names in a particular language and accent. Utilizing a name’s pronunciation can help detect names that sound alike, but are written differently. We demonstrated the proposed approach on a large-scale dataset with more than 250,000 forenames and surnames and evaluated it on two ground truth datasets containing 7400 forenames and 25,000 surnames (including their verified synonyms). The performance of SpokenName2Vec was found superior to the 10 other algorithms evaluated, including phonetic encoding, string similarity, and machine learning algorithms. The results obtained emphasize the potential of spoken name embeddings for improved synonym suggestion.

In a railway vehicle air brake system, a brake pipe is used to connect all related elements, for example, the air source system, brake control device, and brake valve. The brake pipe has a significant impact on the safety and reliability of trains. This paper presents a fracture analysis of a brake pipe installed on the bogie of a metro train running on one metro line in China. The analysis was conducted using a brake pipe modal test in a depot, a bogie vibration test, and a brake pipe dynamic stress measurement applied at the field site. The field experiment results indicate that the main cause of a brake pipe fracture is the coupling resonance between the first-order lateral bending eigenmode of the brake pipe (74 Hz) and the eigenmode of the reverse yawing of two side beams of the bogie frame (73 Hz). The wheel out-of-roundness is the main excitation source. To solve the pipe fracture problem, a dynamic stress analysis method that considers the frequency and time domains is presented to assess the possible measures. The measures investigated in this study are the pipe wall thickness, pipe clamp stiffness (elastic modulus), and pipe clamp numbers. The simulation results and on-track tests show that doubling the pipe wall thickness and decreasing the elastic modulus of the pipe clamp by 5.67 fold can reduce the dynamic stress amplitude of the pipe by approximately 60%. If a pipe clamp is added at the middle position between the two pipe clamps on the pipe, the dynamic stress amplitude of the pipe will decrease by 94% compared to the present pipe structure. This is because the additional pipe clamp alters the frequency of the first-order lateral bending eigenmode of the brake pipe to avoid being equal to the eigenfrequency of the reverse yawing of the two side beams of the bogie frame. After the metro corporation added a pipe clamp and applied a regular wheel re-profiling, no brake pipe fracture occurred on the investigated metro line.

Introduction Several drugs of abuse (DOA) are capable of modulating neurohypophysial hormones, such as oxytocin (OT) and vasopressin (VP), potentially resulting in the development of psychological abnormalities, such as cognitive dysfunction, psychoses, and affective disorders. Efavirenz (EFV), widely used in Africa and globally to treat HIV, induces diverse neuropsychiatric side effects while its abuse has become a global concern. The actions of EFV may involve neurohypophysial system (NS) disruption like that of known DOA. This study investigated whether sub-chronic EFV exposure, at a previously-determined rewarding dose, alters peripheral OT and VP levels versus that of a control, ∆9-tetrahydrocannabinol (∆9-THC), methamphetamine (MA) and cocaine. Materials and methods To simulate the conditions under which reward-driven behavior had previously been established for EFV, male Sprague Dawley rats (n = 16/exposure) received intraperitoneal vehicle (control) or drug administration across an alternating sixteen-day dosing protocol. Control administration (saline/olive oil; 0.2 ml) occurred on odd-numbered and drug administration (EFV: 5 mg/kg, ∆9-THC: 0.75 mg/kg, MA: 1 mg/kg, or cocaine: 20 mg/kg) on even-numbered days followed by euthanasia, trunk blood collection and plasma extraction for neuropeptide assay. Effect of drug exposure on peripheral OT and VP levels was assessed versus controls and quantified using specific ELISA kits. Statistical significance was determined by Kruskal-Wallis ANOVA, with p, Highlights • Efavirenz alters rat plasma neuropeptide levels in a similar manner to illicit drugs. • Opposed to cocaine, methamphetamine reduces plasma vasopressin rather than oxytocin. • Efavirenz and ∆−9-tetrahydrocannabinol comparably affect peripheral neuropeptides. • Efavirenz reduces vasopressin more than oxytocin versus ∆−9-tetrahydrocannabinol.

An idiom is a common phrase that means something other than its literal meaning. Detecting idioms automatically is a serious challenge in natural language processing (NLP) domain applications like information retrieval (IR), machine translation and chatbot. Automatic detection of Idioms plays an important role in all these applications. A fundamental NLP task is text classification, which categorizes text into structured categories known as text labeling or categorization. This paper deals with idiom identification as a text classification task. Pre-trained deep learning models have been used for several text classification tasks; though models like BERT and RoBERTa have not been exclusively used for idiom and literal classification. We propose a predictive ensemble model to classify idioms and literals using BERT and RoBERTa, fine-tuned with the TroFi dataset. The model is tested with a newly created in house dataset of idioms and literal expressions, numbering 1470 in all, and annotated by domain experts. Our model outperforms the baseline models in terms of the metrics considered, such as F-score and accuracy, with a 2% improvement in accuracy.