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As a result of their cell structures, elastomeric foams exhibit high compressibility and are frequently used as buffer cushions in energy absorption. Foam pads between two surfaces typically withstand uniaxial loads. In this paper, we considered the effects of porosity and cell size on the mechanical behavior of random elastomeric foams, and proposed a constitutive model based on an artificial neural network (ANN). Uniform cell size distribution was used to represent monodisperse foam. The constitutive relationship between Cauchy stress and the four input variables of axial stretch λU, lateral stretch λL, porosity φ, and cell size θ was given by con-ANN. The mechanical responses of 500 different foam structures (20% < φ < 60%, 0.1 mm < θ < 0.5 mm) under compression and tension loads (0.4 < λU < 3) were simulated, and a dataset containing 100,000 samples was constructed. We also introduced a pre-ANN to predict lateral stretch to address the issue of missing lateral strain data in practical applications. By combining physical experience, we chose appropriate input forms and activation functions to improve ANN’s extrapolation capability. The results showed that pre-ANN and con-ANN could provide reasonable predictions for λU outside the dataset. We can obtain accurate lateral stretch and axial stress predictions from two ANNs. The porosity affects the stress and λL, while the cell size only affects the stress during foam compression.

The LuTan-1(LT-1), known as the L-band differential interferometric synthetic aperture radar (SAR) satellite system, is an essential piece of civil infrastructure in China, providing extensive applications such as surface deformation monitoring and topographic mapping. To achieve high-resolution and wide-swath (HRWS) observation abilities, the LT-1 takes the dual receiving antenna (DRA) imaging mode as its working mode. However, amplitude and phase errors between channels lead to a mismatch between the reconstruction filter and the multichannel echo signal, worsen the reconstructed azimuth spectrum, and introduce ambiguity targets in the final imaging results, seriously affecting the final imaging quality. In order to better evaluate the channel error and azimuth ambiguity performance of the LT-1 system, this paper proposed an advanced channel consistency correction method and conducted many measured data experiments. The experimental results show that the proposed method is effective, and the LT-1 system has excellent channel error control and azimuth ambiguity performance.

From the Kyoto Protocol to the Copenhagen Conference and the Paris Agreement, eco-environmental problems have gradually become a matter of common concern worldwide. Eco-efficiency (EE) is an essential indicator for measuring levels of sustainable development. This study uses an epsilon-based measure (EBM) model with undesirable outputs to evaluate the EEs of 30 Chinese provinces during the research period 2008 to 2017, and a spatial Durbin model (SDM) to search for the impact factors of EE. The results indicate that most provinces in China have a low EE level. The EE value of the eastern area is higher than are those for the central, western, or northeastern areas. The EE in China as a whole demonstrates an inverted V-shaped trend with a high point in 2011. The SDM shows that economic development level, foreign trade dependence, and technological progress exert significant positive effects on EE, while population density exerts significant negative influences on EE. This paper provides scientific bases for the formulation of policies resulting in sustainable development.

In this paper, we deal with the notion of fuzzy metric space (X,M,∗), or simply X, due to George and Veeramani. It is well known that such fuzzy metric spaces, in general, are not completable and also that there exist p-Cauchy sequences which are not Cauchy. We prove that if every p-Cauchy sequence in X is Cauchy, then X is principal, and we observe that the converse is false, in general. Hence, we introduce and study a stronger concept than principal, called strongly principal. Moreover, X is called weak p-complete if every p-Cauchy sequence is p-convergent. We prove that if X is strongly principal (or weak p-complete principal), then the family of p-Cauchy sequences agrees with the family of Cauchy sequences. Among other results related to completeness, we prove that every strongly principal fuzzy metric space where M is strong with respect to an integral (positive) t-norm ∗ admits completion.

This paper describes the design and development of an active device that was applied to shoulder rotation rehabilitation. It implements a force control in a similar way to the haptic devices that reflect a force on the user’s hand according to the rehabilitation parameters. This device was tested with patients, combining clinician manipulation and programmed exercises. The results were excellent, given that the range of movement of the shoulder increased in most of the pa-tients. The results of the Usability Scale questionnaire were also promising.

In this paper, we reported the fabrication, characterization, and application of carbon nanotube (CNT)-platinum nanocomposite as a novel generation of cathode catalyst in microbial fuel cells (MFCs) for sustainable energy production and wastewater treatment. The efficiency of the carbon nanocomposites was compared by platinum (Pt), which is the most effective and common cathode catalyst. This nanocomposite is utilized to benefit from the catalytic properties of CNTs and reduce the amount of required Pt, as it is an expensive catalyst. The CNT/Pt nanocomposites were synthesized via a chemical reduction technique and the electrodes were characterized by field emission scanning electron microscopy, electronic dispersive X-Ray analysis, and transmission electron microscopy. The nanocomposites were applied as cathode catalysts in the MFC to obtain polarization curve and coulombic efficiency (CE) results. The catalytic properties of electrodes were tested by linear sweep voltammetry. The CNT/Pt at the concentration of 0.3 mg/cm2 had the highest performance in terms of CE (47.16%), internal resistance (551 Ω), COD removal (88.9%), and power generation (143 mW/m2). In contrast, for the electrode with 0.5 mg/L of Pt catalyst, CE, internal resistance, COD removal, and power generation were 19%, 810 Ω, 96%, and 84.1 mW/m2, respectively. So, it has been found that carbon nanocomposite cathode electrodes had better performance for sustainable clean energy production and COD removal by MFC.

The use of Electric Vehicles (EVs) is almost inevitable in the near future for the sake of the environment and our plant’s long-term sustainability. The availability of an Electric-Vehicle-Charging Station (EVCS) is the key challenge that owners are worried about. Therefore, we suggest benefiting from individual EVs that have excess energy and are willing to share it with other EVs in order to maximize the availability of EVCSs without the need to rely on the existing charging infrastructure. The Internet of Electric Vehicles (IoEV) is gradually gaining traction, allowing for a more efficient and intelligent transportation system by leveraging these capabilities between EVs. However, the IoEV is considered a trustless environment, with untrustworthy trading partners such as data sellers, buyers, and brokers. Data exchanged between the EV and the Energy AGgregator (EAG) or EV/EV can be used to analyze users’ behavior and compromise their privacy. Thus, a Vehicle-to-Vehicle (V2V)-charging system that is both secure and private must be established. Several V2V-charging systems with security and privacy features have been proposed. However, even if the transmitted communications are entirely anonymous, anonymity alone will not prevent the tracking adversary from reconstructing the target vehicle’s route. These systems frequently fail to find a balance between privacy concerns (e.g., trade traceability to achieve anonymity, and so on) and security measures. In this paper, we propose an efficient privacy-preserving and secure authentication based on Elliptic Curve Qu–Vanstone (ECQV) for a V2V-charging system that fulfils the essential requirements and re-authentication protocol in order to reduce the overhead of future authentication processes. The proposed scheme utilizes the ECQV implicit-certificate mechanism to create credentials and authenticate EVs. The proposed protocols provide efficient security and privacy to EVs, as well as an 88% reduction in computational time through re-authentication, as compared to earlier efforts.

Communication-based train control systems (CBTCs) have been widely used as crucial systems in urban rail transit networks. CBTCs typically utizes different levels of symmetry structure according to different geographic deployments. While, in practice, CBTCs crashes have destroyed the transportation systems of the whole city level for many times. Based on the extended object-oriented Petri net (EOOPN), this paper proposes a vulnerability model and an evaluation procedure, which are capable of considering the vulnerability factors in both inner system level and equipment level. On the system level, it establishes a complex dynamic communication structure model among the distributed subsystems, while on the equipment level, it details the equipment changing state during train operation. The searching algorithm of EOOPN depicts possible failed paths of CBTCs via the token transition among train&not, &ndash, ground communication EOOPN subnets. The vulnerability calculation is applied to the metro company&rsquo, s in situ CBTCs to illustrate the effectiveness of the approach.

Motivational interviewing (MI) has been increasingly utilized by health care practitioners for many years. MI has been practiced by social workers, nurses, physicians, psychologists, substance use counselors, and many other health care practitioners. Unfortunately, many health care practitioners do not have adequate training in motivational interviewing, and therefore feel ill equipped to utilize this approach when faced with clients who are in need of assessment and coaching. This paper discusses our experiences with a pilot project to implement MI training within an Adolescent SBIRT (Screening, Brief Intervention, Referral to Treatment) content addition to the undergraduate nursing curriculum. It includes discussion of the evaluation, which measured student attitudes towards substance users with the Substance Use Attitudinal Survey (SAAS), student satisfaction with the newly implemented curriculum, and implications for sustainable inclusion of this content and simulation experiences at the undergraduate level to promote MI use by future health care practitioners. Pre- and post-tests (SAAS) were conducted with 51 nursing students, and 56 students completed the satisfaction survey. Overall, students were very satisfied with the implementation of the curriculum, however, we did not see significant changes in SAAS test scores. This may, however, be a positive indicator of a balanced attitude toward substance users. Continuing evaluation of the curriculum change is needed.

Sea wave energy is being increasingly regarded as one of the most promising sources of renewable energy. This paper deals with the modeling and simulation of an onshore wave energy converter system designed by UMBRA GROUP SpA. Several topics are addressed. Starting from the multibody modeling strategy, this paper delves more deeply into the mechanical efficiency evaluation of the ball-screw in the elastohydrodynamic lubrication regime, the core of the energy conversion process, as well as the thermal characterization of the power take-off module, based on the lumped-parameter and finite element method models. High values of ball-screw indirect efficiency have been observed, ranging from 73% to 97%; these results appear even more encouraging when compared to the performance of alternative energy-consuming technologies. Thermal analysis, on the other hand, provided a maximum temperature increase of 40 °C, allowing for the aversion of any structural collapse and the realistic identification of the lubrication regime, which turned out to be mostly mixed. Finally, an inverse multibody dynamic analysis is performed, and the most interesting simulation results are collected to prove the effectiveness of the proposed approach.

A horizontal drum kiln is a traditional method widely used in Southeast Asian countries for producing biochar. An understanding of temperature conditions in the kiln and its influence on biochar properties is crucial for identifying suitable biochar applications. In this study, four agricultural residues (corncob, coconut husk, coconut shell, and rice straw) were used for drum kiln biochar production. The agricultural residues were turned into biochar within 100–200 min, depending on their structures. The suitability of biochar for briquette fuels was analyzed using proximate, ultimate, and elemental analysis. The biochar’s physical and chemical properties were characterized via bulk density, iodine number, pHpzc, SEM, and FTIR measurements. All biochars had low O/C and H/C ratios and negative charge from both carbonyl and hydroxyl groups. Coconut husk and shell biochar had desirable properties such as high heating value and a high amount of surface functional groups which can interact with nutrients in soil. These biochars are thus suitable for use for a variety of purposes including as biofuels, adsorbents, and as soil amendments.

Based on the novel structural online control policy (SOCP) deadlock handling method presented in our previous work, we have shown that for a specific group of use cases it is possible to relax the requirements of the method, providing an improvement in its performance. In the present work, a new type of deadlock-free zone was introduced which enabled the method to improve its efficiency for both bidirectional as well as unidirectional and mixed path systems. For bidirectional systems, a beneficial outcome was obtained by approaching the global problem solution using sequentially solved local problems. For unidirectional and mixed systems, on the other hand, this paper introduces a condition that allows verification of the feasibility of performing process reservations in a staged manner. The fulfillment of this condition means that there is a possibility of obtaining higher efficiency of the transportation system. The effectiveness of the proposed approaches has been verified by simulations. Their results were compared with the results of the original method resulting in a significant improvement.

Optical microfiber-based temperature sensors have been proposed for many applications in a variety of industrial uses, including biomedical, geological, automotive, and defense applications. This increasing demand for these micrometric devices is attributed to their large dynamic range, high sensitivity, fast-response, compactness and robustness. Additionally, they can perform in-situ measurements remotely and in harsh environments. This paper presents an overview of optical microfibers, with a focus on their applications in temperature sensing. This review broadly divides microfiber-based temperature sensors into two categories: resonant and non-resonant microfiber sensors. While the former includes microfiber loop, knot and coil resonators, the latter comprises sensors based on functionally coated/doped microfibers, microfiber couplers, optical gratings and interferometers. In the conclusions, a summary of reported performances is presented.

People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.

The development of the field of digital humanities in recent years has led to the increased use of knowledge graphs within the community. Many digital humanities projects tend to model their data based on CIDOC-CRM ontology, which offers a wide array of classes appropriate for storing humanities and cultural heritage data. The CIDOC-CRM ontology model leads to a knowledge graph structure in which many entities are often linked to each other through chains of relations, which means that relevant information often lies many hops away from their entities. In this paper, we present a method based on graph walks and text processing to extract entity information and provide semantically relevant embeddings. In the process, we were able to generate similarity recommendations as well as explore their underlying data structure. This approach was then demonstrated on the Sphaera Dataset which was modeled according to the CIDOC-CRM data structure.

Agricultural systems in New Zealand, as elsewhere in the world, are subject to increasing environmental (and associated social) pressures, for example, around water quality and greenhouse gas emissions. Whilst novel, knowledge-based, alternative land use systems, exist that could relieve these pressures, the challenge facing New Zealand is how to achieve a timely transition to these systems at any meaningful scale. This paper considers the factors that are important to land managers in determining whether or not to change their land use system when the development of an irrigation scheme provides an opportunity for transformative change. A multicriteria decision-making framework using the analytical hierarchy process is used to assess the factors influencing decision makers who are shareholders in the Central Plains Water Scheme in the South Island of New Zealand. As expected, financial factors generally were weighted above other factors in terms of importance. Social, environmental and market factors were rated similarly, whilst regulatory and knowledge factors appeared generally less important. In addition to profitability, the study identified the desire of land managers to simplify complex agricultural systems, their need for scale, their concerns over knowledge competition, their willingness to collaborate and the challenge brought about by &lsquo, cultural path dependency&rsquo, as being important. This suggests that if novel systems can be developed that better meet these needs and concerns as well as addressing the wider environmental and social challenges, then there may be a greater chance of engendering a land use transition.

Social media is a major channel used for communication by professional and social groups. The text posted on social media contains extremely rich information. To capture the development of social enterprises (SEs), this paper examines the tweets posted on Twitter and searches the hashtags on the Twitter Application Programming Interface (API) that SEs deem to be the most important. The results suggest that these tweets can be divided into three content groups (strategy, impact and business). This paper expands this into four dimensions (strategy, impact, business and people) and six indicators (social, opportunity, change, enterprise, network and team) and establishes a conceptual framework of SEs. This paper aims to enhance the understanding of the pertinent issues recently affecting SEs and extract findings that can act as a reference for follow-up studies.

The postures of wheelchair users can reveal their sitting habit, mood, and even predict health risks such as pressure ulcers or lower back pain. Mining the hidden information of the postures can reveal their wellness and general health conditions. In this paper, a cushion-based posture recognition system is used to process pressure sensor signals for the detection of user's posture in the wheelchair. The proposed posture detection method is composed of three main steps: data level classification for posture detection, backward selection of sensor configuration, and recognition results compared with previous literature. Five supervised classification techniques-Decision Tree (J48), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Naive Bayes, and k-Nearest Neighbor (k-NN)-are compared in terms of classification accuracy, precision, recall, and F-measure. Results indicate that the J48 classifier provides the highest accuracy compared to other techniques. The backward selection method was used to determine the best sensor deployment configuration of the wheelchair. Several kinds of pressure sensor deployments are compared and our new method of deployment is shown to better detect postures of the wheelchair users. Performance analysis also took into account the Body Mass Index (BMI), useful for evaluating the robustness of the method across individual physical differences. Results show that our proposed sensor deployment is effective, achieving 99.47% posture recognition accuracy. Our proposed method is very competitive for posture recognition and robust in comparison with other former research. Accurate posture detection represents a fundamental basic block to develop several applications, including fatigue estimation and activity level assessment.

In 2019, the National Archives of Korea (NAK) developed a blockchain recordkeeping platform to conduct R&amp, D on recordkeeping approaches. This paper introduces two types of R&amp, D studies that have been conducted thus far. The first is the use of blockchain transaction audit trail technology to ensure the authenticity of audiovisual archives, i.e., the application of blockchain to a new system. The second uses blockchain technology to verify whether the datasets of numerous information systems built by government agencies are managed without forgery or tampering, i.e., the application of blockchain to an existing system. Government work environments globally are rapidly shifting from paper records to digital. However, the traditional recordkeeping methodology has not adequately kept up with these digital changes. Despite the importance of responding to digital changes by incorporating innovative technologies such as blockchain in recordkeeping practices, it is not easy for most archives to invest funds in experiments on future technologies. Owing to the Korean government’s policy of investing in digital transformation, NAK’s blockchain recordkeeping platform has been developed, and several R&amp, D tasks are underway. Hopefully, the findings of this study will be shared with archivists around the world who are focusing on the future of recordkeeping.

In this paper, the inference on location parameter for the skew-normal population is considered when the scale parameter and skewness parameter are unknown. Firstly, the Bootstrap test statistics and Bootstrap confidence intervals for location parameter of single population are constructed based on the methods of moment estimation and maximum likelihood estimation, respectively. Secondly, the Behrens-Fisher type and interval estimation problems of two skew-normal populations are discussed. Thirdly, by the Monte Carlo simulation, the proposed Bootstrap approaches provide the satisfactory performances under the senses of Type I error probability and power in most cases regardless of the moment estimator or ML estimator. Further, the Bootstrap test based on the moment estimator is better than that based on the ML estimator in most situations. Finally, the above approaches are applied to the real data examples of leaf area index, carbon fibers’ strength and red blood cell count in athletes to verify the reasonableness and effectiveness of the proposed approaches.

Intelligent traffic management is an important issue for smart cities. City councils try to implement the newest techniques and performant technologies in order to avoid traffic congestion, to optimize the use of traffic lights, to efficiently use car parking, etc. To find the best solution to this problem, Birmingham City Council decided to allow open-source predictive traffic forecasting by making the real-time datasets available. This paper proposes a multi-agent system (MAS) approach for intelligent urban traffic management in Birmingham using forecasting and classification techniques. The designed agents have the following tasks: forecast the occupancy rates for traffic flow, road junctions and car parking; classify the faults; control and monitor the entire process. The experimental results show that k-nearest neighbor forecasts with high accuracy rates for the traffic data and decision trees build the most accurate model for classifying the faults for their detection and repair in the shortest possible time. The whole learning process is coordinated by a monitoring agent in order to automate Birmingham city’s traffic management.

The Northwest (NW) Atlantic has experienced extreme ecological impacts from Marine Heatwaves (MHWs) within the past decade. This paper focuses on four MHW active years (2012, 2016, 2017, and 2020) and the relationship between Sea Surface Temperature anomalies (SSTA), Sea Surface Salinity anomalies (SSSA), North Atlantic Oscillation (NAO), Geopotential Height anomalies (ZA), and anomalous Jet Stream positions (JSPA). Multichannel singular spectrum analysis (MSSA) reveals the strongest temporal covariances between SSSA and SSTA, and JSPA and SSTA for all years, particularly for 2020 (SSSA–SSTA: 50%, JSPA–SSTA: 51%) indicating that this active MHW year was more atmospherically driven, followed by 2012, which had the second highest temporal covariances (SSSA–SSTA: 47%, JSPA–SSTA: 50%) between these parameters. Spatial correlations for SSSA and SSTA between NAO during MHW active years disrupt the long–term (2010–2020) positive relationship in the NW Atlantic. SSSA and JSPA, and SSSA and SSTA were strongly correlated across the NW Atlantic; 2012 SSSA–JSPA correlations were strong and positive between 56–62°W, and 2016, 2017, and 2020 SSSA–JSPA correlations were mostly strong and negative, with strong positive correlations present near the coastline (70–66°W) or off the NW Atlantic shelf (52–48°W). SSSA–SSTA showed the opposite correlations of similar spatial distributions of SSSA–JSPA for all MHW active years. This indicates strong relationships between JSPA, SSSA, and SSTA during MHWs. Understanding the temporal and spatial interplay between these parameters will aid in better monitoring and prediction of MHWs.

“Wasan” is the collective name given to a set of mathematical texts written in Japan in the Edo period (1603–1867). These documents represent a unique type of mathematics and amalgamate the mathematical knowledge of a time and place where major advances where reached. Due to these facts, Wasan documents are considered to be of great historical and cultural significance. This paper presents a fully automatic algorithmic process to first detect the kanji characters in Wasan documents and subsequently classify them using deep learning networks. We pay special attention to the results concerning one particular kanji character, the “ima” kanji, as it is of special importance for the interpretation of Wasan documents. As our database is made up of manual scans of real historical documents, it presents scanning artifacts in the form of image noise and page misalignment. First, we use two preprocessing steps to ameliorate these artifacts. Then we use three different blob detector algorithms to determine what parts of each image belong to kanji Characters. Finally, we use five deep learning networks to classify the detected kanji. All the steps of the pipeline are thoroughly evaluated, and several options are compared for the kanji detection and classification steps. As ancient kanji database are rare and often include relatively few images, we explore the possibility of using modern kanji databases for kanji classification. Experiments are run on a dataset containing 100 Wasan book pages. We compare the performance of three blob detector algorithms for kanji detection obtaining 79.60% success rate with 7.88% false positive detections. Furthermore, we study the performance of five well-known deep learning networks and obtain 99.75% classification accuracy for modern kanji and 90.4% for classical kanji. Finally, our full pipeline obtains 95% correct detection and classification of the “ima” kanji with 3% False positives.

We report the growth of graphene at a low temperature using the cold wall chemical vapor deposition technique (CWCVD). Few layered (~6-8 layers) graphene were grown on nickel-coated silicon with acetylene as the precursor gas. The advantage of the combination of the acetylene (as a carbon feedstock) and the nickel catalyst was the lowering of the graphene growth temperature. Nickel coated silicon samples were pre-treated (heat treatment in inert atmosphere) before the growth and the effect of the pre-treatment on the catalyst as well as on the grown film was studied. The final samples were characterized with scanning electron microscopy and Raman spectroscopy. In CWCVD route, the heating of only the substrate holder enabled high heating and cooling rates, which, along with the control over partial pressure of the precursor gas had profound effect on the formation of graphene. In the best sample we have achieved almost equal intensity of the G and 2D peaks in Raman spectrum, which implied about ~6-8 layers of Graphene. The defect peak (the D band) was extremely small in the sample and it was attributed to the ripples and the underlying roughness of the nickel film. We analyzed that a proper choice of the thickness of catalyst layer and a higher cooling rate after graphene growth it would be possible to obtain monolayered graphene. Similar samples grown in a normal atmospheric CVD (with some engineered design to promote fast cooling) were also compared with the cold wall CVD grown samples and plasma assisted CWCVD, and cold-wall CVD demonstrated a better control over the quality of graphene film through the fast cooling and a controlled partial pressure of the precursor gas.

Social sciences and humanities (SSH) research is divided across a wide array of disciplines, sub-disciplines and languages. While this specialization makes it possible to investigate the extensive variety of SSH topics, it also leads to a fragmentation that prevents SSH research from reaching its full potential. The TRIPLE project brings answers to these issues by developing an innovative discovery platform for SSH data, researchers&rsquo, projects and profiles. Having started in October 2019, the project has already three main achievements that are presented in this paper: (1) the definition of main features of the GOTRIPLE platform, (2) its interoperability, (3) its multilingual, multicultural and interdisciplinary vocation. These results have been achieved thanks to different methodologies such as a co-design process, market analysis and benchmarking, monitoring and co-building. These preliminary results highlight the need for respecting diversity of practices and communities through coordination and harmonization.

Severe arrhythmias&mdash, such as ventricular arrhythmias&mdash, can be fatal, but treatment options are limited. The effects of a combined formulation of tetrodotoxin (TTX) and lidocaine (LID) on severe arrhythmias were studied. Patch clamp recording data showed that the combination of LID and TTX had a stronger inhibitory effect on voltage-gated sodium channel 1.5 (Nav1.5) than that of either TTX or LID alone. LID + TTX formulations were prepared with optimal stability containing 1 &mu, g of TTX, 5 mg of LID, 6 mg of mannitol, and 4 mg of dextran-40 and then freeze dried. This formulation significantly delayed the onset and shortened the duration of arrhythmia induced by aconitine in rats. Arrhythmia-originated death was avoided by the combined formulation, with a decrease in the mortality rate from 64% to 0%. The data also suggests that the anti-arrhythmic effect of the combination was greater than that of either TTX or LID alone. This paper offers new approaches to develop effective medications against arrhythmias.

In vehicular ad hoc networks (VANETs), due to the fast-moving mobile nodes, the topology changes frequently. This dynamically changing topology produces congestion and instability. To overcome this issue, privacy-preserving optimization-based cluster head selection (P2O-ACH) is proposed. One of the major drawbacks analyzed in the earlier cluster-based VANETs is that it creates a maximum number of clusters for communication that leads to an increase in energy consumption which reflects in a degradation of the performance. In this paper, enhanced rider optimization algorithm (ROA)-based CH selection is performed and that optimally selects the CH so that effective clusters are created. By analyzing this, the behavior of the bypass rider’s CH is chosen, and this forms the optimized clusters, and during the process of transmission, privacy-preserving mobility patterns are used to secure the network from all kinds of malfunctions which are performed by the new vehicle blending and migration process. The proposed P2O-ACH is simulated using NS-2, and for performance analysis, two scenarios are taken, which contain a varying number of vehicles and varying speeds. For a varying number of vehicles and speeds, the considered parameters are energy efficiency, energy consumption, network lifetime, packet delivery ratio, packet loss, network latency, network throughput, and routing overhead. From the results, it is understood that the proposed method performed better when compared with earlier work, such as GWO-CH, ACO-SCRS, and QMM-VANET.

As an important part of the urban environment, trees have certain risks while living in harmony with humans. For example, the failure of trees in extreme weather may cause casualties and damage to public and private; the decline and death of old and valuable trees can have an impact on the diversity and cultural value of trees. This paper outlines the theories related to tree risk and the development of tree risk assessment, evaluates the advantages and disadvantages of various tree risk assessment methods in existing studies, and explains some factors affecting the bearing capacity and related applications using knowledge of tree mechanics. Approaches in modern probing techniques are applied to study the response and loading of tree crowns and branches under wind loads, the application of different non-destructive testing techniques in visual assessment for detecting internal defects and root distribution of trees, and the role and impact of objective quantitative test results on tree risk assessment. Finally, the future development direction of tree risk assessment is predicted, which provides an important reference for research on tree risk assessment.

In recent years, education institutions have offered a wide range of course selections with overlaps. This presents significant challenges to students in selecting successful courses that match their current knowledge and personal goals. Although many studies have been conducted on Recommender Systems (RS), a review of methodologies used in course RS is still insufficiently explored. To fill this literature gap, this paper presents the state of the art of methodologies used in course RS along with the summary of the types of data sources used to evaluate these techniques. This review aims to recognize emerging trends in course RS techniques in recent research literature to deliver insights for researchers for further investigation. We provide a systematic review process followed by research findings on the current methodologies implemented in different course RS in selected research journals such as: collaborative, content-based, knowledge-based, Data Mining (DM), hybrid, statistical and Conversational RS (CRS). This study analyzed publications between 2016 and June 2020, in three repositories; IEEE Xplore, ACM, and Google Scholar. These papers were explored and classified based on the methodology used in recommending courses. This review has revealed that there is a growing popularity in hybrid course RS and followed by DM techniques in recent publications. However, few CRS-based course RS were present in the selected publications. Finally, we discussed future avenues based on the research outcome, which might lead to next-generation course RS.

WebShell is a common network backdoor attack that is characterized by high concealment and great harm. However, conventional WebShell detection methods can no longer cope with complex and flexible variations of WebShell attacks. Therefore, this paper proposes a deep super learner for attack detection. First, the collected data are deduplicated to prevent the influence of duplicate data on the result. Second, to detect the results of the algorithm, static and dynamic feature are taken as the feature of the algorithm to construct a comprehensive feature set. We then use the Word2Vec algorithm to vectorize the features. During this period, to prevent the outbreak of the number of features, we use a genetic algorithm to extract the validity of the feature dimension. Finally, we use a deep super learner to detect WebShell. The experimental results show that this algorithm can effectively detect WebShell, and its accuracy and recall are greatly improved.

Coal-accompanying gas is an essential resource, with numerous mining methods. The practice has proved that injecting high-pressure air into the coal seam can replace and flush the gas in the coal seam, effectively solving the problem of inadequate single gas drainage in soft and low permeability coal seams. This paper uses the finite element method to solve the model, simulate and study the gas drainage by high-pressure air injection in the bedding drilling, and establish a fluid-structure coupling model for gas drainage by high-pressure air injection. The competitive adsorption of N2, O2, and CH4, diffusion and migration of CH4 in coal matrix and fissure, matrix deformation caused by CH4 adsorption, and desorption and control of coal deformation by applied stress are considered in the model. When the fixed extraction time is 600 days (d), the optimal spacing between the extraction hole and injection hole is 12.5 m. The safe extraction effect and minimum drilling amount can be ensured. It provides a basis for guiding gas drainage by injecting high-pressure air on-site.

This invited paper demonstrated an ultra-highly sensitive ammonia (NH3) sensor based on the light-induced thermoelastic spectroscopy (LITES) technique for the first time. A quartz tuning fork (QTF) with a resonance frequency of 32.768 kHz was employed as a detector. A fiber-coupled, continuous wave (CW), distributed feedback (DFB) diode laser emitting at 1530.33 nm was chosen as the excitation source. Wavelength modulation spectroscopy (WMS) and second-harmonic (2f) detection techniques were applied to reduce the background noise. In a one scan period, a 2f signal of the two absorption lines located at 6534.6 cm−1 and 6533.4 cm−1 were acquired simultaneously. The 2f signal amplitude at the two absorption lines was proved to be proportional to the concentration, respectively, by changing the concentration of NH3 in the analyte. The calculated R-square values of the linear fit are equal to ~0.99. The wavelength modulation depth was optimized to be 13.38 mA, and a minimum detection limit (MDL) of ~5.85 ppm was achieved for the reported NH3 sensor.

Background: Articles published in scholarly journals form part of the scientific evidence base. It is the responsibility of the scientific community to maintain its integrity. In 2011 the BMJ commissioned a feature article to draw attention to an article that had appeared in another journal- The Lancet 13 years previously. The Lancet had already retracted the article. These actions exemplify the best traditions of scientific record-keeping. Objective: This submission examines whether the main claims summary made in the BMJ were factual. Method: We examine what was published in the Lancet against what was published in the BMJ and verify against the findings in the GMC hearings transcripts and verdict of the UK High Court. Results: The 6 points highlighted in BMJ had errors and need to be corrected. Conclusions: There are significant differences between what was reported in the Lancet paper and what was alleged to be there by the BMJ. This article aims only to point to errors in the BMJ article, to set the record straight. It does not show there was a causal association between MMR vaccination and autism.

In this paper [1], the author, Rosa Lasaponara, should have been numbered as 1 and 6, instead of only 6.[...]

This paper tracked hepatitis B patients from Medan, Indonesia to Penang, Malaysia under transnational medical care and has an understanding of their medical history and socioeconomic status. The condition of these patients improved as a result of good compliance with medical treatment, including lifestyle adjustment and regular medication. Under the influence of the marketization of healthcare, transnational medical patients in the social structure, based on their economic ability and socioeconomic status, may be expected to experience health inequalities. People with unhealthy medical distribution and weak socioeconomic status are easily prone to diseases due to environmental and social conditions; it is easier for such patients to delay or give up their medical treatment. After continuous tracking and increasing patient exposure to medical knowledge and self-care management opportunities, increasing awareness, screening, care, and treatment, the transmission of hepatitis B can be reduced to enable them to gain upward mobility by their capacities and thus improve their health. Social mobility is deemed the main approach to reduce social inequality. There have been limited medical clinical observations and tracking confirming this theory. This paper, which uses medical observation, confirmed that social mobility is considered as the principal key to reducing inequalities in health.

Truss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched multipoint approximation technique has been introduced to decrease the number of structural analyses by establishing approximate functions instead of the structural analyses in Genetic Algorithm (GA) when GA addresses continuous size variables and discrete topology variables. For large-scale trusses with a large number of design variables, an enormous change in topology variables in the GA causes a loss of approximation accuracy and then makes optimization convergence difficult. In this paper, a technique named the label–clip–splice method is proposed to improve the above hybrid method in regard to the above problem. It reduces the current search domain of GA gradually by clipping and splicing the labeled variables from chromosomes and optimizes the mixed-variables model efficiently with an approximation technique for large-scale trusses. Structural analysis of the proposed method is extremely reduced compared with these single metaheuristic methods. Numerical examples are presented to verify the efficacy and advantages of the proposed technique.

This paper presents a benchmarking study of the water and wastewater industry in Italy. A three-stage modeling approach was implemented to measure the efficiency of 53 utility operators. This approach is based on the implementation of network and conventional data envelopment analysis (DEA) to model the production process of the water service utility operators. In comparison to the conventional black-box or one-stage production model generally adopted in previous studies, the proposed approach provides information relative to the different efficiency components of the stages and blocks of the water service production process and its overall efficiency. Further, by shifting the efficiency analysis to a two-dimensional performance space, i.e., resource and market-efficiency, it offers a more comprehensive view of the water service industry and allows accounting for different business goals at the same time and for an investigation of industry trade-offs. Results show that the operators’ efficiencies in the Italian water service industry are generally variable and low. There are no water service utilities which are 100% efficient from the resource-efficiency perspective, and the maximum efficiency score is 0.545. Efficiency measurements suggest that there is a general orientation of the Italian water industry to not invest in upgrading and improving the infrastructure assets, and achieving an acceptable efficiency in the operations is critical to delivering water services to market in an efficient way. Only one utility operator is 100% efficient from the market-efficiency perspective. The low tariffs adopted by the water service operators do not allow the gaining of satisfactory service remuneration and the achievement of long-term business sustainability. The joint analysis of the resource and market efficiency scores indicates that there is a trade-off between the corresponding business goals.

In this paper, we introduce a new class of robust model selection criteria. These criteria are defined by estimators of the expected overall discrepancy using pseudodistances and the minimum pseudodistance principle. Theoretical properties of these criteria are proved, namely asymptotic unbiasedness, robustness, consistency, as well as the limit laws. The case of the linear regression models is studied and a specific pseudodistance based criterion is proposed. Monte Carlo simulations and applications for real data are presented in order to exemplify the performance of the new methodology. These examples show that the new selection criterion for regression models is a good competitor of some well known criteria and may have superior performance, especially in the case of small and contaminated samples.

Research Highlights: This paper presents a cross-city empirical study on micro-climatic thermal benefits of urban trees, using machine-learning analysis to identify the importance of several in situ measured tree physiognomy traits for cooling. Background and Objectives: Green infrastructure and trees in particular play a key role in mitigating the urban heat island (UHI) effect. A more detailed understanding of the cooling potential of urban trees and specific tree traits is necessary to support tree management decisions for cooling our progressively hot cities. The goal of this study was to identify the influence and importance of various tree traits and site conditions. Materials and Methods: Surface temperature, air temperature at 1.1 m and at tree crown height, as well as wet bulb globe-temperature of shaded and fully sun-exposed reference areas, were used to study the cooling effect of seven different urban tree species. For all 100 individuals, tree height, crown base, trunk circumference, crown volume, crown area, leaf area index (LAI) and leaf area density (LAD) were measured. Measurements were conducted in the cities of Dresden, Salzburg, Szeged, and Vienna as representatives for middle European cities in different climate zones. Results: Beside site conditions, tree species, height, height of crown base, as well as trunk circumference, have a great influence on the cooling effect for city dwellers. The trunk circumference is a very valuable indicator for estimating climate regulating ecosystem services and therefore a highly robust estimator for policy makers and tree management practitioners when planning and managing urban green areas for improving the availability and provision of ecosystem services.

This paper demonstrates a predictive method for the spatially explicit and periodic in situ monitoring of surface water quality in a small lake using an unmanned aerial vehicle (UAV), equipped with a multi-spectrometer. According to the reflectance of different substances in different spectral bands, multiple regression analyses are used to determine the models that comprise the most relevant band combinations from the multispectral images for the eutrophication assessment of lake water. The relevant eutrophication parameters, such as chlorophyll a, total phosphorus, transparency and dissolved oxygen, are, thus, evaluated and expressed by these regression models. Our experiments find that the predicted eutrophication parameters from the corresponding regression models may generally exhibit good linear results with the coefficients of determination (R2) ranging from 0.7339 to 0.9406. In addition, the result of Carlson trophic state index (CTSI), determined by the on-site water quality sampling data, is found to be rather consistent with the predicted results using the regression model data proposed in this research. The maximal error in CTSI accuracy is as low as 1.4% and the root mean square error (RMSE) is only 0.6624, which reveals the great potential of low-altitude drones equipped with multispectrometers in real-time monitoring and evaluation of the trophic status of a surface water body in an ecosystem.

The high precision speed control of gimbal servo system in magnetically suspended control moment gyro (MSCMG) suffers from periodic torque disturbances, which lead to periodic fluctuations in speed control. This paper proposes a novel multiple phase-shift resonant controller (MPRC) for a gimbal servo system to suppress the periodic torque ripples whose frequencies vary with the operational speed of the gimbal servo motor and high-speed motor. First, the periodic torque ripples caused by cogging torque, flux harmonics and the dynamic unbalance of the high speed rotor are analyzed. Second, the principle and structure of MPRC parallel with proportional integral (PI) controllers are discussed. The design and stability analysis of the proposed MPRC plus PI control scheme are given both for the current loop and speed loop. The closed-loop stability is ensured by adjusting the phase in the entire operational speed range. Finally, the effectiveness of the proposed control method is verified through simulation and experimental results.

This paper proposes the use of a pseudo-3D ball-lattice artifact to characterize a handheld laser scanner from a metrological standpoint. The artifact allows the computation of local and global errors in measurement by using the reference-frame-independent parameters of size, form, and distance within the measuring volume of the scanner, and in a single point cloud, without the need for registration. A set of tests was performed using the whole measuring volume, and three acquisition parameters, namely the orientation of the sweeps during the scans, the exposure time, and the distance to the scanner were analyzed for their effects on the accuracy of the scan data. A composite error including the errors in measuring size, form, and distance was used as a single figure of merit to characterize the performance of the scanner in relation to the data-acquisition parameters. The orientation of sweeps did not have a considerable effect on the errors. The accuracy of the scan data was strongly affected by exposure time and its interaction with the distance at which the artifact was scanned. The errors followed a quadratic trend with respect to the distance of the artifact to the scanner. The tested scanner performed best at its manufacturer’s recommended stand-off distance.

Changes in the world’s situation and the need to be competitive and less dependent on the global economy lead to improvements in many organisations. Together with business model transformation, using the latest technologies such as augmented reality, autonomous robots, mobile technologies, big data, cloud computing, and others known as an Industry 4.0 pillars, enterprises are changing the industry status quo. These technologies are widely described and studied in the literature, mainly on the example of large production companies. In order to fill the gap, this article had two objectives. The first objective was to investigate the usage of Industry 4.0 technologies in the time of the COVID-19 pandemic. The second one was to verify the strength of the impact of selected barriers on the level of implementation of individual I4.0 technologies and influence of cooperation during technology implementation. To achieve the objectives of the article, quantitative research was conducted. Data from 125 anonymous survey participants were collected in October 2021 and analysed using a statistical tool. The study results revealed that the term Industry 4.0 is known among industry employees. Surveyed organisations are using I4.0 technologies and still planning to implement them in a wider range despite the pandemic situation. Moreover, the most often-cited barriers in the literature on the subject are costs or employees’ acceptations, too-individualised customer requirements, or insufficient standardisation, which affect the implementation of Industry 4.0 solutions with strengths varying from low to moderate intensity. The information in this paper may be of use to Industry 4.0 solution providers or collaborative and partnership organisations.

Financial transparency, including transparency of transactions, is one of the pillars of sustainability. This study investigates whether a company&rsquo, s ownership structure, including ownership concentration, managerial ownership, and the presence of institutional investors, affects upward real earnings management practices. The research is based on companies listed on the Warsaw Stock Exchange in Poland adapting panel data regression models. The significance and contribution to literature of the paper lies in the fact that we provide evidence that the association between the magnitude of total upward real earnings management and shareholder concentration is U-shaped, thereby indicating that there is an optimal level of ownership concentration, minimizing the magnitude of upward real earnings management and thus increasing financial transparency. Our results show the negative relationship between total upward real earnings management and managerial ownership, thereby we confirm the alignment of interest hypothesis, in terms of real earnings management. We also confirm that individual instruments of real earnings management are linked to ownership concentration and managerial ownership in specific ways. The presence of institutional investors reduces the magnitude of total upward real earnings management.

Research on ethnic entrepreneurship has generated substantial literature on the development of ethnic businesses among different immigrant groups in North America. Such studies tend to focus on the emergence of immigrant entrepreneurship among earlier immigrant groups by highlighting either the group characteristics or the opportunity structure. Existing studies also tend to overlook the importance of innovation in immigrants’ small businesses due to the marginality of immigrant businesses. Thus, there is a dearth of research on new immigrant communities in the USA that illuminates immigrants’ innovative practices. Drawing on the experiences of 50 Bangladeshi entrepreneurs in New York, this research examined how this emerging immigrant group transformed into immigrant entrepreneurs through the investigation of their innovative practices in small business. This paper determined that immigrant entrepreneurs are embedded within the dynamics of the immigration trajectory and the broader context of American society. Although these new immigrants were driven towards the lower end of the economy, this study found that innovations have expanded the breadth and depth of their businesses and made their businesses different and rewarding.

A thorough understanding of mining-induced overburden deformation characteristics and the associated stress redistributions are essential to effectively manage complex safety and environmental issues that arise from underground mining. This is particular for mining in deep environments. This paper presents a numerical modelling study on a kilometer-deep longwall coal mine where a thick sandstone aquifer is situated approximately 200 m above the working seam. The mine adopts a special mine layout with narrow and wide pillars between longwalls and areas to manage water inrush and coal burst risks. The modelling results show that overburden deformation stops at a certain height, above which the displacement profile over multiple longwall panels become nearly flat. Increasing panel width and extraction height lead to a greater extent of the fractured zone and a larger magnitude of surface subsidence. An extraction height of more than 7 m may breach the thick aquifer. Stress concentration on the wide pillar can undergo up to 5 times increase in the in-situ stress, posing high risks of coal burst. Adjusting mining parameters such as panel width and extraction height can facilitate an effective strategy to minimize water inrush and coal burst risks in such a mining condition.

The Pech Merle spotted horses have been one of the key lines of evidence put forward in support of the notion Upper Palaeolithic cave depictions relate to a concern for the supernatural. Recent findings from genetics has cast doubt on this notion in confirming that such horses actually existed during the Upper Palaeolithic in Europe and therefore it is possible real, rather than fictitious, horses were being portrayed. As well as examining criticisms in response to this finding, the present paper presents further evidence to support the possibility real horses were portrayed and the implications for explanations that continue to rely on the supernatural.

The decision sight distance (DSD) at freeway exits is a major factor affecting traffic safety. Based on the Hechizhai Interchange in Xi’an City (Shaanxi Province, China), this paper designs a simulation experiment. Through a simulator study and a questionnaire survey, this paper discusses the impact of the DSD, 1.25 times the stopping sight distance (SSD) and a circular curve deflection on a driver’s driving state (including steering wheel angle rate and steering wheel angle frequency domain). Thirty volunteers participated in this research. The result shows that (1) it is safer to drive on an exit that meets DSD. (2) If it only meets the 1.25 times the SSD requirement, the overloaded driving tasks and operation would be more likely to cause crashes. The driving state of the driver on the right circular curve is obviously better than that on the left circular curve, because changing lanes to the right on the left circular curve does not meet the driver’s expectations. (3) Left and right circular curve should be treated differently in the driving area and the constant sight distance requirements should not be applied. (4) The left circular curve should be more stringent to ensure driving safety.

The mining industry sector is notable for the severe service loads and varied environmental conditions that it imposes on its equipment and mechanical systems. It has become essential to identify the causes of failures and use the information to avoid similar failures and improve projects. In this paper, a study on shaft failure in a speed reduction box was carried out. A section of a fractured shaft made of hardened austempered steel was analyzed to determine the cause of the break. Fractography was performed to characterize the failure mode on the fracture surface. The microstructural analysis and hardness profile revealed that the shaft was inadequately heat treated, resulting in low resistance microstructures and the development of a thin layer of bainite at the shaft edge. Large amounts of inclusions were found in the fracture region, and the tensile tests revealed that the material had an elongation below the specification. The analyses showed that the combination of factors of a large amount of inclusions present in the low resistance banded structure, and the presence of concentrated pores in that same region, acted in a synergistic way to decrease the fatigue resistance and fatigue life of the shaft material.

Currently, urbanization driven by global capital flows entails a main trend in many large cites in China, while global capital investment in small inland cities especially in western China is extremely scarce, where their globalization characters the powerful nationalization power and market activation. Dunhuang, a small inland city in western China, has transformed successfully from an agricultural county to an international tourist city, a platform for worldwide cultural communication, and a node city in the Belt and Road region because of its unique and brilliant resources: Mogao Grottoes and Dunhuangology. Therefore, this paper develops a conceptual framework of the multiple cooperative mechanisms and globalization path (MCMGP) of Dunhuang, elaborating the process of industrial transformation, urban globalization, and multiple cooperative mechanisms between government and market actors based on interviewing records and statistics. Findings show that the MCMGP features government-led intervention, resource orientation, and centralization that embodies the driver of state-owned enterprises (SOEs). Also, the MCM in Dunhuang’s globalization contains the mechanism of enrolment, mobilization and action, governance and global marketing, distributed in the two phases. Equally important, in response to the Belt and Road Initiative (BRI) and Silk Road (Dunhuang) International Cultural Expo (SRDICE) from the state, the city government has significantly reinvested and refined cultural tourism via governance mechanisms, carving out a key node city in the Silk Road and elevating an international tourist city. Environmentally, Dunhuang’s tourism internationalization enhances the process of the development of a sustainable shared mobility industry. Furthermore, its tourism development and social–ecology system maintain the synergistic relationships which international tourism promotes such as urban ecosystem and public welfare and in turn, social–ecological enhancement serve Dunhuang’s international tourism well. Practical implications of how Dunhuang’s experience may have lessons for others are discussed in China’s peculiar socialist market economy discourse.