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Leaf level gas exchange is a widely used technique that provides real-time measurement of leaf physiological properties, including CO2 assimilation (A), stomatal conductance to water vapour (gsw ) and intercellular CO2 (Ci ). Modern open-path gas exchange systems offer greater portability than the laboratory-built systems of the past and take advantage of high-precision infrared gas analyzers and optimized system design. However, the basic measurement paradigm has long required steady-state conditions for accurate measurement. For CO2 response curves, this requirement has meant that each point on the curve needs 1-3 min and a full response curve generally requires 20-35 min to obtain a sufficient number of points to estimate parameters such as the maximum velocity of carboxylation (Vc,max ) and the maximum rate of electron transport (Jmax ). For survey measurements, the steady-state requirement has meant that accurate measurement of assimilation has required about 1-2 min. However, steady-state conditions are not a strict prerequisite for accurate gas exchange measurements. Here, we present a new method, termed dynamic assimilation, that is based on first principles and allows for more rapid gas exchange measurements, helping to make the technique more useful for high throughput applications.

Spectroscopic gas sensors rely on spectral data of molecules available from various databases for analysis of gases. The total internal partition sums (TIPS) for molecules is one of the essential parameter needed for analysis of gases using their absorption spectrum. The calculated values of TIPS are reported by many researchers and are available in tabulated form in HITRAN database. The database is useful in many industrial gas sensors. These sensors require fast response along with portability. However, storing and recalling the TIPS data adds to the computational cost and time. For such applications, a polynomial expression for recalling the TIPS values is a fast and convenient method. The present study provides the polynomial coefficients for TIPS as a function of temperature for the rapid recall of data listed in the HITRAN 2016. The entire temperature range 70–3000 K is covered in three sets of temperatures for polynomial fitting. The lowest-order polynomial expressions which can model the variation of TIPS with temperature with better than 1% accuracy are listed for 49 molecules (125 isotopologues) for the ease of recall. The coefficients of polynomials up to fifth order are also provided in supplementary material for applications requiring higher accuracy. The data will be useful in the research and development of spectroscopic gas sensing technology.

With the popularization of the Internet of Things (IoT) in the home environment, security incidents have become more recurrent with end users. Knowledge sharing on incident mitigation with intrusion prevention systems (IPSs) can improve domestic IoT security, but both signa-ture-based and anomaly-based approaches pose challenges of updating, portability, and privacy. Our previous work extended the RFC 8520 functions and proposed the Intra-Network eXposure analyzer Utility (INXU) as a signature-based IPS to address these challenges. The INXU's architecture solves the signature update problem, and its Malicious Traffic Description (MTD) data model solves the portability and privacy issues. However, the referred MTD data model is prone to high false-positive detection rates due to the lack of contextual information for identifying effective threats. Thus, this work proposes an improvement to the MTD data model that reduces the false-positive detection rates. The concepts from an IoT security ontology are incorporated into the MTD data model to insert contextual information for the decision making process. With this, the present work proposes the improved MTD as a standard data model for sharing knowledge about the mitigation of malicious activities through IoT systems. Experiments using a Mirai botnet variant shows the improved performance provided by the solution herein proposed. The infections are reduced by up to 63 percent when compared to RFC 8520, and the false-positive detection is reduced by approximately 27 percent when compared with the original MTD.

Formal educational qualification is increasingly built into immigrant selection systems in many countries, but in a global context, the transferability and portability of such qualifications has been questioned. In 2013, Canada introduced the requirement for a formal assessment of educational credential equivalence for applicants in the skilled worker category. In this study, we use a Canadian national immigration database and difference-in-differences methodology to investigate whether requiring formal Educational Credential Assessment (ECA) as part of the selection process for skilled immigrants has improved labour market outcomes. Our results indicate that the ECA requirement is positively related to early employment rates and earnings for both men and women. However, this effect is limited to those with no previous employment experience in Canada. We also find that, even with the ECA requirement, significant differences in the earnings of immigrants from different source regions remain. Implications and recommendations are discussed.

Performance portability is a critical issue for the Exascale Computing Project (ECP) because of nontrivial architectural differences between machines available today and those expected at exascale. Many ECP project teams are working toward performance portability, and would expect to benefit from sharing lessons learned, identifying gaps, and discovering opportunities for partnerships. To facilitate this communication, the IDEAS-ECP project partnered with the three focus areas of ECP (application development, software technology, and hardware and integration), and Department of Energy computing facilities, to lead a series of panel discussions on performance portability. The panels were organized around broadly common themes of algorithmic and data locality challenges. In this article, we describe the panel series, its objectives, and perspectives from the various areas of the project. We also discuss use cases that are distinctive, as well as conclusions drawn from the collective experience of the participants.

Pipeline safety early warning (PSEW) systems based on distributed optical fiber sensors are used to recognize and locate third-party events that may damage long-distance energy transportation pipelines and are essential to ensure pipeline safety and energy supply. However, the deployment of PSEW systems in real sites is hindered by the high experimental cost of collecting large real-site data sets for model building and the small percentage of labeled data (typically less than 0.5%). Besides, the optical fiber sensors are sensitive to hardware and the environment, ensuring challenges to directly migrate the old PSEW system for a new deployment. In this study, a novel semi-supervised learning model is proposed to monitor the safety of pipelines in real-time. Concretely, the sparse stacked autoencoder trained with unlabeled data is used to extract more robust features, and the fully-connected network trained with a small amount of labeled data is used for location and identification. Encouraging empirical results on the real-world long-distance energy pipelines of the PipeChina confirm that our method achieves better recognition and localization performance in comparison to the baseline with less labeled data. Further, the model size and recognition latency are reduced by $18.9\times $ and $7.9\times $ of the baseline, respectively. Also, the decoded features have better visualization than the input. This work reduces the cost of PSEW system deployments, improves its performance and portability, and will contribute to the widespread use of PSEW systems in the industry.

State-of-the-art engineering and science codes have grown in complexity dramatically over the last two decades. Application teams have adopted more sophisticated development strategies, leveraging third party libraries, deploying comprehensive testing, and using advanced debugging and profiling tools. In today’s environment of diverse hardware platforms, these applications also desire performance portability—avoiding the need to duplicate work for various platforms. The Kokkos EcoSystem provides that portable software stack. Based on the Kokkos Core Programming Model, the EcoSystem provides math libraries, interoperability capabilities with Python and Fortran, and Tools for analyzing, debugging, and optimizing applications. In this article, we overview the components, discuss some specific use cases, and highlight how codesigning these components enables a more developer friendly experience.

The design and principle of operation of the module for data acquisition and control of a broadband near-IR spectrometer are described. It was developed as the main interface device in the system for visualizing intraocular structures using optical coherence tomography. The achieved performance characteristics of this module make it possible to create a spectral diagnostic optical coherence tomography system with a high resolution value of a few micrometers. The USB 3.0 interface is used as the main computer communication channel, thus providing the compactness, portability, and versatility of the diagnostic system. The data-acquisition system is capable of operating against the background of computationally complex asynchronous procedures for synthesizing a continuous flow of tomographic images in real time, due to which the visualization system can operate in an interactive mode.

In the nuclear sciences and radiation detection fields, the differentiation between gamma-ray and neutron interactions inside a detector volume continues to be an area of active research. Historically, the primary mechanism for conducting particle identification has been pulse shape discrimination (PSD). However, almost all variations of this technique rely on only two factors: the area of the tail and the total area of the pulse. In the last decade, the emergence of advanced machine learning techniques, most specifically artificial neural networks (ANNs), offers a unique opportunity to capitalize on the entirety of the waveform. But such techniques appear highly reliant on the quality of datasets used for training. Our research addresses this challenge to quantify the relative performances of networks trained on a variety of datasets and subjected to the same test. Furthermore, we offer an analysis of the portability of a network trained on one detector to a similar detector.

Adaptive mesh refinement (AMR) is an important method that enables many mesh-based applications to run at effectively higher resolution within limited computing resources by allowing high resolution only where really needed. This advantage comes at a cost, however: greater complexity in the mesh management machinery and challenges with load distribution. With the current trend of increasing heterogeneity in hardware architecture, AMR presents an orthogonal axis of complexity. The usual techniques, such as asynchronous communication and hierarchy management for parallelism and memory that are necessary to obtain reasonable performance are very challenging to reason about with AMR. Different groups working with AMR are bringing different approaches to this challenge. Here, we examine the design choices of several AMR codes and also the degree to which demands placed on them by their users influence these choices.

Alok Moharir,1,2 Joseph D Tobias1,2 1Department of Anesthesiology & Pain Medicine, Nationwide Children’s Hospital, Columbus, OH, USA; 2Department of Anesthesiology & Pain Medicine, The Ohio State University College of Medicine, Columbus, OH, USACorrespondence: Alok MoharirDepartment of Anesthesiology & Pain Medicine, Nationwide Children’s Hospital, 700 Children’s Drive, Columbus, OH, 43205, USATel +1 614 722-4200Fax +1 614 722-4203Email Alok.Moharir@Nationwidechildrens.orgAbstract: Point-of-care ultrasound is a growing field within anesthesiology as well as several other medical and surgical specialties. The recent development of handheld ultrasound devices has the potential to expand the use or this technology by decreasing the cost and increasing accessibility for the healthcare provider. These handheld devices may be used to assist with education, training, and even direct patient care. We outline our process for using a handheld ultrasound device, the Lumify, to assist in the completion of a point-of-care ultrasound certificate program offered by the American Society of Anesthesiologists. We outline the unique advantages of this device as it relates to cost, portability, and applicability of its web-based technology compared to a traditional standalone ultrasound machine for the completion of this course.Keywords: ultrasound, point-of-care ultrasound, POCUS, hand-held

Augmented Reality (AR) is more than an added value for Cultural Heritage (CH); it is vital for its sustainability, promotion and dissemination, increasing accessibility in CH even during difficult periods of time, like the Covid-19 pandemic. In order to be meaningful and engaging, an AR application should have the following characteristics: easiness of use, high-quality representations and compatibility. This paper presents a marker-less mobile AR application for the display and inspection of high-resolution 3D cultural assets, overlayed on a particular location in the real-world scene. Instead of predefined markers, an image captured by the user is exploited as a pattern for real-time feature matching, pose estimation and scene augmentation. Our approach is based on pure computer vision and photogrammetric techniques, implemented using native C++ and Java code for Android mobile platforms. It is built with the use of the OpenCV library and the OpenGL ES graphics API without any dependencies of AR Software Development Kits (SDKs). Therefore, it supports cross-vendor portability regarding mobile model devices and hardware specifications. The evaluation of the developed application examines the performance of various matching techniques and the overall responsiveness of processing and 3D rendering on mid-range and low-end smartphones. The results showcase the reliability and responsiveness of the pattern recognition as well as the potential of the 3D graphics engine to render and overlay complex 3D models balancing between visual quality and time. The proposed methodology is applied to the Ciborium of the church of St. Charalabos, located at St. Stephen’s Monastery in Meteora, Greece.

Crowd counting plays a significant role in crowd monitoring and management, which suffers from various challenges, especially in crowd-scale variations and background interference issues. Therefore, we propose a method named depth and edge auxiliary learning for still image crowd density estimation to cope with crowd-scale variations and background interference problems simultaneously. The proposed multi-task framework contains three sub-tasks including the crowd head edge regression, the crowd density map regression and the relative depth map regression. The crowd head edge regression task outputs distinctive crowd head edge features to distinguish crowd from complex background. The relative depth map regression task perceives crowd-scale variations and outputs multi-scale crowd features. Moreover, we design an efficient fusion strategy to fuse the above information and make the crowd density map regression generate high-quality crowd density maps. Various experiments were conducted on four main-stream datasets to verify the effectiveness and portability of our method. Experimental results indicate that our method can achieve competitive performance compared with other superior approaches. In addition, our proposed method improves the counting accuracy of the baseline network by $$15.6\%$$ .

Emerging persistent memory in commodity hardware allows byte-granular accesses to persistent state at memory speeds. However, to prevent inconsistent state in persistent memory due to unexpected system failures, different write-semantics are required compared to volatile memory. Transaction-based library solutions for persistent memory facilitate the atomic modification of persistent data in languages where memory is explicitly managed by the programmer, such as C/C++. For languages that provide extended capabilities like automatic memory management, a more native integration into the language is needed to maintain the high level of memory abstraction. It is shown in this paper how persistent software transactional memory (PSTM) can be tightly integrated into the runtime system of Haskell to atomically manage values of persistent transactional data types. PSTM has a clear interface and semantics extending that of software transactional memory (STM). Its integration with the language’s memory management retains features like garbage collection and allocation strategies, and is fully compatible with Haskell's lazy execution model. Our PSTM implementation demonstrates competitive performance with low level libraries and trivial portability of existing STM libraries to PSTM. The implementation allows further interesting use cases, such as persistent memoization and persistent Haskell expressions.

Wearable electronic devices have attracted significant attention as important components in several applications. Among various wearable electronic devices, interest in textile electronic devices is increasing because of their high deformability and portability in daily life. To develop textile electronic devices, fiber-based electronic devices should be fundamentally studied. Here, we report a stretchable and sensitive fiber strain sensor fabricated using only harmless materials during an in situ formation process. Despite using a mild and harmless reducing agent instead of typical strong and hazardous reducing agents, the developed fiber strain sensors feature a low initial electrical resistance of 0.9 Ω/cm, a wide strain sensing range (220%), high sensitivity (∼5.8 × 104), negligible hysteresis, and high stability against repeated stretching-releasing deformation (5000 cycles). By applying the fiber sensors to various textiles, we demonstrate that the smart textile system can monitor various gestures in real-time and help users maintain accurate posture during exercise. These results will provide meaningful insights into the development of next-generation wearable applications.

Image-based diagnostics are key for fusion experiments. The operating conditions at ITER and the future machines require changing the role of such systems from monitoring and archiving for offline postprocessing to real-time processing. One of the roles of such systems is machine protection. A relevant application of vision diagnostics is the wall and divertor temperature monitoring and hot spot detection. However, algorithms for hot spot detection are computationally costly. To achieve real-time performance at the required time resolution for all these experiments, evaluating and validating the newest technologies is vital. This work applies heterogeneous computing techniques based on the OpenCL standard to the real-time hot spot detection problem and obtains the performance values in a Micro Telecommunications Computer Architecture (MTCA) platform. OpenCL reduces the development time, improves portability, and simplifies the evaluation and validation of each part of the algorithm to find the best-suited device in the heterogeneous system. The proposed solution enables balancing the computational load between a field-programmable gate array (FPGA) and a graphical processing unit (GPU). The algorithm has been adapted and optimized, taking profit on the particularities of each platform.

The driving forces behind the rapid development of accessible 3d modelling acquisition are generally economic. As the requirements for on-site data acquisition technology become cheaper and more user friendly, opportunities for the geographic dislocation of expertise become more viable. In effect, much of the diagnosis of a monuments’ morphology or condition can be made remotely, as a virtual model is constructed. This potential portability serves to reduce the impact, invasiveness and cost of survey and documentation processes. In cases of contested heritage conservation practices, the simple act of photographic recording can cause concern. However, photogrammetric recording is eminently advantageous for its capacity to provide non-destructive means to consider degradation and condition mapping as well as to record and monitor change over time. Here, two rapid surveys taken with portable 360° cameras a year apart, demonstrate the potential value and limitations of deploying recent techniques in order to deliver credible or useful survey data in a highly complex pillared hall that is intensively occupied.

Network-in-a-box (NIB) aims to provide personalized private network services in mission-crit-ical situations. Innovation in business models in industry 4.0 would require NIB to get positioned anywhere and provision seamless Internet connectivity. However, legacy technologies restrict the vision of NIB to become feasible due to the lack of efficient mobility management, portability, all-dimension network coverage and privacy aware service mitigation. In this study, we elaborate on the significance of NIB upon integration with 6G, that is, 6G-NIB for serving actual use cases in the future. We present a visionary augmentation about 6G-NIB on top of vital aspects, such as key requirements, technological aspects, use cases, deployment schemes, variants, and open research issues and future directions. We learn that 6G-NIB would become feasible, reliable and resilient upon true comprehension of earlier aspects. We recommend that applications related to industry 4.0 might benefit from 6G-NIB when proper connectivity features are sought.

Automated insulin delivery (AID) is the most recent advance in type 1 diabetes (T1D) management. It has the potential to achieve glycemic targets without disabling hypoglycemia, to improve quality of life and reduce diabetes distress and burden associated with self-management. Several AID systems are currently licensed for use by people with T1D in Europe, United States, and the rest of the world. Despite AID becoming a reality in routine clinical practice over the last few years, the commercially hybrid AID and other systems, are still far from a fully optimized automated diabetes management tool. Implementation of AID systems requires education and support of healthcare professionals taking care of people with T1D, as well as users and their families. There is much to do to increase usability, portability, convenience and to reduce the burden associated with the use of the systems. Co-design, involvement of people with lived experience of T1D and robust qualitative assessment is critical to improving the real-world use of AID systems, especially for those who may have greater need. In addition to this, information regarding the psychosocial impact of the use of AID systems in real life is needed. The first commercially available AID systems are not the end of the development journey but are the first step in learning how to optimally automate insulin delivery in a way that is equitably accessible and effective for people living with T1D.

The study designed and developed RECORDS (Research and Extension Completed and On-going Registered programs Database System which will serve as the central storage of data of SLSU Research and Extension offices. The development of the system used VB.Net as a programming language and MySQL as database of the system application to function properly. This research followed the process of developmental research. It was conducted during the school year 2019-2020 at SLSU Campus Lucena The developed software and data base system were evaluated by IT experts, selected members of Research and Extension Council and researchers and extensionists of SLSU. The parameters used in evaluating the developed system were functionality, reliability, usability, efficiency, maintainability, portability and security. The evaluation result showed that the 30 respondents highly accepted the system. Overall rating of the RECORDS evaluation got the average weighted mean of 3.79 with equivalent interpretation of highly acceptable. This implied that, the developed RECORDS (Research and Extension Completed and On-going Registered programs Database System) can be utilized by the SLSU Research and Extension Offices.

Objective: To design, fabricate and evaluate the new features of a portable manual rebar bender, a Hand Tool that is used for bending rebar and other malleable materials. Methods: The researcher improves on the previously available portable manual rebar bender. Experts assessed the acceptability of this bender in terms of design and portability. Furthermore, as previously stated, the study significantly improves the usefulness of the inexpensively produced bending machine for reinforcing concrete with negligible human force. Findings: The device created has a valuable impact on the construction industry’s human force due to its affordability and marketability. Also,the present tools guaranteed strength, durability, and lightness in weight. Furthermore, the portable manual rebar is made of different elements such as a center axle, stationary backup roller, and sleeve. The results after ten trials showed that the portable manual rebar bender could finish 9 mm, 20mm, and 12 mm stirrup at an average of 24.3 seconds, 26.3 seconds, and 27.7 seconds, respectively. Additional results from seven to ten trials showed the bending time for 24 seconds stirrup for 9mm, 26 seconds stirrups for 10 mm, and 28 seconds stirrup for 12 mm. Furthermore, after a series of ten trials, the instrument achieved an accurate angle bent of 90 degrees. The M= 4.77 and 4.82 are very acceptable in terms of design and portability tools based on the evaluators’ rating. Novelty: The design is novel in terms of cost, portability, and efficiency. The device can be a great help in the structures and buildings. Furthermore, the construction industry will use the portable manual rebar bender to build houses and other local infrastructure to test its effectiveness. Then, the researcher is encouraged to engage in another research to try the innovation. Keywords: Improvised; Innovative; Locally made; Device; Bender

Trypanosomatid-caused diseases are among the neglected infectious diseases with the highest disease burden, affecting about 27 million people worldwide and, in particular, socio-economically vulnerable populations. Trypanothione synthetase (TryS) is considered one of the most attractive drug targets within the thiol-polyamine metabolism of typanosomatids, being unique, essential and druggable. Here, we have compiled a dataset of 401 T. brucei TryS inhibitors that includes compounds with inhibitory data reported in the literature, but also in-house acquired data. QSAR classifiers were derived and validated from such dataset, using publicly available and open-source software, thus assuring the portability of the obtained models. The performance and robustness of the resulting models were substantially improved through ensemble learning. The performance of the individual models and the model ensembles was further assessed through retrospective virtual screening campaigns. At last, as an application example, the chosen model-ensemble has been applied in a prospective virtual screening campaign on DrugBank 5.1.6 compound library. All the in-house scripts used in this study are available on request, whereas the dataset has been included as supplementary material., Facultad de Ciencias Exactas, Laboratorio de Investigación y Desarrollo de Bioactivos

BACKGROUND Live streaming surgery is a developing communication platform in medicine. To maximize the technological advances that allow for the live streaming of surgery, it is crucial to have an understanding of the various video-capturing devices that are available and their pros and cons of implementation. Possible barriers to the widespread use of live streaming surgery include cost, concerns about patient safety and privacy, and limited understanding of the current available resources. In this article, we present the results of our literature review of techniques for live streaming of surgery as a means to inform readers and promote their implementation. METHODS We conducted a literature review of the literature to identify previous articles indexed in PubMed and Ovid. We used the following search terms: [Surgery AND Streaming], which generated 32 articles for initial review. References were reviewed within each document to find similar articles that were not captured by the initial search. The article selection criteria were peer-reviewed publications, case reports, and case series describing the use of live surgical streaming technologies. RESULTS Literature review showed enhanced surgeon interaction with viewers and improved anatomy scores with the widespread use of live streaming. Surgeons reported positive feedback and wished to engage in more sessions in the future. The largest barriers to implementation of streaming technology are video quality through the Internet and patient information protection. CONCLUSIONS Live streaming of surgery for educational purposes has not been widely accepted in surgical training programs to date. Streaming accessibility has advanced over the past 2 decades with the availability of handheld mobile devices. However, little has been done to allow for live streaming of surgery to trainees in a manner compliant with the Health Portability Insurance and Accountability Act.

Mobile number portability (MNP) across telecommunication networks entails the movement of a customer from one mobile service provider to another. This, often, is as a result of seeking better service delivery or personal choice. Churning prediction techniques seek to predict customers tending to churn and allow for improved customer sustenance campaigns and the cost therein through an improved service efficiency to customer. In this paper, MNP predicting model using integrated kernel logistic regression (integrated-KLR) is proposed. The Integrated-KLR is a combination of kernel logistic regression and expectation-maximization clustering which helps in proactively detecting potential customers before defection. The proposed approach was evaluated with five others, mostly used algorithms: SOM, MLP, Naïve Bayes, RF, J48. The proposed iKLR outperforms the other algorithms with ROC and PRC of 0.856 and 0.650, respectively.

Assembly torque monitoring is an important aspect of assembly process monitoring. Currently, in manual assembly lines, torque monitoring sensors are usually installed on assembly tools or assembly jigs. This approach is restricted by the working environment and assembly space. To this end, operators can use wearable devices containing sensors without being restricted by the tool and working spaces, with the added advantage of portability. To use wearable electromyography equipment to monitor the assembly torque, this paper proposes an assembly torque monitoring method using surface electromyography (sEMG) and inertial signals. Two torque regression models, one based on a heterogeneous-kernel-based temporal convolutional network (Het-TCN) and the other based on a two-stream convolutional neural network (CNN), are proposed and compared with CNN, long short-term memory (LSTM), and temporal convolutional network (TCN) models. The experimental results show that, compared with using sEMG signals alone, performing an assembly torque data regression using a combination of sEMG and inertial signals can significantly reduce the regression error. The proposed Het-TCN model has the best regression performance, with an average error of 3.31 N m an RMSE of 9.18 %, and a coefficient of determination R2 of 0.72. Thus, the proposed Het-TCN along with the application of both sEMG and inertial signals can help effectively perform assembly torque data regression, which can be used for assembly torque or operation monitoring.

The information-blocking provision of the Cures Act is designed to promote interoperability of health IT systems and mandates immediate access and portability of personal electronic health information for patients, providers and payers. In essence, this legislation requires no delay in access to clinical information including radiology reports once entered into the electronic health record. This is at odds with the current settings of many electronic health record systems, which employ time-delayed releases (embargo) of radiology reports. In such systems, there is a predetermined delay, such as days to weeks, between when a radiology report is signed off by the radiologist and when the report becomes available for patient access via the online patient portal. The idea behind this practice is that the delay allows time for the referring provider to read the report and coordinate care for the patient before the patient becomes aware of potentially abnormal and anxiety-provoking imaging findings. At the time of this writing, it is unclear whether such embargo programs will meet information-blocking definitions and thereby be subject to financial disincentives. Many provider groups are preparing for enforcement of the information-blocking by removing their report embargo programs. This article describes the challenges and opportunities created by the immediate release of radiology reports to patients via online patient portals and suggests strategies that groups may consider to ease their transition to this model of care delivery.

In this paper the author analyses the possible convergences and divergences of two current legislative proposals of the European Commission: the Portability Regulation and the Digital Content Directive. Both proposals address the issue of access to digital content from different perspectives and complement each other, although they use different legal terminology and in some respects have a different scope of application in the question of cross-border portability of the digital content provided for monetary counter performance to consumers. The Portability Regulation focuses on the cross-border portability of digital content [Art. 3 (1) Portability Regulation), while the Digital Content Directive specifies the consumer rights related to the distribution of digital content and subsumes the accessibility of digital content under the concept of “conformity of the digital content with the contract” [Art. 6 (1), (2) Digital Content Directive). The author aims to answer whether a consumer who is not allowed to use the digital content in EU Member States besides the Member State of his residence will be entitled to pursue claims arising from the non-conformity of the digital content with the contract.

With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger , an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.

Biometric systems are becoming increasingly important in today’s society. The Electrocardiogram signal proves a suitable contender for such systems thanks to its universality and robustness to attacks. We implement a cloud-based system for subject authentication using the Electrocardiogram signal and Siamese Neural Networks. The key point of this approach consists in using images of the ECG signal, rather than numerical values, for training and deploying the model in our private cloud orchestrated by OpenStack. The experimental results were obtained using data from 90 subjects: the sensitivity of the authentication system is 87.3%, the False Rejection Rate is 12.7% and the False Acceptance Rate is 13.74%. The overall accuracy of the system is 86.47%. This paper demonstrates the feasibility of an authentication system, deployed in a private cloud orchestrated by OpenStack, which uses Siamese Neural Networks and graphical representations of the ECG signal. Our contribution is two-fold: first, we make use of the inherent properties of Siamese Neural Networks to help simplify the training process and make it easier to enroll new subjects. Second, by deploying the model in our private cloud we not only ensure the portability, but also the scalability and security of the system.

Radiocarbon (14C) analysis of carbon dioxide (CO2) can be extremely useful in carbon cycle studies because it provides unique information that can infer the age and source of this greenhouse gas. Cartridges containing the CO2-adsorbing zeolite molecular sieve are small and highly portable, which makes them more suitable for field campaigns in remote locations compared to some other CO2 collection methods. However, sampling with molecular sieve cartridges usually requires additional equipment, such as an infrared gas analyser, which can reduce portability and pose limitations due to power demands. In addition, 14C analysis of CO2 is increasingly being used in field experiments which require high numbers of replicate CO2 collections, placing extra pressure on an expensive and cumbersome collection apparatus. We therefore designed and built a molecular sieve CO2 sampling kit that utilizes a small, low power CO2 sensor. We demonstrate the reliability of the new kit for the collection of CO2 samples for 14C analysis in a series of laboratory and field tests. This inexpensive sampling kit is small, light-weight, highly portable, and has low power demands, making it particularly useful for field campaigns in remote and inaccessible locations.

The main goal of this ongoing research is the evaluation of the iPad Pro built-in LiDAR sensor for large scale 3D rapid mapping. Different aspects have been considered from the architectural surveying perspective and several analyses were carried out focusing on the acquisition phase and the definition of best practices for data collection, the quantitative analysis on the acquired data and their 3D positional accuracy assessment, and the qualitative analysis of the achievable metric products. Despite this paper is a preliminary analysis and deeper studies in various application environment are necessary, the availability of a LiDAR sensor embedded in a tablet or mobile phone, appears promising for rapid surveying purposes. According to test outcomes, the sensor is able to rapidly acquire reliable 3D point clouds suitable for 1:200 architectural rapid mapping; the iPad Pro could represent an interesting novelty also thanks to its price (compared to standard surveying instruments), portability and limited time required both for data acquisition and processing.

The joint optimal operation of cascade reservoir system can greatly improve the utilization of water resources. However, the complex high-dimensional and non-linear features and calculated costs often hinder the refined operation and management of reservoirs. Recently, the local parallel computing has become an effective way to alleviate the "curse of dimensionality". Current local parallel computing has hardware limitations, which is difficult to adapt to large-scale computing. This study proposes a novel parallel dynamic programming algorithm based on Spark (PDPoS) via cloud computing. The simulation experiments are carried out for a comparative analysis of the solution efficiency, influence factors and stability of cloud computing. The results are as follows: (1) The efficiency of the cloud-based PDPoS is related to some factors; the number of CPU cores is the main influencing factor, followed by the operator, and the architecture has the least influence. (2) The runtime variance of cloud computing is 2.03, indicating cloud computing has high stability. (3) Under the same configuration (i.e., CPU and memory), the runtime of cloud computing is 41.5% ~ 110.3% longer than that of physical machines. However, cloud computing has rich resources, good scalability, and good portability of online operations, which is an attractive alternative for optimal operation of large-scale reservoir system.

The portability and low cost of e-commerce platforms have attracted more and more businesses and consumers. However, due to the virtual nature of e-commerce platforms, there is a serious information asymmetry between merchants and consumers–resulting in a serious credit problem of e-commerce platforms. The trust crisis remains a challenging issue for the development of e-commerce. Existing researches propose to strengthen trust supervision to protect the consumers. However, the trust crisis often hurts multiple parties’ interests. We can neither attribute the cause of dishonest transactions to a single party, nor supervise only a single party. To solve the problem of commercial breach of trust, we propose a margin policy-based consumer-to-business-to-consumer (or MP-C2B2C) model for e-commerce platforms. First, we add a third-party platform supervision mechanism to the C2C e-commerce model by presenting different margin policies. Second, we give the unit price trading mode, dual price trading mode and consumer reporting behavior mode under the margin policies based on two-way games. We investigate the game strategies of each participant in the e-commerce transaction process under MP-C2B2C model by comparing them with different representative game models. The research results show that participants in the transaction will be influenced by the margin system to choose the game strategy with better trustworthiness. Trustworthiness of the e-commerce platform is enhanced under the MP-C2B2C model.

Semantic web and cloud technology systems have been critical components in creating and deploying applications in various fields. Although they are self-contained, they can be combined in various ways to create solutions, which has recently been discussed in depth. We have shown a dramatic increase in new cloud providers, applications, facilities, management systems, data, and so on in recent years, reaching a level of complexity that indicates the need for new technology to address such tremendous, shared, and heterogeneous services and resources. As a result, issues with portability, interoperability, security, selection, negotiation, discovery, and definition of cloud services and resources may arise. Semantic Technologies, which has enormous potential for cloud computing, is a vital way of re-examining these issues. This paper explores and examines the role of Semantic-Web Technology in the Cloud from a variety of sources. In addition, a "cloud-driven" mode of interaction illustrates how we can construct the semantic web and provide automated semantical annotations to web applications on a large scale by leveraging Cloud computing properties and advantages.

Software Quality is the key priority of today’s marketplace and software development organization to which a system, technique, or factor meets particular requirements and conditions. Soft computing techniques play a vital role in developing software engineering applications. In this paper, we have identified five parameters: Reliability, Efficiency, Usability, Maintainability, and Portability for accessing the level of quality of software. A fuzzy logic-based intelligent identification methodology has been proposed to access the quality of particular software-based on five parameters. The proposed identification scheme takes these five parameters as input and predicts the quality of the software using the fuzzy rule base which is generated using various studies. As this scheme takes five inputs and each input is divided into three regions i.e. ‘Low’, ‘Medium’, ‘High’ and thus a total of 35 i.e. 243 rules has been generated to analyze the software quality. Furthermore, Mamdani fuzzy model has been used as the reference model. To show the effectiveness of the proposed methodology, simulation results have been performed in MATLAB, which shows that the software's quality closely matches with the actual one.

There is an increasing trend in cloud adoption of enterprise applications in, for example, manufacturing, healthcare, and finance. Such applications are routinely subject to targeted cyberattacks, which result in significant loss of sensitive data (e.g., due to data exfiltration in advanced persistent threats) or valuable utilities (e.g., due to resource the exfiltration of power in cryptojacking). There is a critical need to train highly skilled cybersecurity professionals, who are capable of defending against such targeted attacks. In this article, we present the design, development, and evaluation of the Mizzou Cyber Range, an online platform to learn basic/advanced cyber defense concepts and perform training exercises to engender the next-generation cybersecurity workforce. Mizzou Cyber Range features flexibility, scalability, portability, and extendability in delivering cyberattack/defense learning modules to students. We detail our “research-inspired learning” and “learn–apply–create” three-phase pedagogy methodologies in the development of four learning modules that include laboratory exercises and self-study activities using realistic cloud-based application testbeds. The learning modules allow students to gain skills in using latest technologies (e.g., elastic capacity provisioning, software-defined everything infrastructure) to implement sophisticated “attack defense by pretense” techniques. Students can also use the learning modules to understand the attacker–defender game in order to create disincentives (i.e., pretense initiation) that make the attacker's tasks more difficult, costly, time consuming, and uncertain. Lastly, we show the benefits of our Mizzou Cyber Range through the evaluation of student learning using auto-grading, rank assessments with peer standing, and monitoring of students’ performance via feedback from prelab evaluation surveys and postlab technical assessments.

Recent developments in the portability, flexibility, and affordability of augmented reality technology allow it to address some limitations of current medical training. This pilot study aims to determine whether augmented reality can enhance the detection and identification of subtle cues used to make accurate medical diagnoses. We randomly assigned five participants to a control group, where they received diagnostic training by reading a standard textbook, and an experimental group, where they received diagnostic training by a combination of reading a standard textbook and augmented reality-based training. All participants were tested with a tension pneumothorax scenario, where they listed symptoms and made a diagnosis. We recorded the time until a final diagnosis was made and categorized the diagnosis and symptoms as accurate or inaccurate. A two-tailed t-test was used to assess differences between groups. The experimental group correctly identified more symptoms than the control group (average 12 vs. 5). These results indicate the potential of AR-based training to enhance detection and identification of cues in medical diagnostics.

Circulating cell-free miRNAs (ccf-miRs) have gained significant interest as biomarkers for lung cancer (LC) diagnosis. However, the clinical application of ccf-miRs is mainly limited by time, cost, and expertise-related problems of existing detection strategies. Recently, the development of different point-of-care (POC) approaches offers useful on-site platforms, because these technologies have important features such as portability, rapid turnaround time, minimal sample requirement, and cost-effectiveness. In this review, we discuss different POC approaches for detecting ccf-miRs and highlight the utility of incorporating nanomaterials for enhanced biorecognition and signal transduction, further improving their diagnostic applicability in LC settings.

Radar system simulation usually needs to consider not only the modeling work of its static system structure but also the representation of dynamic behaviors. Traditionally, the standard Simulation Model Portability (SMP) published by European Space Agency is very suitable to build the structure of entities and their internal and external static relationships as well, but it shows pale to specify complex dynamic behaviors, such as that of radar system. To describe the behavioral aspect, we applied Synchronous data flow (SDF) and combined SMP with it. If so, one can benefit from a loose coupling way to design radar system in two different kinds of formalisms, and can simultaneously support structural modeling and behavioral modeling. For this purpose, the goal of this paper is to design a mechanism of how to combine and use these two formalisms together to form a novel simulation language. As a proof of concept, we built a set of functional models of a general radar system by using this new language and simulation results show its proper availability.

Recurrent Neural Networks are ubiquitous and pervasive in many artificial intelligence applications such as speech recognition, predictive healthcare, creative art, and so on. Although they provide accurate superior solutions, they pose a massive challenge “training havoc.” Current expansion of IoT demands intelligent models to be deployed at the edge. This is precisely to handle increasing model sizes and complex network architectures. Design efforts to meet these for greater performance have had inverse effects on portability on edge devices with real-time constraints of memory, latency, and energy. This article provides a detailed insight into various compression techniques widely disseminated in the deep learning regime. They have become key in mapping powerful RNNs onto resource-constrained devices. While compression of RNNs is the main focus of the survey, it also highlights challenges encountered while training. The training procedure directly influences model performance and compression alongside. Recent advancements to overcome the training challenges with their strengths and drawbacks are discussed. In short, the survey covers the three-step process, namely, architecture selection, efficient training process, and suitable compression technique applicable to a resource-constrained environment. It is thus one of the comprehensive survey guides a developer can adapt for a time-series problem context and an RNN solution for the edge.

Purpose This study aims to provide a system capable of static searching on a large number of unstructured texts directly on the Web domain while keeping costs to a minimum. The proposed framework is applied to the unstructured texts of Migne’s Patrologia Graeca (PG) collection, setting PG as an implementation example of the method. Design/methodology/approach The unstructured texts of PG have automatically transformed to a read-only not only Structured Query Language (NoSQL) database with a structure identical to that of a representational state transfer access point interface. The transformation makes it possible to execute queries and retrieve ranked results based on a specialized application of the extended Boolean model. Findings Using a specifically built Web-browser-based search tool, the user can quickly locate ranked relevant fragments of texts with the ability to navigate back and forth. The user can search using the initial part of words and by ignoring the diacritics of the Greek language. The performance of the search system is comparatively examined when different versions of hypertext transfer protocol (Http) are used for various network latencies and different modes of network connections. Queries using Http-2 have by far the best performance, compared to any of Http-1.1 modes. Originality/value The system is not limited to the case study of PG and has a generic application in the field of humanities. The expandability of the system in terms of semantic enrichment is feasible by taking into account synonyms and topics if they are available. The system’s main advantage is that it is totally static which implies important features such as simplicity, efficiency, fast response, portability, security and scalability.

A high-resolution (1/20∘) global ocean general circulation model with graphics processing unit (GPU) code implementations is developed based on the LASG/IAP Climate System Ocean Model version 3 (LICOM3) under a heterogeneous-compute interface for portability (HIP) framework. The dynamic core and physics package of LICOM3 are both ported to the GPU, and three-dimensional parallelization (also partitioned in the vertical direction) is applied. The HIP version of LICOM3 (LICOM3-HIP) is 42 times faster than the same number of CPU cores when 384 AMD GPUs and CPU cores are used. LICOM3-HIP has excellent scalability; it can still obtain a speedup of more than 4 on 9216 GPUs compared to 384 GPUs. In this phase, we successfully performed a test of 1/20∘ LICOM3-HIP using 6550 nodes and 26 200 GPUs, and on a large scale, the model's speed was increased to approximately 2.72 simulated years per day (SYPD). By putting almost all the computation processes inside GPUs, the time cost of data transfer between CPUs and GPUs was reduced, resulting in high performance. Simultaneously, a 14-year spin-up integration following phase 2 of the Ocean Model Intercomparison Project (OMIP-2) protocol of surface forcing was performed, and preliminary results were evaluated. We found that the model results had little difference from the CPU version. Further comparison with observations and lower-resolution LICOM3 results suggests that the 1/20∘ LICOM3-HIP can reproduce the observations and produce many smaller-scale activities, such as submesoscale eddies and frontal-scale structures.

Owing to the high toxicity and large-scale use of pesticides, it is imperative to develop selective, sensitive, portable, and convenient sensors for rapid monitoring of pesticide. Therefore, the electrochemical detection platform offers a promising analytical approach since it is easy to operate, economical, efficient, and user-friendly. Meanwhile, with advances in functional nanomaterials and aptamer selection technologies, numerous sensitivity-enhancement techniques alongside a widespread range of smart nanomaterials have been merged to construct novel aptamer probes to use in the biosensing field. Hence, this study intends to highlight recent development and promising applications on the functional nanomaterials with aptamers for pesticides detection based on electrochemical strategies. We also reviewed the current novel aptamer-functionalized microdevices for the portability of pesticides sensors. Furthermore, the major challenges and future prospects in this field are also discussed to provide ideas for further research.

Fiber-based wearable electronics are highly desirable for wearable devices that are expected to be lightweight, easily prepared, durable, flexible, washable, and conformable. However, developing fiber-based fabric electronics to simulate human perceptual systems or even transcend the sensory capabilities of natural creatures is still a pivotal challenge. Herein, we present a Kevlar/MXene (KM) intelligent wearable fabric with multiple sensory capabilities using an ingenious strategy of continuous wet-spinning. The KM fibers can be washed, knitted, sewed, and fabricated into smart KM fabric sensory systems. An intelligent KM sensory mask is prepared to monitor human breathing in time to detect respiratory problems with high accuracy and portability. It provides an important reference for judging diseases and achieving remote diagnosis. Additionally, a smart temperature-responsive sensory glove is developed to help people make proper behavioral prejudgments and prevent potential injuries by sensing surrounding hazards beforehand. Moreover, this sensory system allows soft robotics to make a rough identification about the basic properties of unknown liquid molecules. Overall, by the virtue of the ultrafast responsiveness (90 ms), resilience (110 ms), and ultrasensitive capability in pressure responding, this KM sensory system offers a gentle approach for wireless detection in information encryption, transmission, and preservation by touching the sensory system with variable pressing time on the basis of the International Morse code principles, establishing a competitive and promising candidate for next generation wearable flexible fabric electronics.

The Controller model is a heterogeneous parallel programming model implemented as a library. It transparently manages the coordination, communication and kernel launching details on different heterogeneous computing devices. It exploits native or vendor specific programming models and compilers, such as OpenMP, CUDA or OpenCL, thus enabling the potential performance obtained by using them. This work discusses the integration of FPGAs in the Controller model, using high-level synthesis tools and OpenCL. A new Controller backend for FPGAs is presented based on a previous OpenCL backend for GPUs. We discuss new configuration parameters for FPGA kernels and key ideas to adapt the original OpenCL backend while maintaining the portability of the original model. We present an experimental study to compare performance and development effort metrics obtained with the Controller model, Intel oneAPI and reference codes directly programmed with OpenCL. The results show that using the Controller library has advantages and drawbacks compared with Intel oneAPI, while compared with OpenCL it highly reduces the programming effort with negligible performance overhead.

The proliferation process's efficiency is related to the number of cells grown in culture and the maximum efficiency obtained at the stationary phase. Since the culture's growth speed is different for various cells and even for subgroups of the same cells, it is essential to monitor the process properly to obtain maximum efficiency. In this work, ultrasonic velocity measurement was performed noninvasively for wireless real-time monitoring of the suspension cell culture using a single integrated device to get maximum efficiency from the process by determining the phases. Using the advantage of the developed device's portability and wireless connectivity, the cells are monitored in the incubator without interfering with the actual process. Therefore, a real-time highly sampled growth curve is obtained, which was not possible to obtain with the currently used methods or the offline methods that are based on taking samples from the culture invasively. Filtering and curve fitting methods are also applied to the data to obtain a clean growth curve. The method developed as a result of this study ensures that the suspension cell culture was monitored most conveniently in the actual growth medium in real-time and noninvasively.

In response to the demand for high-quality electronic information talents in the mobile network industry, in the situation of artificial intelligence (AI) to promote technological innovation, this paper conducts an overall design in the target system, curriculum system, teaching platform, teaching mode and teaching case. The practice education mode of teaching practice, engineering practice, innovation practice, and enterprise practice, which aims to improve students’ ability to solve complex engineering problems, is constructed. The mode breaks geographical boundaries between schools and enterprises to build the through-through experimental teaching course system based on artificial intelligence and edge computing and design a medical image intelligent analysis system project case based on Mobile Edge Computing (MEC), which improves students’ practical ability, engineering design ability, scientific research innovation ability, enterprise practice ability and mobile network application capabilities. At the same time, the hardware portability of the edge computing platform provides good conditions for long-distance education and the mobile network. This method is a beneficial attempt to cultivate high-level, diversified, and creative electronic information talents.