bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatiotemporal Analysis of Urban Heat Islands in Relation to Urban Development, in the Vicinity of the Atacama Desert
Authors: Espinoza-Molina Jorge , Acosta-Caipa Karina , Chambe-Vega Esther , Huayna Germán , Pino-Vargas Edwin , Abad Jorge
Published: June 2022

Near the Atacama Desert, Tacna city in Peru is among the largest arid cities with constant urban development, thus understanding of the urban surface thermal pattern is needed. We propose a comprehensive study of the urban heat island phenomenon, with the objective of (1) determining the spatial and temporal variations of the urban heat islands (UHIs), in the period 1985 to 2020; (2) analyzing the relationship between the UHI and influencing factors such as vegetation, urban area, and population, using indices calculated with satellite images. The Google Earth Engine repository was used to evaluate the corrected images from the years 1985 to 2020. The coincidence between the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) was good, negative between NDVI and the land surface temperature (LST), attributable to dense vegetation, and negative and very high (−0.81) between NDBI and NDVI, as massive urbanization leads to the reduction in the vegetal surface. The NDBI has a high impact on the LST; a coefficient of connections is recorded as 0.46. Tacna is a very arid region, and an increase in the time of the LST occurred with the increase in industrialization and urbanization. The land use/cover change (LUCC) evidences change in the climate in the city of Tacna; temperatures of 24.2 °C to 44.2 °C are observed in the built-up areas. In vegetated areas, the temperature remains below 24 °C, which is associated with a high rate of potential evapotranspiration. Thus, this study shows that variations in urban form and growth have produced the development of intraurban surface thermal patterns.
A Light Signaling Approach to Node Grouping for Massive MIMO IoT Networks
Authors: Fitzgerald Emma , Pióro Michał , Tataria Harsh , Callebaut Gilles , Gunnarsson Sara , Van der Perre Liesbet
Published: June 2022

Massive MIMO is one of the leading technologies for connecting very large numbers of energy-constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes into groups that can communicate simultaneously such that the mutual interference is minimized. Here we propose node partitioning strategies that do not require full channel state information, but rather are based on nodes’ respective directional channel properties. In our considered scenarios, these typically have a time constant that is far larger than the coherence time of the channel. We developed both an optimal and an approximation algorithm to partition users based on directional channel properties, and evaluated them numerically. Our results show that both algorithms, despite using only these directional channel properties, achieve similar performance in terms of the minimum signal-to-interference-plus-noise ratio for any user, compared with a reference method using full channel knowledge. In particular, we demonstrate that grouping nodes with related directional properties is to be avoided. We hence realize a simple partitioning method, requiring minimal information to be collected from the nodes, and in which this information typically remains stable over the long term, thus promoting the system’s autonomy and energy efficiency.
Correction: Majeed et al. Novel Topical Application of a Postbiotic, LactoSporin®, in Mild to Moderate Acne: A Randomized, Comparative Clinical Study to Evaluate Its Efficacy, Tolerability and Safety. Cosmetics 2020, 7, 70
Authors: Majeed Muhammed , Majeed Shaheen , Nagabhushanam Kalyanam , Mundkur Lakshmi , Rajalakshmi H. , Shah Kalpesh , Beede Kirankumar
Published: June 2022

In the original publication [...]
Scalable and Demography-Agnostic Confinement Strategies for COVID-19 Pandemic with Game Theory and Graph Algorithms
Authors: Kasarapu Sreenitha , Hassan Rakibul , Homayoun Houman , Pudukotai Dinakarrao Sai Manoj
Published: June 2022

In the past, epidemics such as AIDS, measles, SARS, H1N1 influenza, and tuberculosis caused the death of millions of people around the world. In response, intensive research is evolving to design efficient drugs and vaccines. However, studies warn that new pandemics such as Coronavirus (COVID-19), variants, and even deadly pandemics can emerge in the future. The existing epidemic confinement approaches rely on a large amount of available data to determine policies. Such dependencies could cause an irreversible effect before proper strategies are developed. Furthermore, the existing approaches follow a one-size-fits-all control technique, which might not be effective. To overcome this, in this work, we develop a game-theory-inspired approach that considers societal and economic impacts and formulates epidemic control as a non-zero-sum game. Further, the proposed approach considers the demographic information that provides a tailored solution to each demography. We explore different strategies, including masking, social distancing, contact tracing, quarantining, partial-, and full-lockdowns and their combinations, and present demography-aware optimal solutions to confine a pandemic with minimal history information and optimal impact on the economy. To facilitate scalability, we propose a novel graph learning approach, which learns from the previously obtained COVID-19 game outputs and mobility rates of one state (region) depending on the other to produce an optimal solution. Our optimal solution is strategized to restrict the mobility between states based on the impact they are causing on COVID-19 spread. We aim to control the COVID-19 spread by more than 50% and model a dynamic solution that can be applied to different strains of COVID-19. Real-world demographic conditions specific to each state are created, and an optimal strategic solution is obtained to reduce the infection rate in each state by more than 50%.
ChaCha20–Poly1305 Authenticated Encryption with Additional Data for Transport Layer Security 1.3
Authors: Serrano Ronaldo , Duran Ckristian , Sarmiento Marco , Pham Cong-Kha , Hoang Trong-Thuc
Published: June 2022

Transport Layer Security (TLS) provides a secure channel for end-to-end communications in computer networks. The ChaCha20–Poly1305 cipher suite is introduced in TLS 1.3, mitigating the sidechannel attacks in the cipher suites based on the Advanced Encryption Standard (AES). However, the few implementations cannot provide sufficient speed compared to other encryption standards with Authenticated Encryption with Associated Data (AEAD). This paper shows ChaCha20 and Poly1305 primitives. In addition, a compatible ChaCha20–Poly1305 AEAD with TLS 1.3 is implemented with a fault detector to reduce the problems in fragmented blocks. The AEAD implementation reaches 1.4-cycles-per-byte in a standalone core. Additionally, the system implementation presents 11.56-cycles-per-byte in an RISC-V environment using a TileLink bus. The implementation in Xilinx Virtex-7 XC7VX485T Field-Programmable Gate-Array (FPGA) denotes 10,808 Look-Up Tables (LUT) and 3731 Flip-Flops (FFs), represented in 23% and 48% of ChaCha20 and Poly1305, respectively. Finally, the hardware implementation of ChaCha20–Poly1305 AEAD demonstrates the viability of using a different option from the conventional cipher suite based on AES for TLS 1.3.
Achievement of Unidirectional Aluminum Tin Oxide/UV-Curable Polymer Hybrid Film via UV Nanoimprinting Lithography for Uniform Liquid Crystal Alignment
Authors: Lee Dong-Wook , Kim Dong-Hyun , Won Jonghoon , Oh Jin-Young , Seo Dae-Shik
Published: June 2022

A uniform unidirectional nanostructure composed of aluminum tin oxide and ultraviolet (UV)-curable polymer is introduced herein. The nanostructure was produced by UV-nanoimprint lithography (UV-NIL), and the fabricated hybrid film was used as a uniform liquid crystal (LC) alignment layer. Atomic force microscopy and line profile analysis were performed to confirm a well-ordered nanostructure with 760 nm periodicity and 30 nm height. X-ray photoelectron spectroscopy analysis was also conducted to examine the chemical modifications to the hybrid film surface during UV exposure. Optical transmittance investigation of the nanopatterned hybrid film revealed its compatibility for LC device application. Stable, uniform, and homogeneous LC alignment on the hybrid film was confirmed by polarized optical microscopy observance and analysis of LC pretilt angle. The unidirectional structure on the film surface enabled uniform LC orientation along with surface anisotropy property. Hence, we expect that the proposed UV-NIL process can be applied to fabricate high-resolution unidirectional nanostructures with various inorganic/organic hybrid materials and that these nanostructures have high potential for next-generation LC systems.
Review of Additive Manufacturing Techniques for Large-Scale Metal Functionally Graded Materials
Authors: Zhang Ruiying , Jiang Fan , Xue Long , Yu Junyu
Published: June 2022

Functionally graded materials (FGMs), which constitute a new type of composite material, have received considerable attention in industry because of the spatial gradient of their composition and the microstructure-induced gradient in their material performance, which make them better suited for high-performance multifunctional applications. Additive manufacturing (AM) has become one of the most promising techniques for the manufacture of materials and structures because of its high flexibility. The combination of advanced materials (FGMs) and advanced manufacturing methods (AM) is expected to facilitate the further development of such engineering materials. In this paper, the definition, historical development and material gradient types of FGMs are introduced. The classification, process principle and typical research results of the AM of metal FGMs are summarized and discussed. In particular, the research status of wire and arc additive manufacture (WAAM), which is more suitable for the preparation of large-scale metal FGMs, is reviewed in detail according to the types of FGMs, and a double-wire bypass plasma arc additive manufacturing technique, which is suitable for inducing a gradient along the direction of single-pass cladding, is proposed. On the basis of this summary of the important achievements made to date, future research is proposed.
Factors Influencing Recognition Capability of Inverse Opal Structured Photonic Crystal Sensors
Authors: Yang Chaokun , Pan Mengyao , Zhao Xin
Published: June 2022

Nowadays, many kinds of colloidal photonic crystal (PC) sensors with inverse opal (IO) structures have been developed. However, there are few systematic studies on the factors influencing their recognition capability and responsiveness capability. In this paper, the relationships between recognition capability of IO structured PC sensors and all the parameters in Bragg–Snell’s law have been explored. In addition, research on the recognition ability of PC sensors typically focuses only on the refractive index difference between the identified substances. Herein, we define two concepts, namely the absolute refractive index difference and the relative refractive index difference, and prove that the recognition ability not only relies on the absolute refractive index between the identified substances, but also on the relative refractive index. Bragg–Snell’s law analysis confirms that the responsiveness capability is directly proportional to the void size of the IO structure, which is also confirmed by the finite difference time domain (FDTD) method. It is believed that these systematic studies have important guiding significance for creating advanced IO structured PC sensors.
A Narrative Review on the Collection and Use of Electronic Patient-Reported Outcomes in Cancer Survivorship Care with Emphasis on Symptom Monitoring
Authors: van den Hurk Corina J. G. , Mols Floortje , Eicher Manuela , Chan Raymond J. , Becker Annemarie , Geleijnse Gijs , Walraven Iris , Coolbrandt Annemarie , Lustberg Maryam , Velikova Galina , Charalambous Andreas , Koczwara Bogda , Howell Doris , Basch Ethan M. , van de Poll-Franse Lonneke V.
Published: June 2022

Electronic patient-reported outcome (ePRO) applications promise great added value for improving symptom management and health-related quality of life. The aim of this narrative review is to describe the collection and use of ePROs for cancer survivorship care, with an emphasis on ePRO-symptom monitoring. It offers many different perspectives from research settings, while current implementation in routine care is ongoing. ePRO collection optimizes survivorship care by providing insight into the patients’ well-being and prioritizing their unmet needs during the whole trajectory from diagnosis to end-of-life. ePRO-symptom monitoring can contribute to timely health risk detection and subsequently allow earlier intervention. Detection is optimized by automatically generated alerts that vary from simple to complex and multilayered. Using ePRO-symptoms during in-hospital consultation enhances the patients’ conversation with the health care provider before making informed decisions about treatments, other interventions, or self-management. ePRO(-symptoms) entail specific implementation issues and complementary ethics considerations. The latter is due to privacy concerns, digital divide, and scarcity of adequately representative data for particular groups of patients.
Pre-Transit Oriented Development Assessment Guidelines for Assessing Metro Station Dependent Component Area
Authors: Jamaleddin Mohuiddin , Ibrahim Hatem , Ferwati M. , Khamidi Mohd , Indraganti Madhavi
Published: June 2022

Transit-Oriented Development (TOD) assessment models are commonly used to assess existing and planned metro station catchment areas. Elements like diversity, density, design, destination, distance, and demand management are considered in the 6Ds TOD assessment model. Assessment of these elements in the designing stage depends on the TOD expert’s involvement to ensure the effectiveness of the model. The aim of this research is to develop novel pre-TOD assessment guidelines that consist of Spatial Analysis, Virtual Function of Transit Station (VFTS), and Estimated Spatial Analysis, which can improve the reliability of the assessment conclusion according to the 16 planning elements. The research applies three methodologies: (1) analytical approach to evaluate the TOD concept and its assessment model; (2) development of pre-TOD assessment guidelines using the Delphi technique; and (3) conduct a case study of the Doha Exhibition and Convention Center metro station in Qatar to validate the developed model. The findings are a new concept, called the Dependent Component Area (DCA) of the metro station, and VFTS classifications of the metro station include an urban connector, suburban connector, destination, and destination connector. The newly developed pre-TOD assessment guidelines can enhance TOD assessment methods, especially in emerging cities. Moreover, these assessment guidelines will provide TOD and planning experts with more precise data and information related to the DCA.
Artificial Intelligence-Assisted Image Analysis of Acetaminophen-Induced Acute Hepatic Injury in Sprague-Dawley Rats
Authors: Baek Eun Bok , Hwang Ji-Hee , Park Heejin , Lee Byoung-Seok , Son Hwa-Young , Kim Yong-Bum , Jun Sang-Yeop , Her Jun , Lee Jaeku , Cho Jae-Woo
Published: June 2022

Although drug-induced liver injury (DILI) is a major target of the pharmaceutical industry, we currently lack an efficient model for evaluating liver toxicity in the early stage of its development. Recent progress in artificial intelligence-based deep learning technology promises to improve the accuracy and robustness of current toxicity prediction models. Mask region-based CNN (Mask R-CNN) is a detection-based segmentation model that has been used for developing algorithms. In the present study, we applied a Mask R-CNN algorithm to detect and predict acute hepatic injury lesions induced by acetaminophen (APAP) in Sprague-Dawley rats. To accomplish this, we trained, validated, and tested the model for various hepatic lesions, including necrosis, inflammation, infiltration, and portal triad. We confirmed the model performance at the whole-slide image (WSI) level. The training, validating, and testing processes, which were performed using tile images, yielded an overall model accuracy of 96.44%. For confirmation, we compared the model’s predictions for 25 WSIs at 20× magnification with annotated lesion areas determined by an accredited toxicologic pathologist. In individual WSIs, the expert-annotated lesion areas of necrosis, inflammation, and infiltration tended to be comparable with the values predicted by the algorithm. The overall predictions showed a high correlation with the annotated area. The R square values were 0.9953, 0.9610, and 0.9445 for necrosis, inflammation plus infiltration, and portal triad, respectively. The present study shows that the Mask R-CNN algorithm is a useful tool for detecting and predicting hepatic lesions in non-clinical studies. This new algorithm might be widely useful for predicting liver lesions in non-clinical and clinical settings.
Unsupervised Learning Based on Multiple Descriptors for WSIs Diagnosis
Authors: Sheikh Taimoor Shakeel , Kim Jee-Yeon , Shim Jaesool , Cho Migyung
Published: June 2022

An automatic pathological diagnosis is a challenging task because histopathological images with different cellular heterogeneity representations are sometimes limited. To overcome this, we investigated how the holistic and local appearance features with limited information can be fused to enhance the analysis performance. We propose an unsupervised deep learning model for whole-slide image diagnosis, which uses stacked autoencoders simultaneously feeding multiple-image descriptors such as the histogram of oriented gradients and local binary patterns along with the original image to fuse the heterogeneous features. The pre-trained latent vectors are extracted from each autoencoder, and these fused feature representations are utilized for classification. We observed that training with additional descriptors helps the model to overcome the limitations of multiple variants and the intricate cellular structure of histopathology data by various experiments. Our model outperforms existing state-of-the-art approaches by achieving the highest accuracies of 87.2 for ICIAR2018, 94.6 for Dartmouth, and other significant metrics for public benchmark datasets. Our model does not rely on a specific set of pre-trained features based on classifiers to achieve high performance. Unsupervised spaces are learned from the number of independent multiple descriptors and can be used with different variants of classifiers to classify cancer diseases from whole-slide images. Furthermore, we found that the proposed model classifies the types of breast and lung cancer similar to the viewpoint of pathologists by visualization. We also designed our whole-slide image processing toolbox to extract and process the patches from whole-slide images.
Triglyceride and Glucose Index Predicts Acute Coronary Syndrome in Patients with Antineutrophil Cytoplasmic Antibody-Associated Vasculitis
Authors: Park Pil Gyu , Pyo Jung Yoon , Ahn Sung Soo , Song Jason Jungsik , Park Yong-Beom , Huh Ji Hye , Lee Sang-Won
Published: June 2022

This study investigated whether the triglyceride (TG) glucose (TyG) index at diagnosis could predict acute coronary syndrome (ACS) in patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). The medical records of 152 AAV were reviewed. Clinical and laboratory data were collected. The TyG index was calculated by TyG index = Ln (fasting TG (mg/dL) × fasting glucose (mg/dL)/2). The cut-offs of Birmingham vasculitis activity score (BVAS) and the TyG were obtained by the receiver operator characteristic (ROC) curve and the highest tertile (9.011). The mean age was 57.2 years and 32.9% were male. AAV patients with a TyG index ≥ 9.011 exhibited a lower cumulative ACS-free survival rate than those with a TyG index < 9.011. However, a TyG index ≥ 9.011 was not independently associated with ACS in the multivariable Cox analysis. Meanwhile, there might be a close relationship for predicting ACS among the TyG index, metabolic syndrome (MetS), and BVAS. AAV patients with a TyG index ≥ 9.011 exhibited a higher risk for MetS than those with a TyG index < 9.011 (relative risk 2.833). AAV patients with BVAS ≥ 11.5 also exhibited a higher risk for ACS than those with BVAS < 11.5 (relative risk 10.225). Both AAV patients with MetS and those with BVAS ≥11.5 exhibited lower cumulative ACS-free survival rates than those without. The TyG index at AAV diagnosis could estimate the concurrent presence of MetS and predict the occurrence of ACS during follow-up along with high BVAS at diagnosis in patients with AAV.
Spiking Neural P Systems with Membrane Potentials, Inhibitory Rules, and Anti-Spikes
Authors: Liu Yuping , Zhao Yuzhen
Published: June 2022

Spiking neural P systems (SN P systems for short) realize the high abstraction and simulation of the working mechanism of the human brain, and adopts spikes for information encoding and processing, which are regarded as one of the third-generation neural network models. In the nervous system, the conduction of excitation depends on the presence of membrane potential (also known as the transmembrane potential difference), and the conduction of excitation on neurons is the conduction of action potentials. On the basis of the SN P systems with polarizations, in which the neuron-associated polarization is the trigger condition of the rule, the concept of neuronal membrane potential is introduced into systems. The obtained variant of the SN P system features charge accumulation and computation within neurons in quantity, as well as transmission between neurons. In addition, there are inhibitory synapses between neurons that inhibit excitatory transmission, and as such, synapses cause postsynaptic neurons to generate inhibitory postsynaptic potentials. Therefore, to make the model better fit the biological facts, inhibitory rules and anti-spikes are also adopted to obtain the spiking neural P systems with membrane potentials, inhibitory rules, and anti-spikes (referred to as the MPAIRSN P systems). The Turing universality of the MPAIRSN P systems as number generating and accepting devices is demonstrated. On the basis of the above working mechanism of the system, a small universal MPAIRSN P system with 95 neurons for computing functions is designed. The comparisons with other SN P models conclude that fewer neurons are required by the MPAIRSN P systems to realize universality.
Projected Climate and Hydroregime Variability Constrain Ephemeral Wetland-Dependent Amphibian Populations in Simulations of Southern Toads
Authors: Awkerman Jill A. , Greenberg Cathryn H.
Published: June 2022

Amphibian populations are threatened globally by stressors, including diminishing availability of suitable wetland breeding sites, altered hydroregimes driven by changing weather patterns, and exposure to contaminants. Ecological risk assessment should encompass spatial and temporal scales that capture influential ecological processes and demographic responses. Following the PopGUIDE framework of population model development for risk assessment, we used matrix population models, in conjunction with existing hydroregime predictions, under a climate change scenario to evaluate the effects of environmental stochasticity and aquatic pesticide exposure on amphibians that are dependent on ephemeral wetlands. Using southern toads (Anaxyrus terrestris) as an example, we simulated population dynamics with breeding success dependent on hydroregime suitability. Years were defined as optimal, marginal, or insufficient for successful toad recruitment, based on the duration of their potential breeding season and rate of larval development to metamorphosis. We simulated both probabilistic and chronologically specific population projections, including variable annual fecundity, based on hydroregime suitability and reduced larval survival from carbaryl exposure. In our simulations, populations were more negatively impacted by prolonged drought, and consequently multiple sequential years of reproductive failure, than by aquatic pesticide exposure. These results highlight the necessity of reliable climate projections to accurately represent the effects of altered hydroregimes on amphibian populations. Risk assessment approaches could be improved with flexible modifications that allow inclusion of various extrinsic stressors and identification of demographic and ecological vulnerabilities when precise data are lacking.
Understanding Urban Land Leasing System as a Strategic Value Capture Instrument to Enhance Urban Revenue in Ethiopia: A Case Study of Bahir Dar City
Authors: Yimam Seid Hussen , Lind Hans , Alemu Belachew Yirsaw
Published: June 2022

Since 1993, the Ethiopian government has been using the urban land leasing system to monetize the increase in land value created due to factors other than private investment. Thus, this paper aims to explore and understand whether Bahir Dar city is leveraging the urban land lease system as a strategic value capture instrument to enhance its local revenue or not. This study has used the qualitative research method and in-depth analysis. It has used a desk review of documents and key informant interviews of experts and brokers to collect the data required to realize the objective of this study. The study has found that the majority of the urban land is held in a permit system, with landholders paying a small amount of land rent per annum. In addition, the study has uncovered that the city has transferred most of its land through administrative allotment at low and outdated benchmark prices, which has dwindled its lease revenue. Moreover, it has identified that there is weak enforcement of the lease payment collection, and it has adversely affected the city’s urban revenue generation potential. As a result, the city is not leveraging the public land leasing system as a strategic value capture instrument. Based on these findings, this paper has advised the government to implement a modern property tax system to capture value increment on permit holding land. In addition, the study proposes to carry out an empirical investigation and identify the factors that significantly affect the benchmark prices and update the benchmark price regularly given those factors. Moreover, the study has suggested a proper enforcement of the lease payment collection in the city.
Electrodeposition and Characterization of Conducting Polymer Films Obtained from Carbazole and 2-(9H-carbazol-9-yl)acetic Acid
Authors: Lakard Sophie , Contal Emmanuel , Mougin Karine , Lakard Boris
Published: June 2022

Electrochemical oxidation of electrolyte solutions containing carbazole (Cz) and 2-(9H-carbazol-9-yl)acetic acid (CzA) monomers was performed in acetonitrile solutions. Different Cz and CzA feed ratios were used to electrodeposit solid polymer films of various compositions, and to study the influence of the monomer ratio on the physicochemical properties (electroactivity, topography, adhesion, stiffness, wettability) of the polymer films. Thus, electrochemical oxidation led to the deposition of a solid film of micrometric thickness, but only for the solutions containing at least 30% of Cz. The proportion of Cz and CzA in the electrodeposited polymer films has little impact on the adhesion strength values measured by AFM. On the contrary, this proportion significantly modifies the stiffness of the films. Indeed, the stiffness of the polymer films varies from 9 to 24 GPa depending on the monomer ratio, which is much lower than the value obtained for unmodified polycarbazole (64 GPa). This leads to the absence of cracks in the films, which all have a fairly homogeneous globular structure. Moreover, among the different polymer films obtained, those prepared from 70:30 and 50:50 ratios in Cz:CzA monomer solutions seem to be the most interesting because these green films are conductive, thick, low in stiffness, do not show cracks and are resistant to prolonged immersion in water.
Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach
Authors: Bangyal Waqas Haider , Rehman Najeeb Ur , Nawaz Asma , Nisar Kashif , Ibrahim Ag. Asri Ag. , Shakir Rabia , Rawat Danda B.
Published: June 2022

Facts can be exchanged in multiple fields with the help of disease-specific ontologies. A range of diverse values can be produced by mining ontological approaches for demonstrating disease mechanisms. Alzheimer’s disease (AD) is an incurable neurological brain illness. An early diagnosis of AD can be helpful for better treatment and the prevention of brain tissue destruction. Researchers have used machine learning techniques to predict the early detection of AD. However, Alzheimer’s disorders are still underexplored in the knowledge domain. In the biomedical field, the illustration of terminologies and notions is essential. Multiple methods are adopted to represent these notions, but ontologies are the most frequent and accurate. Ontology construction is a complex and time-consuming process. The designed ontology relies on Disease Ontology (DO), which is considered the benchmark in medical practice. Ontology reasoning mechanisms can be adopted for AD identification. In this paper, a deep convolutional neural network-based approach is proposed to diagnose Alzheimer’s disease, using an AD dataset acquired from Kaggle. Machine learning-based approaches (logistic regression, gradient boosting, XGB, SGD, MLP, SVM, KNN, random forest) are also used for a fair comparison. The simulation results are generated using three strategies (default parameters, 10-cross validation, and grid search), and MLP provides superior results on a default parameter strategy with an accuracy of 92.12%. Furthermore, the deep learning-based approach convolutional neural network (CNN) achieved an accuracy of 94.61%. The experimental results indicate that the construction of ontology, with the help of deep learning knowledge, can produce better results where the robustness and scalability can be enhanced. In comparisons to other methods, the CNN results are excellent and encouraging.
Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications
Authors: Zaman Umar , Imran  , Mehmood Faisal , Iqbal Naeem , Kim Jungsuk , Ibrahim Muhammad
Published: June 2022

With the growth of computing and communication technologies, the information processing paradigm of the healthcare environment is evolving. The patient information is stored electronically, making it convenient to store and retrieve patient information remotely when needed. However, evolving the healthcare systems into smart healthcare environments comes with challenges and additional pressures. Internet of Things (IoT) connects things, such as computing devices, through wired or wireless mediums to form a network. There are numerous security vulnerabilities and risks in the existing IoT-based systems due to the lack of intrinsic security technologies. For example, patient medical data, data privacy, data sharing, and convenience are considered imperative for collecting and storing electronic health records (EHR). However, the traditional IoT-based EHR systems cannot deal with these paradigms because of inconsistent security policies and data access structures. Blockchain (BC) technology is a decentralized and distributed ledger that comes in handy in storing patient data and encountering data integrity and confidentiality challenges. Therefore, it is a viable solution for addressing existing IoT data security and privacy challenges. BC paves a tremendous path to revolutionize traditional IoT systems by enhancing data security, privacy, and transparency. The scientific community has shown a variety of healthcare applications based on artificial intelligence (AI) that improve health diagnosis and monitoring practices. Moreover, technology companies and startups are revolutionizing healthcare with AI and related technologies. This study illustrates the implication of integrated technologies based on BC, IoT, and AI to meet growing healthcare challenges. This research study examines the integration of BC technology with IoT and analyzes the advancements of these innovative paradigms in the healthcare sector. In addition, our research study presents a detailed survey on enabling technologies for the futuristic, intelligent, and secure internet of health things (IoHT). Furthermore, this study comprehensively studies the peculiarities of the IoHT environment and the security, performance, and progression of the enabling technologies. First, the research gaps are identified by mapping security and performance benefits inferred by the BC technologies. Secondly, practical issues related to the integration process of BC and IoT devices are discussed. Third, the healthcare applications integrating IoT, BC, and ML in healthcare environments are discussed. Finally, the research gaps, future directions, and limitations of the enabling technologies are discussed.
A Decision-Support Informatics Platform for Minimally Invasive Aortic Valve Replacement
Authors: Capellini Katia , Positano Vincenzo , Murzi Michele , Farneti Pier Andrea , Concistrè Giovanni , Landini Luigi , Celi Simona
Published: June 2022

Minimally invasive aortic valve replacement is performed by mini-sternotomy (MS) or less invasive right anterior mini-thoracotomy (RT). The possibility of adopting RT is assessed by anatomical criteria derived from manual 2D image analysis. We developed a semi-automatic tool (RT-PLAN) to assess the criteria of RT, extract other parameters of surgical interest and generate a view of the anatomical region in a 3D space. Twenty-five 3D CT images from a dataset were retrospectively evaluated. The methodology starts with segmentation to reconstruct 3D surface models of the aorta and anterior rib cage. Secondly, the RT criteria and geometric information from these models are automatically and quantitatively evaluated. A comparison is made between the values of the parameters measured by the standard manual 2D procedure and our tool. The RT-PLAN procedure was feasible in all cases. Strong agreement was found between RT-PLAN and the standard manual 2D procedure. There was no difference between the RT-PLAN and the standard procedure when selecting patients for the RT technique. The tool developed is able to effectively perform the assessment of the RT criteria, with the addition of a realistic visualisation of the surgical field through virtual reality technology.
Reivew of Light Field Image Super-Resolution
Authors: Yu Li , Ma Yunpeng , Hong Song , Chen Ke
Published: June 2022

Currently, light fields play important roles in industry, including in 3D mapping, virtual reality and other fields. However, as a kind of high-latitude data, light field images are difficult to acquire and store. Thus, the study of light field super-resolution is of great importance. Compared with traditional 2D planar images, 4D light field images contain information from different angles in the scene, and thus the super-resolution of light field images needs to be performed not only in the spatial domain but also in the angular domain. In the early days of light field super-resolution research, many solutions for 2D image super-resolution, such as Gaussian models and sparse representations, were also used in light field super-resolution. With the development of deep learning, light field image super-resolution solutions based on deep-learning techniques are becoming increasingly common and are gradually replacing traditional methods. In this paper, the current research on super-resolution light field images, including traditional methods and deep-learning-based methods, are outlined and discussed separately. This paper also lists publicly available datasets and compares the performance of various methods on these datasets as well as analyses the importance of light field super-resolution research and its future development.
Early Fault Diagnosis Strategy for WT Main Bearings Based on SCADA Data and One-Class SVM
Authors: Tutivén Christian , Vidal Yolanda , Insuasty Andres , Campoverde-Vilela Lorena , Achicanoy Wilson
Published: June 2022

To reduce the levelized cost of wind energy, through the reduction in operation and maintenance costs, it is imperative that the wind turbine downtime is reduced through maintenance strategies based on condition monitoring. The standard approach toward this challenge is based on vibration monitoring, which requires the installation of specific tailored sensors that incur associated added costs. On the other hand, the life expectancy of wind parks built during the 1990s wind power boom is dwindling, and data-driven maintenance strategies issued from already accessible supervisory control and data acquisition (SCADA) data is an auspicious competitive solution because no additional sensors are required. Note that it is a major issue to provide fault diagnosis approaches built only on SCADA data, as these data were not established with the objective of being used for condition monitoring but rather for control capacities. The present study posits an early fault diagnosis strategy based exclusively on SCADA data and supports it with results on a real wind park with 18 wind turbines. The contributed methodology is an anomaly detection model based on a one-class support vector machine classifier; that is, it is a semi-supervised approach that trains a decision function that categorizes fresh data as similar or dissimilar to the training set. Therefore, only healthy (normal operation) data is required to train the model, which greatly expands the possibility of employing this methodology (because there is no need for faulty data from the past, and only normal operation SCADA data is needed). The results obtained from the real wind park show that this is a promising strategy.
On the Use of the Disability-Adjusted Life Year (DALY) Estimator as a Metric to Optimally Manage ICE Emissions
Authors: Rossetti Antonio , Andretta Nicola , Macor Alarico
Published: June 2022

We propose a new management strategy for engines equipped with automatic transmissions based on the damage to human health caused by emissions. The damage to human health is quantified by the years of life lost in a population due to disability or early death caused by exposure to pollutants. Various engine emissions share a common factor: damage to human health. Our strategy aims to keep engines running along the line of minimum damage instead of focusing on minimal fuel consumption. We applied the minimum damage strategy to the powertrain of a light vehicle to evaluate its effectiveness. In this work, we discuss this strategy’s effects on continuous variable transmission and seven gears automatic transmission and compare the classic minimum fuel consumption strategy to the minimum damage strategy. The latter results in a 50% reduction in damage compared with the minimum consumption strategy at the expense of an 8% increase in fuel consumption.
Asset Profitability in the Electricity Sector: An Iterative Approach in a Linear Optimization Model
Authors: Gillich Annika , Hufendiek Kai
Published: June 2022

In a competitive electricity market, generation capacities can exactly cover their full costs. However, the real market deviates from this ideal in some aspects. One is the concern of non-existent or insufficient scarcity prices. We present an iterative method in a linear optimization model to investigate the profitability of assets in the absence of scarcity prices and how the system changes when this risk is incorporated into investors’ expectations. Therefore, we use a two-step optimization of capacity planning and unit commitment. Iteratively, mark-ups at the height of uncovered costs are added to investment costs. This typically leads to a system with better investment profitability while keeping the system cost increase low. The methodology is applied to a simplified brownfield generation system, targeting CO2-free power generation within 25 years. In a model with annual foresight of actors, iterations result in a generation system with significantly lower (or even no) uncovered costs for new investments within ten or fewer iterations. Our example case with full foresight shows that early-added gas (combined cycle) and wind onshore capacities are able to recover their full costs over a lifetime, even without scarcity prices. However, the contribution margin gap remains high, especially for storage and biomass.
Controlling the Hydro-Swelling of Smectite Clay Minerals by Fe(III) Reducing Bacteria for Enhanced Oil Recovery from Low-Permeability Reservoirs
Authors: Cui Kai , Wang Chengjun , Li Li , Zou Jungang , Huang Weihong , Zhang Zhongzhi , Wang Heming , Guo Kun
Published: June 2022

The hydro-swelling of smectite clay minerals in low-permeability reservoirs further decreases the reservoir permeability and results in low oil recovery. Currently, the traditional chemical anti-swelling agents are widely used, but most of them are only effective in the short term and are not environmentally friendly. Here, we report the use of Fe(III) reducing microorganisms (FeRM) as a novel green anti-swelling agent to enhance oil recovery from low-permeability reservoirs. The results showed that FeRM (Proteus hauserifective) inhibited/reduced the hydro-swelling of smectite clay minerals through a three-step biochemical mineralization reaction process. The structural Fe(III) reduction in minerals by FeRM can be an important driving force for illitization. The maximum inhibition efficiency (36.6%) and shrinkage efficiency (69.3%) were achieved at 35 °C and 0.1 Mpa. Furthermore, core displacement tests showed that FeRM reduced the waterflooding injection pressure by 61.1%, increased the core permeability by 49.6%, and increased the oil recovery by 8.1%. Finally, the mechanism of FeRM-enhanced oil recovery was revealed. This study demonstrates that using FeRM to inhibit/reduce the hydro-swelling of clay minerals holds great potential to enhance the oil recovery from low-permeability reservoirs.
Study of Tower Clearance Safety Protection during Extreme Gust Based on Wind Turbine Monitoring Data
Authors: Wang Yazhou , Cai Xin , Lin Shifa , Xu Bofeng , Zhang Yuan , Bian Saixian
Published: June 2022

Large-scale wind turbines often face the problem of tower clearance safety under extreme gust conditions. Since gust intensity is positively correlated with the change rate of the generator’s speed, a gust identification method is proposed based on wind turbine monitoring data. Furthermore, a novel tower clearance safety protection strategy is proposed, which superimposes some additional speed requirements on the basis of normal pitch rate when identifying extreme gust so as to alleviate the dynamic response of the wind turbine. Simulations and comparison of a 5 MW wind turbine, before and after applying the new strategy, showed that the new strategy can induce an increase in pitch angle for the wind turbine and, simultaneously, avoids the emergency stop caused by the generator’s overspeed. Meanwhile, when the new strategy is adopted, the blade tip’s deformation and the load on the top of the tower are reduced by 19.9% and 52.2%, respectively. Therefore, the proposed strategy can not only protect the safety of the wind turbine but it also reduces costs.
Solid Digestate—Mathematical Modeling of Combustion Process
Authors: Dziedzic Krzysztof , Łapczyńska-Kordon Bogusława , Jurczyk Michał , Wróbel Marek , Jewiarz Marcin , Mudryk Krzysztof , Pająk Tadeusz
Published: June 2022

The combustion of waste biomass is becoming a key challenge for the energy sector, especially in times of growing energy demand and increasing environmental awareness of society. Processing such fuels is usually demanding. Mathematical modeling makes it possible to adapt the course of the process. The best option is to use appropriate fuel mixtures. The aim of this work was to develop a simple model that would allow the pre-selection of the mixture of two types of digestate. Residues from methane fermentation of maize and apple pomace were used in the study. The paper presents both a mathematical model and laboratory tests. The practical part was to check the validity of the model estimates. The model works on the basic data from physicochemical analysis. Laboratory tests included combustion of the samples of different mixtures. We used image analysis of thermograms to determine changes during combustion, which allowed us to determine when and where the main combustion occurs. Results show that a simple mathematical model is able to estimate the course of the combustion of a fuel particle.
Effect of Confinement on Detonation Velocity and Plate Dent Test Results for ANFO Explosive
Authors: Bohanek Vječislav , Sućeska Muhamed , Dobrilović Mario , Hartlieb Philipp
Published: June 2022

The detonation properties of nonideal explosives are highly dependent on charge diameter and existence and properties of confinement. In this study, the effect of different confinements on the detonation velocity of ANFO explosives was experimentally determined along with the results of the plate dent test. ANFO explosive was selected as one of the most commonly used nonideal explosives. Following the measurement results, we found that the detonation velocity increased with increasing wall thickness, and the velocity increase was different for different confinement materials. A strong correlation existed between the ratio of the mass of confiner and explosive (M/C) and the detonation velocity (R = 0.995), and between (M/C) and the depth of the dent (δ) (R = 0.975). The data presented in this paper represent preliminary findings in developing a confinement model required for reliable numerical modeling of nonideal explosives.
Retrofitting Buildings into Thermal Batteries for Demand-Side Flexibility and Thermal Safety during Power Outages in Winter
Authors: Erba Silvia , Barbieri Alessandra
Published: June 2022

Decarbonizing heating in buildings is a key part of climate change mitigation policies, but deep retrofit is progressing slowly, e.g., at a pace of 0.2%/y of the building stock in Europe. By means of tests in two flats of a multiapartment housing complex recently renovated to very low values of energy needs, this paper explores the role of deep retrofitted buildings in providing energy flexibility services for the occupants/owners/managers and for the energy system. Key to this flexibility increase and capacity savings is the large reduction of energy needs for heating via a high level of external insulation, which allows the thermal capacity of the building mass to act as an energy storage, without the large energy losses presently affecting a large part of the building stock. Due to the limited number of case studies reporting experimental applications in real buildings, this research aims to offer an analysis based on a series of tests and detailed monitoring which show a significant increase in the time interval during which the low-energy-needs building remains in the comfort range, compared to a high-energy-needs building, when active delivery of energy is deactivated during the heating season. Intermittent renewable energy might hence be stored when available, thus enhancing the ability of the energy system to manage inherent variability of some renewable energy sources and/or increasing the share of the self-consumption of locally generated RES energy. Besides, two unplanned heating power outages which have involved the entire building complex allowed us to verify that deep retrofitted buildings are able to maintain thermally safe indoor conditions under extreme events, such as a power outage, for at least 5 days.