bluuebunny/crossref_metadata_2025 huggingface dataset:
The risk of thromboembolic events with early intravenous 2‐ and 4‐g bolus dosing of tranexamic acid compared to placebo in patients with severe traumatic bleeding: A secondary analysis of a randomized, double‐blind, placebo‐controlled, single‐center trial
Authors: Spinella Philip C. , Bochicchio Kelly , Thomas Kimberly A. , Staudt Amanda , Shea Susan M. , Pusateri Anthony E. , Schuerer Douglas , Levy Jerrold H. , Cap Andrew P. , Bochicchio Grant
Published: June 2022

Background

Screening for the risk of thromboembolism (TE) due to tranexamic acid (TXA) in patients with severe traumatic injury has not been performed in randomized clinical trials. Our objective was to determine if TXA dose was independently‐associated with thromboembolism.

Study Design and Methods

This is a secondary analysis of a single‐center, double‐blinded, randomized controlled trial comparing placebo to a 2‐g or 4‐g intravenous TXA bolus dose in trauma patients with severe injury. We used multivariable discrete‐time Cox regression models to identify associations with risk for thromboembolic events within 30 days post‐enrollment. Event curves were created using discrete‐time Cox regression.

Results

There were 50 patients in the placebo group, 49 in the 2‐g, and 50 in the 4‐g TXA group. In adjusted analyses for thromboembolism, a 2‐g dose of TXA had an hazard ratio (HR, 95% confidence interval [CI]) of 3.20 (1.12–9.11) (p = .029), and a 4‐g dose of TXA had an HR (95% CI) of 5.33 (1.94–14.63) (p = .001). Event curves demonstrated a higher probability of thromboembolism for both doses of TXA compared to placebo. Other parameters independently associated with thromboembolism include time from injury to TXA administration, body mass index, and total blood products transfused.

Discussion

In patients with severe traumatic injury, there was a dose‐dependent increase in the risk of at least one thromboembolic event with TXA. TXA should not be withheld, but thromboembolism screening should be considered for patients receiving a dose of at least 2‐g TXA intravenously for traumatic hemorrhage.
Identification of Concept, Factors Affecting and the Need for Adaptive Reuse of Government Teachers’ Quarters in Malaysia
Authors: Zulkanain Mohamad Izzat , Jaafar Mastura , Salim Nuzaihan Aras Agus
Published: June 2022

Government building quarters are essential for providing adequate accommodation and improving the quality of public service employees. Following independence, the government has allocated funds to each ministry to construct its own quarters. A portion of the Ministry of Education's budget has been allocated to the construction of residential quarters for teachers in Malaysia. However, some of these quarters, particularly those located in rural areas, are underutilised and are in a deplorable state. This is due to a number of issues, including poor building maintenance, the absence of adjacent amenities, and an inappropriate location. The adaptive reuse strategy is employed to mitigate the issue. As Malaysia aims to become a modern nation, adaptive reuse as a means of achieving sustainability in the built environment. Even adaptive reuse was utilised in Malaysia, the initiative focused mostly on heritage or historical buildings. Insufficient research has been conducted on the adaptive reuse of public buildings, notably teachers' quarters. This research offers a narrative literature review of the adaptive reuse concept and the factors influencing the adaptive reuse of teachers' quarters in Malaysia. Numerous parameters affecting adaptive reuse have been revealed by previous researchers. Each researcher specifies factors depending on their research; hence, these factors are compiled and discussed throughout this work. Such aspects include actors, adaptive reuse potential, building condition, economics, location, regulations and laws, architectural value, environment, and social and cultural factors. These variables may be relevant to consider when encouraging adaptive reuse for government quarters (teachers' quarters), as they may affect the building's future or new role. In order to make appropriate decisions, one needs to have a comprehensive awareness of the relevant aspects. The second half of the study discusses the condition of teachers’ quarters and how the adaptive reuse concept could be of assistance. The study concluded with a number of research recommendations and suggestions for future research topics.
Adequacy of Fire Safety Measures of Residential Buildings in Peri-Urban Neighbourhoods of Ibadan, Oyo State, Nigeria
Authors: Oloke Olayinka Clement , Oluwatobi Afolashade O. , Amusan Lekan M.
Published: June 2022

The spate of fire outbreaks and the lack of capacity to mitigate the incidence in most urban areas reflect the violation of physical planning regulations and relevant building codes. In the past two decades, the peri-urban neighbourhoods have witnessed rapid developments as a result of the growing population in these areas. However, the level of compliance of various developments with the provisions of the existing National Fire Safety Code remains unclear. Consequently, an assessment of the adequacy of fire safety provisions vis-à-vis the effectiveness of the fire safety standards in rapidly urbanising communities became imperative. This study examined the adequacy of fire safety measures of residential buildings in peri-urban neighbourhoods of Ibadan, Oyo State, Nigeria. Relevant studies were reviewed while structured questionnaires were administered to 320 respondents across four towns in Lagelu West Local Council Development Area (LCDA). A total 296 questionnaires were retrieved representing 92.5% rate of response. Responses were analysed with basic descriptive statistic tools. The mean scores were interpreted using cut-off points and results presented in tables and charts. The study found that human activities and attitudes constituted the principal causes of fire incidence and that there is good compliance with passive fire control guidelines in the study area. However, the active fire control systems were grossly deficient in residential buildings. Moreover, lack of active fire control equipment, lack of community fire safety apparatus, and lack of reliable water supply constituted the principal challenges against effective fire control measures facing the neighbourhoods. Whilst filling the gap in literature especially on fire hazards in peri-urban areas, the study recommended the strict compliance with extant fire safety codes by property owners, provision of community fire safety apparatus by the government, residents’ awareness programs on fire safety measures, subsidized active firefighting equipment and improvement of road and water infrastructure, amongst others.
Bayesian Low-Rank Interpolative Decomposition for Complex Datasets
Authors: Lu Jun
Published: June 2022

In this paper, we introduce a probabilistic model for learning interpolative decomposition (ID), which is commonly used for feature selection, low-rank approximation, and identifying hidden patterns in data, where the matrix factors are latent variables associated with each data dimension. Prior densities with support on the specified subspace are used to address the constraint for the magnitude of the factored component of the observed matrix. Bayesian inference procedure based on Gibbs sampling is employed. We evaluate the model on a variety of real-world datasets including CCLE EC50, CCLE IC50, CTRP EC50, and MovieLens 100K datasets with different sizes, and dimensions, and show that the proposed Bayesian ID GBT and GBTN models lead to smaller reconstructive errors compared to existing randomized approaches.
Enhancing Mechanical Metamodels With a Generative Model-Based Augmented Training Dataset
Authors: Kobeissi Hiba , Mohammadzadeh Saeed , Lejeune Emma
Published: June 2022

Modeling biological soft tissue is complex in part due to material heterogeneity. Microstructural patterns, which play a major role in defining the mechanical behavior of these tissues, are both challenging to characterize and difficult to simulate. Recently, machine learning (ML)-based methods to predict the mechanical behavior of heterogeneous materials have made it possible to more thoroughly explore the massive input parameter space associated with heterogeneous blocks of material. Specifically, we can train ML models to closely approximate computationally expensive heterogeneous material simulations where the ML model is trained on datasets of simulations with relevant spatial heterogeneity. However, when it comes to applying these techniques to tissue, there is a major limitation: the number of useful examples available to characterize the input domain under study is often limited. In this work, we investigate the efficacy of both ML-based generative models and procedural methods as tools for augmenting limited input pattern datasets. We find that a style-based generative adversarial network with an adaptive discriminator augmentation mechanism is able to successfully leverage just 1000 example patterns to create authentic generated patterns. In addition, we find that diverse generated patterns with adequate resemblance to real patterns can be used as inputs to finite element simulations to meaningfully augment the training dataset. To enable this methodological contribution, we have created an open access finite element analysis simulation dataset based on Cahn–Hilliard patterns. We anticipate that future researchers will be able to leverage this dataset and build on the work presented here.
In vivo multidimensional CRISPR screens identify
Lgals2
as an immunotherapy target in triple-negative breast cancer
Authors: Ji Peng , Gong Yue , Jin Ming-liang , Wu Huai-liang , Guo Lin-Wei , Pei Yu-Chen , Chai Wen-Jun , Jiang Yi-Zhou , Liu Yin , Ma Xiao-Yan , Di Gen-Hong , Hu Xin , Shao Zhi-Ming
Published: June 2022

Immune checkpoint inhibitors exhibit limited response rates in patients with triple-negative breast cancer (TNBC), suggesting that additional immune escape mechanisms may exist. Here, we performed two-step customized in vivo CRISPR screens targeting disease-related immune genes using different mouse models with multidimensional immune-deficiency characteristics. In vivo screens characterized gene functions in the different tumor microenvironments and recovered canonical immunotherapy targets such as
Ido1
. In addition, functional screening and transcriptomic analysis identified
Lgals2
as a candidate regulator in TNBC involving immune escape. Mechanistic studies demonstrated that tumor cell–intrinsic
Lgals2
induced the increased number of tumor-associated macrophages, as well as the M2-like polarization and proliferation of macrophages through the CSF1/CSF1R axis, which resulted in the immunosuppressive nature of the TNBC microenvironment. Blockade of LGALS2 using an inhibitory antibody successfully arrested tumor growth and reversed the immune suppression. Collectively, our results provide a theoretical basis for LGALS2 as a potential immunotherapy target in TNBC.
Activation of the human chemokine receptor CX3CR1 regulated by cholesterol
Authors: Lu Minmin , Zhao Wenli , Han Shuo , Lin Xiaowen , Xu Tingyu , Tan Qiuxiang , Wang Mu , Yi Cuiying , Chu Xiaojing , Yang Weibo , Zhu Ya , Wu Beili , Zhao Qiang
Published: June 2022

As the only member of the CX3C chemokine receptor subfamily, CX3CR1 binds to its sole endogenous ligand CX3CL1, which shows notable potential as a therapeutic target in atherosclerosis, cancer, and neuropathy. However, the drug development of CX3CR1 is hampered partially by the lack of structural information. Here, we present two cryo–electron microscopy structures of CX3CR1-G
i1
complexes in ligand-free and CX3CL1-bound states at 2.8- and 3.4-Å resolution, respectively. Together with functional data, the structures reveal the key factors that govern the recognition of CX3CL1 by both CX3CR1 and US28. A much smaller conformational change of helix VI upon activation than previously solved class A GPCR-G
i
complex structures is observed in CX3CR1, which may correlate with three cholesterol molecules that play essential roles in conformation stabilization and signaling transduction. Thus, our data deepen the understanding of cholesterol modulation in GPCR (G protein–coupled receptor) signaling and provide insights into the diversity of G protein coupling.
Socioecological vulnerability and the risk of zoonotic disease emergence in Brazil
Authors: Winck Gisele R. , Raimundo Rafael L. G. , Fernandes-Ferreira Hugo , Bueno Marina G. , D’Andrea Paulo S. , Rocha Fabiana L. , Cruz Gabriella L. T. , Vilar Emmanuel M. , Brandão Martha , Cordeiro José Luís P. , Andreazzi Cecilia S.
Published: June 2022

In developing countries, outbreaks of zoonotic diseases (ZDs) result from intertwined ecological, socioeconomic, and demographic processes that shape conditions for (i) increased contact between vulnerable human population and wildlife in areas undergoing environmental degradation and (ii) the rapid geographic spread of infections across socially vulnerable regions. In Brazil, recent increases in environmental and social vulnerabilities, amplified by economic and political crises, are potential triggers for outbreaks. We discuss Brazilian features that favor outbreaks and show a novel quantitative method for zoonotic risk assessment. Using data on nine ZDs from 2001 to 2019, we found that the most significant causal variables were vegetation cover and city remoteness. Furthermore, 8 of 27 states presented low-level risk of ZD outbreaks. Given the ZD-bushmeat connection, we identified central hunted mammals that should be surveilled to prevent spillover events. The current challenge is to coordinate intersectoral collaboration for effective One Health management in megadiverse countries with high social vulnerability and growing environmental degradation like Brazil.
Rethinking computing hardware for robots
Authors: Sandamirskaya Yulia
Published: June 2022

Emerging computing hardware systems address the need of robotic AI for robust, fast, and efficient computation for a variety of tasks.
Closed-loop stimulation using a multiregion brain-machine interface has analgesic effects in rodents
Authors: Sun Guanghao , Zeng Fei , McCartin Michael , Zhang Qiaosheng , Xu Helen , Liu Yaling , Chen Zhe Sage , Wang Jing
Published: June 2022

Effective treatments for chronic pain remain limited. Conceptually, a closed-loop neural interface combining sensory signal detection with therapeutic delivery could produce timely and effective pain relief. Such systems are challenging to develop because of difficulties in accurate pain detection and ultrafast analgesic delivery. Pain has sensory and affective components, encoded in large part by neural activities in the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC), respectively. Meanwhile, studies show that stimulation of the prefrontal cortex (PFC) produces descending pain control. Here, we designed and tested a brain-machine interface (BMI) combining an automated pain detection arm, based on simultaneously recorded local field potential (LFP) signals from the S1 and ACC, with a treatment arm, based on optogenetic activation or electrical deep brain stimulation (DBS) of the PFC in freely behaving rats. Our multiregion neural interface accurately detected and treated acute evoked pain and chronic pain. This neural interface is activated rapidly, and its efficacy remained stable over time. Given the clinical feasibility of LFP recordings and DBS, our findings suggest that BMI is a promising approach for pain treatment.
Challenges in the Treatment of Invasive Aspergillosis in Immunocompromised Children
Authors: Hsu Alice J. , Tamma Pranita D. , Fisher Brian T.
Published: June 2022

Invasive aspergillosis (IA) is associated with significant morbidity and mortality. Voriconazole remains the drug of choice for the treatment of IA in children; however, the complex kinetics of voriconazole in children make dosing challenging and therapeutic drug monitoring (TDM) essential for treatment success.
The Novel Monooxygenase Gene
dipD
in the
dip
Gene Cluster of
Alcaligenes faecalis
JQ135 Is Essential for the Initial Catabolism of Dipicolinic Acid
Authors: Mu Yang , Xu Siqiong , Liu Guiping , Cheng Minggen , Dai Weixian , Chen Qing , Yan Xin , Hong Qing , He Jian , Jiang Jiandong , Qiu Jiguo
Published: June 2022

Dipicolinic acid (DPA) is a natural pyridine derivative that serves as an essential component of the
Bacillus
spore. DPA accounts for 5 to 15% of the dry weight of spores.
A Method for Variant Agnostic Detection of SARS-CoV-2, Rapid Monitoring of Circulating Variants, and Early Detection of Emergent Variants Such as Omicron
Authors: Lai Eric , Kennedy Emily B. , Lozach Jean , Hayashibara Kathleen , Davis-Turak Jeremy , Becker David , Brzoska Pius , Cassens Tyler , Diamond Evan , Gandhi Manoj , Greninger Alexander L. , Hajian Pooneh , Leonetti Nicole A. , Nguyen Jason M. , O’Donovan K. M. Clair , Peck Troy , Ramirez Jimmy M. , Roychoudhury Pavitra , Sandoval Efren , Wesselman Cassandra , Wesselman Timothy , White Simon , Williams Stephen , Wong David , Yu Yufei , Creager Richard S.
Published: June 2022

The rapid emergence of SARS-CoV-2 variants raised public health questions concerning the capability of diagnostic tests to detect new strains, the efficacy of vaccines, and how to map the geographical distribution of variants to understand transmission patterns and loads on healthcare resources. Next-generation sequencing (NGS) is the primary method for detecting and tracing new variants, but it is expensive, and it can take weeks before sequence data are available in public repositories.
The Myeloid-Specific Transcription Factor PU.1 Upregulates Mannose Receptor Expression but Represses Basal Activity of the HIV-LTR Promoter
Authors: Kao Sandra , Miyagi Eri , Mallorson Rosa , Saito Hideki , Sukegawa Sayaka , Mukherji Abhik , Mateja Allyson , Ferhadian Damien , Fabryova Helena , Clouse Kathleen , Strebel Klaus
Published: June 2022

HIV-1 replication in primary human cells depends on the activity of virus-encoded proteins but also involves cellular factors that can either promote (viral dependency factors) or inhibit (host restriction factors) virus replication. In previous work, we identified human MRC1 as a macrophage-specific host restriction factor that inhibits the detachment of viral particles from infected cells.
Aeromonas hydrophila Induces Skin Disturbance through Mucosal Microbiota Dysbiosis in Striped Catfish (
Pangasianodon hypophthalmus
)
Authors: Chen Li-Hsuan , Lin Chia-Hsuan , Siao Ru-Fang , Wang Liang-Chun
Published: June 2022

The fish skin mucosal microbiota is essential in modulating the host response to the presence of pathogens. Our study provides a platform to study both the correlation and causation of the interactions among the pathogen, fish skin, and the skin mucosal microbiota.
Russia and the United States in the Arctic: from Competition to Confrontation
Authors: Raikov Yu. A.
Published: June 2022

The rivalry of great powers for hegemony and influence that has unfolded in the modern multipolar world is increasingly evident in the Arctic, a strategically important region of the world and exceptionally rich in natural resources. Тhe attractiveness of the Arctic and the competition of great powers around it are growing due to the objective changes taking place in the Far North in four important areas (technology, economy, climate, and law), contributing to the economic development of the region. The clash of interests, primarily between the United States and Russia, is leading to an increase in military and political tension, turning the once abandoned peripheral region of the Far North into a zone of increased information, as well as political, and, most importantly, military activity of the superpowers. This article analyzes the increased popularity of the Arctic topic in the foreign policy of the United States and Russia, which is associated with climate warming, the accelerated melting of glaciers in the Arctic, the prospect of opening new sea routes, and the growing activity of the Arctic and other states, primarily China, in the region. Against the background of recent international events related to the Arctic, the Arctic direction of the foreign policy of Washington, Moscow, and Beijing is considered. The emphasis is placed on its military‒political component, given that the growing political tension in the Arctic can lead to a new generation of armed conflicts and exacerbate the situation not only in the Northern region but also in the international arena as a whole. The situation is aggravated by the fact that there are no recognized norms of international law in the Arctic regarding the Arctic states’ ownership of concrete vast sectors of the Arctic Ocean and islands in its aquatic area. The delimitation of maritime boundaries and the determination of rights to economic activities here remain the main problem. Political tension is growing.
Peasants and Power in 1917: the Localization of the Revolution
Authors: Kabytov P. , Kabytova N.
Published: June 2022

This article analyzes the relationship between the peasants and the authorities during the Great Russian Revolution. The stages in the study of this problem are highlighted. When considering the mechanism of formation of power structures in rural areas, the specifics of sociopolitical associations in the countryside, which differed from those in the city, are revealed. The self-organization of the rural world manifested itself in the creation of grassroots peasant committees that implemented communal forms of popular representation. The organizing role of the provincial and district peasant congresses, which the peasants endowed with law-making powers, is shown. The reasons for the support of the peasants, both by community members and small owners, of Soviet power are revealed, the ways of its constitution on the ground are considered, and the use of power without justification of the traditional folk values is proved.
Advances in the management of idiopathic pulmonary fibrosis and progressive pulmonary fibrosis
Authors: Liu Gabrielle Y , Budinger G R Scott , Dematte Jane E
Published: June 2022

Similarly to idiopathic pulmonary fibrosis (IPF), other interstitial lung diseases can develop progressive pulmonary fibrosis (PPF) characterized by declining lung function, a poor response to immunomodulatory therapies, and early mortality. The pathophysiology of disordered lung repair involves common downstream pathways that lead to pulmonary fibrosis in both IPF and PPF. The antifibrotic drugs, such as nintedanib, are indicated for the treatment of IPF and PPF, and new therapies are being evaluated in clinical trials. Clinical, radiographic, and molecular biomarkers are needed to identify patients with PPF and subgroups of patients likely to respond to specific therapies. This article reviews the evidence supporting the use of specific therapies in patients with IPF and PPF, discusses agents being considered in clinical trials, and considers potential biomarkers based on disease pathogenesis that might be used to provide a personalized approach to care.
Association between democratic governance and excess mortality during the COVID-19 pandemic: an observational study
Authors: Jain Vageesh , Clarke Jonathan , Beaney Thomas
Published: June 2022

Background

Excess mortality has been used to assess the overall health impact of COVID-19 across countries. Democracies aim to build trust in government and enable checks and balances on decision making, which may be useful in a pandemic. But during the pandemic, they have been criticised as being hesitant to enforce restrictive public health measures.

Methods

Through linking open-access datasets we constructed univariable and multivariable linear regression models investigating the association between country V-Dem Liberal Democracy Indices (LDI), representing strength of democratic governance and excess mortality rates, from January 2020 to September 2021. We adjusted for several important confounders and conducted a range of sensitivity analyses to assess the robustness of our findings.

Results

Across 78 countries, 4.19 million deaths million excess deaths were recorded. On multivariable regression, a one-point increase in V-Dem LDI was associated with a decrease in excess mortality of 2.18 per 100 000 (p=0.004), after accounting for age, gender, wealth and universal health coverage. This association was only partially attenuated by COVID-19 vaccination rates and remained robust in all sensitivity analyses.

Conclusions

Democratic governance may have played an important role in mitigating the overall health impact of COVID-19 across countries. This study strengthens the case to broaden the scope of traditional pandemic risk assessment and discussions on preparedness.
A no-go theorem for scalar fields with couplings from Ginzburg–Landau models
Authors: Liu Guohua , Peng Yan
Published: June 2022

Recently Hod proved a no-go theorem that static scalar fields cannot form spherically symmetric boson stars in the asymptotically flat background. On the other side, scalar fields can be coupled to the gradient according to next-to-leading order Ginzburg–Landau models. In the present work, we extend Hod’s discussions by considering couplings between static scalar fields and the field gradient. For a non-negative coupling parameter, we show that there is no asymptotically flat spherically symmetric boson stars made of coupled static scalar fields.
Intelligent networks knacks for numerical treatment of nonlinear multi-delays SVEIR epidemic systems with vaccination
Authors: Shoaib Muhammad , Anwar Nabeela , Ahmad Iftikhar , Naz Shafaq , Kiani Adiqa Kausar , Raja Muhammad Asif Zahoor
Published: June 2022

This paper portrays the exploitation/exploration of artificial intelligence (AI) inspired computing to study the behavior of the multi-delay differential systems that revealed the impact of latent period and the dynamics of the a susceptible, vaccinated, exposed, infectious and recovered (SVEIR) epidemic model involving vaccination by means of the neural networks backpropagation with Levenberg–Marquardt scheme (NNs-BLMS). The reference solutions of the five classes ordinary differential equations (ODEs) model of SVEIR dynamics are calculated by applying the Adams method for variation in delay due to the time spent in preventing the infection and delay due to the duration of recovery immunity in the cured population. The designed NNs-BLMS used the created dataset arbitrarily for training, validation, as well as, testing samples to determine the estimated results of the nonlinear SVEIR epidemic model involving vaccination impact. The achieved accuracy of the designed NNs-BLMS is authenticated/proven by analyzing the fitness function based on mean square error (MSE), regression analysis, and error histogram for sundry scenarios of SVEIR epidemic system with the impact of vaccination.
Bandgap structure and density of states of two-dimensional magnetized plasma photonic crystals
Authors: Gharaati Abdolrasoul , Esmaeili Fariba
Published: June 2022

In this paper, we study dispersion properties in a two-dimensional magnetized plasma photonic crystal, based on the modified plane wave expansion method. Transverse electric polarization is considered. The influence of different factors such as external magnetic field, lattice constant, dielectric constants of background and central rod, and electron density of plasma in photonic bandgap structure and photonic bandgap map is studied. The photonic density of states is also demonstrated. Results show that a large magnetic field produces more band gaps in higher frequencies. The lattice constant is an important factor in controlling the bandgap structure and it can compensate low magnetic field effect. Using a larger dielectric constant in the background compared to the central dielectric rod leads to more and sharper band gaps in higher frequencies. Electron density of plasma plays an important role in tuning bandgap structure. There would be three or four band areas in the band structure and the band frequencies are larger than the cutoff frequency of plasma.
Deep Learning Feature Extraction Using Attention-Based DenseNet 121 for Copy Move Forgery Detection
Authors: Rajkumar Rajeev
Published: June 2022

In modern society, digital images can be far-reaching, and the images are manipulated by various software and hardware technologies. The image forgery activities are undertaken by the attackers mainly for damaging the reputation of people or receiving fiscal gain, etc. Taking this into consideration, many techniques are developed to detect the forged images. In this paper, a new deep learning-based approach is introduced for copy-move forgery detection. The input images are segmented into non-overlapping patches using superpixel-based modified dense peak clustering and the features are extracted from the segmented patches by applying deep learning structure of attention-based DenseNet 121 model. Besides, to compare every block, the depth of each pixel is reconstructed, and eventually matching process is carried out using the adaptive chimp patch matching approach, which detects the suspicious forged regions in an image. Finally, the matched keypoints are merged with the segmented patches using the merged keypoint matching algorithm. As a result, the new deep learning approach has detected the forged regions efficiently from the tampered image with less time even the image is compressed, rotated, or scaled. The performance is evaluated in terms of recall, precision, accuracy, F1-score, computational time, and False Positive Rate (FPR). Moreover, the performance is compared with the other existing approaches, and the outcomes showed that the proposed method has achieved higher accuracy of 97%, recall of 99%, precision of 97.84%, F1-score of 98.81%, FPR of 0.022 and less computational time of 2.5 s.
Food Image-based Nutritional Management System to Overcome Polycystic Ovary Syndrome using DeepLearning: A Systematic Review
Authors: Kaur Rajdeep , Kumar Rakesh , Gupta Meenu
Published: June 2022

Polycystic Ovary Syndrome (PCOS) is one of the growing non-communicable diseases in those women who do not take proper nutrients in their meals. Medically, it is not proven that an unhealthy diet is the only cause of PCOS, but it is one of the major causes behind this disease. PCOS is an endocrine disorder that influences 8–10% of women at their reproductive age and may cause infertility or other health problems. Deep Learning (DL) is a popular technique to classify the food images for identifying the nutrients in the food. This work considers food image datasets (FOOD-101, UEC-256, UEC-100, etc.) to analyze the food image using pre-trained Convolutional Neural Network (CNN) and a nutritional information dataset for identifying the nutrients in food. The proposed study aims to find the solution to overcome the PCOS problem in women by tracking nutrient intake using food images and recommending the diet. Further, this study will also provide comprehensive review of image classification and recommendation techniques that may help the dieticians to track the nutrient intake using food images provided by PCOS patients to overcome the disease.
Virtual classrooms during Covid-19
Authors: Islam S. M. Rakibul , Halder Nabarun , Islam Ashraful , Ahmed Eshtiak , Noori Sheak Rashed Haider
Published: June 2022

The
Interactions
website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.
Church after Sunday
Authors: O'Leary Teresa K. , Stowell Elizabeth , Sackitey Darley , Yun Hye Sun , Wright David , Paasche-Orlow Michael , Bickmore Timothy , Parker Andrea G.
Published: June 2022

Community + Culture features practitioner perspectives on designing technologies for and with communities. We highlight compelling projects and provocative points of view that speak to both community technology practice and the interaction design field as a whole.
--- Sheena Erete, Editor
Efficient and Effective Feature Space Exploration for Testing Deep Learning Systems
Authors: Zohdinasab Tahereh , Riccio Vincenzo , Gambi Alessio , Tonella Paolo
Published: June 2022

Assessing the quality of Deep Learning (DL) systems is crucial, as they are increasingly adopted in safety-critical domains. Researchers have proposed several input generation techniques for DL systems. While such techniques can expose failures, they do not explain which features of the test inputs influenced the system’s (mis-) behaviour.
DeepHyperion
was the first test generator to overcome this limitation by exploring the DL systems’ feature space at large. In this article, we propose
DeepHyperion-CS
, a test generator for DL systems that enhances
DeepHyperion
by promoting the inputs that contributed more to feature space exploration during the previous search iterations. We performed an empirical study involving two different test subjects (i.e., a digit classifier and a lane-keeping system for self-driving cars). Our results proved that the contribution-based guidance implemented within
DeepHyperion-CS
outperforms state-of-the-art tools and significantly improves the efficiency and the effectiveness of
DeepHyperion
.
DeepHyperion-CS
exposed significantly more misbehaviours for five out of six feature combinations and was up to 65% more efficient than
DeepHyperion
in finding misbehaviour-inducing inputs and exploring the feature space.
DeepHyperion-CS
was useful for expanding the datasets used to train the DL systems, populating up to 200% more feature map cells than the original training set.
An Empirical Survey on Long Document Summarization: Datasets, Models, and Metrics
Authors: Koh Huan Yee , Ju Jiaxin , Liu Ming , Pan Shirui
Published: June 2022

Long documents such as academic articles and business reports have been the standard format to detail out important issues and complicated subjects that require extra attention. An automatic summarization system that can effectively condense long documents into short and concise texts to encapsulate the most important information would thus be significant in aiding the reader’s comprehension. Recently, with the advent of neural architectures, significant research efforts have been made to advance automatic text summarization systems, and numerous studies on the challenges of extending these systems to the long document domain have emerged. In this survey, we provide a comprehensive overview of the research on long document summarization and a systematic evaluation across the three principal components of its research setting: benchmark datasets, summarization models, and evaluation metrics. For each component, we organize the literature within the context of long document summarization and conduct an empirical analysis to broaden the perspective on current research progress. The empirical analysis includes a study on the intrinsic characteristics of benchmark datasets, a multi-dimensional analysis of summarization models, and a review of the summarization evaluation metrics. Based on the overall findings, we conclude by proposing possible directions for future exploration in this rapidly growing field.
The Keys to the Kingdom
Authors: Vachon Phil
Published: June 2022

An unlucky fat-fingering precipitated the current crisis: The client had accidentally deleted the private key needed to sign new firmware updates. They had some exciting new features to ship, along with the usual host of reliability improvements. Their customers were growing impatient, but my client had to stall when asked for a release date. How could they come up with a meaningful date? They had lost the ability to sign a new firmware release.
Environmental Policies Benefit Economic Development: Implications of Economic Geography
Authors: Morgan Seth , Pfaff Alexander , Wolfersberger Julien
Published: June 2022

For over a century, starting with the work of Alfred Marshall (and also in resource economics), economic geography has emphasized the productivity of dense urban agglomerations. Yet little attention is paid to one key policy implication of economic geography's core mechanisms: Environmental policies can aid economic development, per se—not hurting the economy to help the environment but advancing both objectives. We review mechanisms from economic geography that imply that environmental policies can deliver such win-wins: influences upon agglomeration of long-standing natural conditions, like usable bays, which long were perceived as fixed yet now are being shifted by global environmental quality; agglomeration's effects on other influential conditions, like urban environmental quality; and the effects of rural environmental quality on the flows to cities of people and environmental quality. Finally, we consider a geographic policy typology in asking why society leaves money on the table by failing to promote environmental policies despite the potential win-wins that we highlight.
Investigation of Water-Washing Effect on Electrochemical Properties of Ni-Rich NCA Cathode Material for Lithium-Ion Batteries
Authors: Kato Yukihiro , Nagahara Akiko , Gerile Naren , Fujinaka Shota , Hamamoto Nishiki , Nishimura Hitoshi , Nakai Hideki
Published: June 2022

This study was conducted to understand the effect of well-known water-washing process on Ni-rich LiNi0.885Co0.100Al0.015O2 (NCA) cathode material, which reduces the amount of residual lithium compounds in NCA to improve lithium-ion batteries (LIBs) characteristics. X-ray absorption fine structure (XAFS) analysis revealed that the oxidation state of the surface nickel in washed NCA particles was reduced. The fact was consistent with the increase in charge transfer resistance (Rct) measured by the electrochemical impedance spectroscopy (EIS) method. From X-ray photoelectron spectroscopy (XPS) and Auger electron spectroscopy (AES) analyses, it was found that the residual lithium compounds were washed away not only from the surface of NCA particles but also from grain boundaries or voids in between primary particles of NCA, by water-washing process. Differential scanning calorimetry (DSC) measurements suggested that an increase in specific surface area of NCA particles by water-washing was attributed a rapid heat release from the charged cathode material during heating to. On the other hand, cross-sectional scanning electron microscope (SEM) images showed that the crack formation inside washed NCA particles were suppressed after charge/discharge cycling. As a result, the capacity retention of washed NCA electrode at 60 °C cycling was improved.
Study on Steady-State Temperature Rise Characteristics of Motor Heat Balance under Load Rate
Authors: Zhou Feng , Wang Pengfei , Bai Xingzhen , Cui Baochun , Lyu Qiang
Published: June 2022

The influence of load rate and other factors is not considered in the temperature rise monitoring process of traditional motor, which will lead to the wrong diagnosis of motor health status. In this paper, the characteristics of motor temperature rise under different ambient temperatures and different load factors are studied. Taking an 11 kW three-phase asynchronous motor as an example, the temperature rise model of the motor is based on the finite element method established using ANSYS software. The thermal conductivity coefficient and surface heat dissipation coefficient of each part of the motor are determined. According to the electromagnetic field calculation, the motor loss distribution is obtained, and the temperature rise distribution curve of the motor is obtained by the simulation of the three-dimensional steady temperature field of the motor. In this paper, the temperature rise variation law of the motor considering the influence of environmental temperature and load factors is proposed, and combined with the temperature rise threshold curve of the motor under different load rates, a dynamic threshold judgment system is established to accurately warn the motor of overheating under any load rate. When the motor’s health status changes after running for a long time, it provides an important theoretical basis for accurate diagnosis of the motor’s health status.
Advance of Serum Biomarkers and Combined Diagnostic Panels in Nonalcoholic Fatty Liver Disease
Authors: Zeng Yuping , He He , An Zhenmei
Published: June 2022

Nonalcoholic fatty liver disease (NAFLD) affects approximately 25-30% population worldwide, which progresses from simple steatosis to nonalcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and hepatocellular carcinoma, and has complications such as cardiovascular events. Liver biopsy is still the gold standard for the diagnosis of NAFLD, with some limitations, such as invasive, sampling deviation, and empirical judgment. Therefore, it is urgent to develop noninvasive diagnostic biomarkers. Currently, a large number of NAFLD-related serum biomarkers have been identified, including apoptosis, inflammation, fibrosis, adipokines, hepatokines, and omics biomarkers, which could effectively diagnose NASH and exclude patients with progressive fibrosis. We summarized serum biomarkers and combined diagnostic panels of NAFLD, to provide some guidance for the noninvasive diagnosis and further clinical studies.
The Design of Personalized Education Resource Recommendation System under Big Data
Authors: Fu Rong , Tian Mijuan , Tang Qianjun
Published: June 2022

With the advent of the Internet and the era of big data, education is increasingly dependent on data resources to support product and business innovation, and the lack of data resources has severely limited the areas involved. As a general information filtering method, personalized recommendation systems analyze the historical interaction data between users and items to build user interest models in an environment of “information overload”, allowing users to discover and recommend information that interests them. However, the explosive growth of information in the network makes users wander in the sea of information, and it is increasingly difficult to find the information they really need, i.e., information overload. This has given rise to personalized recommendation systems, which currently have more mature applications in industries such as e-commerce, music services, and movie services. To this end, this paper studies and implements a customized educational resource recommendation system that can handle big data. The results show that the values of different similarity calculations all fluctuate with the gradual increase of the number of nearest neighbors, and the algorithm in this paper is maximum at the number of neighbors around 60; then, it is inferred that applying the calculation method to the recommendation algorithm will improve the recommendation accuracy. Therefore, education uses the concept of big data to process the huge amount of education data and find some correlations and laws in education, so as to realize “teaching according to the material, teaching according to the material”.
Theoretical Analysis of Isentropic Exponent Effects on the Internal Flow of Supercritical CO2 Centrifugal Compressors
Authors: Zhao Ben , Liu Zhiyuan
Published: June 2022

The isentropic exponent in the near-critical region can sharply change with operation condition transitions of a closed Brayton cycle, thus having significant impacts on the compressor aerodynamic performance and the internal flow of the compressor embedded into the cycle. This paper investigates the isentropic exponent effects of the supercritical carbon dioxide (CO2) using theoretical analyses and three-dimensional computational fluid dynamic (CFD) simulations. The theoretical analysis reveals that the increasing isentropic exponent mitigates the impeller work coefficient and the impeller outlet Mach number but raises the suction side Mach number of the impeller blade and blade loading at the leading edge. The CFD results provide validations for the theoretical results. Based on the findings, a suggestion is offered to guide the physical experiment design of a centrifugal compressor with supercritical CO2 as its working fluid.
Research on Online Social Network Information Leakage-Tracking Algorithm Based on Deep Learning
Authors: Han Shuhe
Published: June 2022

The rapid iteration of information technology makes the development of online social networks increasingly rapid, and its corresponding network scale is also increasingly large and complex. The corresponding algorithms to deal with social networks and their corresponding related problems are also increasing. The corresponding privacy protection algorithms such as encryption algorithm, access control strategy algorithm, and differential privacy protection algorithm have been studied and analyzed, but these algorithms do not completely solve the problem of privacy disclosure. Based on this, this article first searches and accurately filters the relevant information and content of online social networks based on the deep convolution neural network algorithm, so as to realize the perception and protection of users’ safe content. For the corresponding graphics and data, this article introduces the compressed sensing technology to randomly disturb the corresponding graphics and data. At the level of tracking network information leakage algorithm, this article proposes a network information leakage-tracking algorithm based on digital fingerprint, which mainly uses relevant plug-ins to realize the unique identification processing of users, uses the uniqueness of digital fingerprint to realize the tracking processing of leakers, and formulates the corresponding coding scheme based on the social network topology, and at the same time, the network information leakage-tracking algorithm proposed in this article also has high efficiency in the corresponding digital coding efficiency and scalability. In order to verify the advantages of the online social network information leakage-tracking algorithm based on deep learning, this article compares it with the traditional algorithm. In the experimental part, this article mainly compares the accuracy index, recall index, and performance index. At the corresponding accuracy index level, it can be seen that the maximum improvement of the algorithm proposed in this article is about 10% compared with the traditional algorithm. At the corresponding recall index level, the proposed algorithm is about 5–8% higher than the traditional algorithm. Corresponding to the overall performance index, it improves the performance by about 50% compared with the traditional algorithm. The comparison results show that the proposed algorithm has higher accuracy and the corresponding source tracking is more accurate.
Stage Performance Characteristics of Minority Dance Based on Human Motion Recognition
Authors: Shi Yuzhu
Published: June 2022

Due to the different development history of different nationalities, there is a big gap between traditional cultures. Especially, as an important embodiment and component of traditional culture, ethnic minority dance has unique characteristics. With the development of machine vision technology, human motion recognition has gradually become a hot research direction, but its application research in the field of dance motion recognition is still in its infancy. In this paper, the characteristics of stage performances of ethnic minority dances in the field of intelligent auxiliary training based on human motion recognition technology are introduced. The human body regions in the frame are obtained by using human posture, and 3D_SIFT and optical flow features are extracted from each region. Then, we use the extracted key frames and DTW (dynamic time warping) algorithm to realize the motion recognition of the motion capture data and carry out simulation experiments. The test results show that the algorithm can identify the minority dance video database, effectively identify the dance movements, and realize the movement correction of dancers.