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Rastall introduced a stress-energy tensor whose divergence is proportional to the gradient of the Ricci scalar. This proposal leads to a change in the form of the field equations of General Relativity, but it preserves the number of degrees of freedom. Rastall's field equations can be either interpreted as GR with a redefined SET, or it can imply different physical consequences inside the matter sector. We investigate limits under which the Rastall field equations can be directly derived from an action, in particular from two $f(R)$-gravity extensions: $f(R,\mathcal L_m)$ and $f(R,T)$. We show that there are similarities between these theories, but the Rastall SET cannot be fully recovered from them, apart from certain particular cases here discussed. It is remarkable that a simple, covariant and invertible redefinition of the SET, as the one proposed by Rastall, is hard to be directly implemented in the action., Comment: 11 pages, accepted for publication in the European Physical Journal Plus

Background The X-linked PTCHD1 locus is strongly associated with autism spectrum disorder (ASD). Males who carry chromosome microdeletions of PTCHD1 antisense long non-coding RNA (PTCHD1-AS)/DEAD-box helicase 53 (DDX53) have ASD, or a sub-clinical form called Broader Autism Phenotype. If the deletion extends beyond PTCHD1-AS/DDX53 to the next gene, PTCHD1, which is protein-coding, the individuals typically have ASD and intellectual disability (ID). Three male siblings with a 90 kb deletion that affects only PTCHD1-AS (and not including DDX53) have ASD. We performed a functional analysis of DDX53 to examine its role in NGN2 neurons. Methods We used the clustered regularly interspaced short palindromic repeats (CRISPR) gene editing strategy to knock out DDX53 protein by inserting 3 termination codons (3TCs) into two different induced pluripotent stem cell (iPSC) lines. DDX53 CRISPR-edited iPSCs were differentiated into cortical excitatory neurons by Neurogenin 2 (NGN-2) directed differentiation. The functional differences of DDX53-3TC neurons compared to isogenic control neurons with molecular and electrophysiological approaches were assessed. Results Isogenic iPSC-derived control neurons exhibited low levels of DDX53 transcripts. Transcriptional analysis revealed the generation of excitatory cortical neurons and DDX53 protein was not detected in iPSC-derived control neurons by western blot. Control lines and DDX53-3TC neurons were active in the multi-electrode array, but no overt electrophysiological phenotype in either isogenic line was observed. Conclusion DDX53-3TC mutation does not alter NGN2 neuronal function in these experiments, suggesting that synaptic deficits causing ASD are unlikely in this cell type.

Although the share of older people has been growing in Brazil in past decades, studies investigating trends in overweight and obesity prevalence in this population remain scarce. The objective of this study was to analyze the time trend of overweight and obesity prevalence in older adults in Brazilian State Capitals and the Federal District from 2006 to 2019. This is a time trend study based on data from the Surveillance System for Risk and Protective Factors for Chronic Diseases by Telephone Survey. The subsample used was composed of individuals aged 60 years or older (n = 202,049). Self-reported weight and height data were used to calculate Body Mass Index (BMI). Overweight (BMI ≥ 25 kg/m2) and obesity (BMI ≥ 30 kg/m2) prevalence were estimated per year for the total population and according to sex, age, schooling, region, and NCD presence. Prais–Winsten regression models were used to identify significant trends in overweight and obesity prevalence over the years. Overweight prevalence increased (p

Schistosomiasis is a major public health problem that afflicts more than 240 million individuals globally, particularly in poor communities. Treatment of schistosomiasis relies heavily on a single oral drug, praziquantel, and there is interest in the search for new antischistosomal drugs. This study reports the anthelmintic evaluation of carvacryl acetate, a derivative of the terpene carvacrol, against Schistosoma mansoni ex vivo and in a schistosomiasis animal model harboring either adult (patent infection) or juvenile (prepatent infection) parasites. For comparison, data obtained with gold standard antischistosomal drug praziquantel are also presented. Initially in vitro effective concentrations of 50% (EC50) and 90% (EC90) were determined against larval and adult stages of S. mansoni. In an animal with patent infection, a single oral dose of carvacryl acetate (100, 200, or 400 mg/kg) caused a significant reduction in worm burden (30–40%). S. mansoni egg production, a process responsible for both life cycle and pathogenesis, was also markedly reduced (70–80%). Similar to praziquantel, carvacryl acetate 400 mg/kg had low efficacy in pre-patent infection. In tandem, although carvacryl acetate had interesting in vitro schistosomicidal activity, the compound exhibited low efficacy in terms of reduction of worm load in S. mansoni-infected mice.

Content-centric networking (CCN) has emerged as a promising future Internet architecture that brings effective content retrieval mechanism. In CCN, the contents are accessed using their names instead of searching for the host location in the network. To improve Quality-of-Service for the users, CCN offers the in-network caching capability that places the incoming contents in the intermediate on-path nodes. During content retrieval, the in-network caching reduces network delay and bandwidth requirements as the requester access the content from the nearest node having a copy of the required content. Therefore, it is crucial to control the content caching decisions in the CCN as the efficiency of the caching scheme largely affects the performance of the network. In this context, a novel content placement scheme is proposed that considers node degree centrality and content provider distance based on the network bandwidth parameters for effective content placement decisions. A heuristic approach has been proposed that investigates the compound effect of these parameters to minimize average delay and network traffic during retrieval of the requested content. The proposed caching scheme has been implemented on the Abilene network topology and evaluated for different content access patterns and caching capacities. Extensive simulation results demonstrate the superiority of the proposed scheme on the existing peer schemes for various performance parameters such as average network hop-count, delay, and network traffic load.

Reinforced concrete beams lose resistance at high temperatures and so as to guarantee their good performance, it is mandatory to consider this accidental action in design. As an alternative to the methods proposed by the European and Brazilian standards, above all the tabular method that, despite its simple application, limits calculations to a few pre-defined values, a new tool for fire design of these beams is proposed herein. A graphical method was developed with the Temperature Calculation and Design (TCD) software, which performs transient bi-dimensional thermo-structural analysis using the finite element method. The main variable of this method consists of the resistance to the bending moment of a heated cross-section. For its validation, the moment of four rectangular sections was determined according to four simplified methods. These results were compared among themselves and to those obtained by a more advanced method. They were all verified to lead to similar results. With regard to the TCD, whose results were used for the preparation of the graphical method, the maximum difference compared to a more advanced method was approximately 2.5%. After validation, graphs that associate the ratio between the applied moment in fire and the resistance to bending moment at ambient temperature to the fire resistance for different beams were created. These graphs present design solutions for more than 2300 beam models with T-shaped cross-sections in assorted lengths, heights, covers and reinforcement arrangements, both positive and negative. In the application examples, the results from the graphical method were generally more economic when compared to the tabular method of the fire design standards.

This paper presents the development of a finite element model for predicting the flexural behavior of cold-formed steel beams subjected to fire and with restrained thermal elongation (different grades of axial and rotational restraint). The numerical simulations were carried out with the finite element software Abaqus/CAE. The numerical model was compared with experimental results in order to validate it for further parametric studies. This paper provides details of the simulation methodology for achieving the numerical stability and faithful representation of detailed structural behavior. To verify the accuracy of the Abaqus model the numerical and experimental results were compared in terms of axial restraining forces, vertical mid-span deflections, critical temperatures and failure modes of the beams. The numerical results showed a good agreement with the experimental ones. However, the critical temperatures determined numerically were slightly lower than the numerical ones in the simply supported and axially restrained beams and the opposite in the axially and rotationally restrained beams, this for both sigma and 2-sigma section beams.

In current digital era, users of various social media upload photos which usually contain tremendous amount of private information on daily basis. Though the private information contained within photos can assist enterprises to provide users with better services, it is also at the risk of being disclosed. Especially, with deep learning techniques developed for object detection tasks, users’ privacy can be extracted with no difficulty. Therefore, we propose an approach to prevent DNN detectors from detecting private objects, especially human body. An algorithm is developed by exploiting an inherent vulnerability of deep learning models known as the adversarial sample problem, and is integrated under a general framework which is also proposed in this work. We evaluate our method on the task of reducing the performance of DNN detectors on PASCAL VOC dataset. Our proposed algorithm can reduce the recall of human detection from 81.1% to 18.0%, while having few effects on pixel value. The results show that our proposed method performs remarkably well on preventing privacy from being exposed by DNN detectors, while causing very limited degradation to the visual quality of images.

Complex agroforestry systems (AFS) based on the diversity of natural ecosystems have been strategic for the restoration of the degraded areas allying biodiversity conservation with food production that allows minimizing restoration costs. We supposed that AFS may provide diversification of production and earnings for farmers over time depending on the system design, species utilized, and management. The financial analysis conducted in a biodiverse, multistratified, and successional AFS, with 20 species mostly from the Rainforest, associated with annual species pointed out that the restoration costs were minimized. The financial analysis was based on technical and economic coefficients obtained for two years and simulated for 20 years. The established AFS (EAFS), however, demonstrated low financial performance in the first 10 years of the project with IRR of -3.9% and 5.6% and B/C ratio of 0.8 and 1.1 for 10 and 20 years, respectively, with payback in 14 years. Based on the results obtained, simulation of a system (SAFS) was carried out from changes in the arrangement, species selection, and management. The SAFS presented IRR of 20.7% e 13.5%, B/C ratio of 1.6 and 1.8 for 10 and 20 years, respectively, with payback in 2 years. It has been shown that financial analysis might help to promote agroforestry restoration and the environmental compliance of rural properties through the financial prognosis of the system that allows the generation of products with a positive financial balance over the time required for the ecological restoration process.

Curcumin, a potent phytochemical derived from the spice element turmeric, has been identified as a herbal remedy decades ago and has displayed promise in the field of medicinal chemistry. However, multiple traits associated with curcumin, such as poor bioavailability and instability, limit its effectiveness to be accepted as a lead drug-like entity. Different reactive sites in its chemical structure have been identified to incorporate modifications as attempts to improving its efficacy. The diketo group present in the center of the structural scaffold has been touted as the group responsible for the instability of curcumin, and substituting it with a heterocyclic ring contributes to improved stability. In this study, four heterocyclic curcumin analogues, representing some broad groups of heterocyclic curcuminoids (isoxazole-, pyrazole-, N-phenyl pyrazole- and N-amido-pyrazole-based), have been synthesized by a simple one-pot synthesis and have been characterized by FTIR, 1H-NMR, 13C-NMR, DSC and LC–MS. To predict its potential anticancer efficacy, the compounds have been analyzed by computational studies via molecular docking for their regulatory role against three key proteins, namely GSK-3β—of which abnormal regulation and expression is associated with cancer; Bcl-2—an apoptosis regulator; and PR which is a key nuclear receptor involved in breast cancer development. One of the compounds, isoxazole-curcumin, has consistently indicated a better docking score than the other tested compounds as well as curcumin. Apart from docking, the compounds have also been profiled for their ADME properties as well as free energy binding calculations. Further, the in vitro cytotoxic evaluation of the analogues was carried out by SRB assay in breast cancer cell line (MCF7), out of which isoxazole-curcumin (IC50–3.97 µM) has displayed a sevenfold superior activity than curcumin (IC50–21.89 µM). In the collation of results, it can be suggested that isoxazole-curcumin behaves as a potential lead owing to its ability to be involved in a regulatory role with multiple significant cancer proteins and hence deserves further investigations in the development of small molecule-based anti-breast cancer agents. Graphic abstract

In recent times, wireless communication systems have been considered by a fixed spectrum allocation policy, where administrative agencies provide wireless spectrum to licensees on a long-term basis for high geographical areas. Cognitive radio networks (CRN) will provide a large bandwidth to mobile users. However, CRN networks impose challenges due to security issues and spectrum management issues. Hence, in this paper, blockchain based security enhancement and spectrum sensing method is developed for managing the spectrum as well as identify the malicious user in the CRN. In the CRN, spectrum sensing is a fundamental requirement which affected by the malicious user. The malicious user is attacking the general signal detection of network and disturbs the accuracy of the system performance. The occurrence of a malicious user in CRN sends false sensing data which decreases the presentation of the system. Blockchain-based security and spectrum sensing is achieved in the CRN network which empowers the system performance. The blockchain-based method is utilized to identify the malicious user in the CRN by an Adaptive threshold spectrum energy detection algorithm. The proposed method is implemented in MATLAB and performance is evaluated by performance metrics such as the probability of detection, false alarm probability, sensing performance gain, total error probability, missed detection probability, number of selected sensing nodes, average network throughput, and energy efficiency. The proposed method is compared by existing methods such as Friend or Foe and Tidal Trust Algorithm.

With the widespread use of Internet of Things (IoT) in various applications and several security vulnerabilities reported in them, the security requirements have become an integral part of an IoT system. Authentication and access control are the two principal security requirements for ensuring authorized and restricted accesses to limited and essential resources in IoT. The built-in authentication mechanism in IoT devices is not reliable, because several security vulnerabilities are revealed in the firmware implementation of authentication protocols in IoT. On the other hand, the current authentication approaches for IoT that are not firmware are vulnerable to some security attacks prevalent in IoT. Moreover, the recent access control approaches for IoT have limitations in context-awareness, scalability, interoperability, and security. To mitigate these limitations, there is a need for a robust authentication and access control system to safeguard the rapidly growing number of IoT devices. Consequently, in this paper, we propose a new secure unified authentication and access control system for IoT, called SUACC-IoT. The proposed system is based around the notion of capability, where a capability is considered as a token containing the access rights for authorized entities in the network. In the proposed system, the capability token is used to ensure authorized and controlled access to limited resources in IoT. The system uses only lightweight Elliptic Curve Diffie-Hellman Ephemeral (ECDHE), symmetric key encryption/decryption, message authentication code and cryptographic hash primitives. SUACC-IoT is proved to be secure against probabilistic polynomial-time adversaries and various attacks prevalent in IoT. The experimental results demonstrate that the proposed protocol’s maximum CPU usage is 29.35%, maximum memory usage is 2.79% and computational overhead is 744.5 ms which are quite acceptable. Additionally, in SUACC-IoT, a reasonable communication cost of 872 bits is incurred for the longest message exchanged.

Although severe COVID-19 in children is rare, they may develop multisystem inflammatory syndrome, long-COVID and downstream effects of COVID-19, including social isolation and disruption of education. Data on the effectiveness of the CoronaVac vaccine is scarce during the Omicron period. In Brazil, children between 6 to 11 years are eligible to receive the CoronaVac vaccine. We conducted a test-negative design to estimate vaccine effectiveness using 197,958 tests from January 21, 2022, to April 15, 2022, during the Omicron dominant period in Brazil among children aged 6 to 11 years. The estimated vaccine effectiveness for symptomatic infection was 39.8% (95% CI 33.7–45.4) at ≥14 days post-second dose. For hospital admission vaccine effectiveness was 59.2% (95% CI 11.3–84.5) at ≥14 days. Two doses of CoronaVac in children during the Omicron period showed low levels of protection against symptomatic infection, and modest levels against severe illness.

Near-infrared spectroscopy and chemometrics were investigated as a rapid analytical method to evaluate quality parameters of acai oil during thermal oxidation under accelerated conditions. A two-factors full factorial design with 2 levels for the treatment (without antioxidant and with 400 ppm of myricetin) and 8 levels for the oxidation time (giving insight of the initial stage of oil oxidation) was used. Three quality parameters (peroxide, conjugated diene, and p-anisidine values) were monitored by reference analysis. Multi-factorial analysis of variance and estimation of the order of the kinetic models pointed out that both factors and their interaction had a significant effect on the quality parameters (p value 0.83) and ratio of prediction to deviation (> 2.9) that indicated good performance for the prediction of new independent samples. High sensitivity (> 83%) and accuracy (> 95%) of the linear discriminant analysis, obtained when the first derivative was used as pre-treatment, were indicators of a suitable technique for classifying new independent samples. Principal component analysis showed the formation of two clusters corresponding to acai oil samples safe and unfit for commercialization. In conclusion, near-infrared spectroscopy revealed to be a powerful alternative analytical technique for prediction and classification of acai oil samples at industrial scale.

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such an environment. In this paper, we propose a malware detection mechanism in IoT-enabled industrial multimedia environment with the help of machine-learning approach, which is named as MADP-IIME. MADP-IIME uses four different types of machine learning methods (i.e., naive bayes, logistic regression, artificial neural networks (ANN) and random forest) to detect the presence of malware attacks successfully. Furthermore, MADP-IIME performs better than other related existing schemes and achieves $$99.5 \%$$ detection and $$0.5 \%$$ false positive rate. In addition, the conducted security analysis proves the resilience of the proposed MADP-IIME against different types of malware attacks.

Signboard detection and recognition is an important task in automated context-aware marketing. Recently many scripting languages like Latin, Japanese, and Chinese have been effectively detected by several machine learning algorithms. As compared to other languages, outdoor Urdu text needs further attention in detection and recognition due to its cursive nature. Urdu detection and recognition are also difficult due to a wide variety of illuminations, low resolution, inconsistent font styles, color, and backgrounds. To overcome the deficiency of Urdu text detection from the outdoor environment, we have proposed a new Urdu-text signboard dataset with 467 ligature categories, containing a 30 + K images for recognition and 700 base images with annotation are created for detection. We also propose a methodology, that consists of 3-phases. In first phase text regions containing Urdu ligatures from shop-signboard images are detected by a faster regional convolutional neural network (FasterRCNN) using pre-trained CNNs like Alexnet and Vgg16. In the second phase detected regions from the first phase are clustered to identify unique ligatures in a dataset. Lastly in the third phase, all detected regions are recognized by 18-layer convolutional neural network trained model. The proposed system has successfully achieved the precision and recall of 87% and 96% respectively using vgg16 model for detection. For the classification of ligatures, a recognition rate of 97.50% is achieved. Recognition of ligatures was also evaluated using bilingual evaluation understudy (BLEU), and achieved an encouraging score of 0.96 on the newly developed Urdu-Signboard dataset.

Background More doses of CoronaVac have been administered worldwide than any other COVID-19 vaccine. However, the effectiveness of COVID-19 inactivated vaccines in pregnant women is still unknown. We estimated the vaccine effectiveness (VE) of CoronaVac against symptomatic and severe COVID-19 in pregnant women in Brazil. Methods We conducted a test-negative design study in all pregnant women aged 18–49 years with COVID-19-related symptoms in Brazil from March 15, 2021, to October 03, 2021, linking records of negative and positive SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) tests to national vaccination records. We also linked records of test-positive cases with notifications of severe, hospitalised or fatal COVID-19. Using logistic regression, we estimated the adjusted odds ratio and VE against symptomatic COVID-19 and against severe COVID-19 by comparing vaccine status in test-negative subjects to test-positive symptomatic cases and severe cases. Results Of the 19,838 tested pregnant women, 7424 (37.4%) tested positive for COVID-19 and 588 (7.9%) had severe disease. Only 83% of pregnant women who received the first dose of CoronaVac completed the vaccination scheme. A single dose of the CoronaVac vaccine was not effective at preventing symptomatic COVID-19. The effectiveness of two doses of CoronaVac was 41% (95% CI 27.1–52.2) against symptomatic COVID-19 and 85% (95% CI 59.5–94.8) against severe COVID-19. Conclusions A complete regimen of CoronaVac in pregnant women was effective in preventing symptomatic COVID-19 and highly effective against severe illness in a setting that combined high disease burden and marked COVID-19-related maternal deaths.

Museums have adapted their traditional ways of providing services with the advent of novel digital technologies to match up with the pace and growing needs of current industry revolution. Mixed Reality has revitalized interpretation of numerous domains by offering immersive experiences in digital and real world. In the proposed study, an attempt was made to enrich user’s museum experience with relevant multimedia information and for building a better connection with the artifacts with in Taxila Museum in Pakistan, which has beautifully preserved the Gandhara civilization. The proposed solution is an Augmented Reality (AR)-based smartphone application which recognizes artifacts using Deep Learning in real time and retrieve supportive multimedia information for the visitors. To provide user with exact content, convolutional neural networks (CNN) will be applied to correctly recognize artifacts. The significance of proposed application is compared with traditional human guided or free user tours through user-centric questionnaire-based survey. The evaluation is carefully performed using relevant evaluation models including Museum Experience Scale (MES) and triptych model of interactivity. The findings of the study are discussed and assessed comprehensively using statistical methods to highlight its significance.

OBJECTIVE: Multiple implantations of the implant healing abutment (IHA) could adversely impact its surface properties in vivo. Furthermore, the effect of sterilization and reuse of the IHA on soft tissue viability and bacterial contamination has not been extensively studied. The goal of this study was to perform an in vitro analysis of mammalian cell viability and bacterial adhesion on the surfaces of retrieved IHA after single & multiple implantations and repetitive cycles of sterilization. MATERIALS AND METHODS: IHA surface morphology was studied using optical microscopy. Cell viability of gingival fibroblasts (HGF-1) and oral keratinocytes (HOKg) in indirect contact with IHAs was assessed for 3 and 7 days. Immersion in bacterial culture was performed with a polyculture of Streptococcus species for 3 days and Streptococcus species with Fusobacterium nucleatum for 7 days. RESULTS: IHAs exhibited signs of surface damage even after a single exposure to the oral cavity. Fibroblasts did not show a significant preference towards control IHAs over used IHAs, whereas keratinocytes exhibited a significant decrease in viability when exposed to IHAs after multiple implantation cycles as compared to controls. Adherent bacterial count increased with increasing number of IHA implantations for both polycultures. CONCLUSIONS: Reusing of IHAs in vivo promoted surface degradation in addition to adversely impacting host cell viability and oral bacterial attachment in vitro. These findings show IHA reuse might potentially affect its clinical performance. CLINICAL RELEVANCE: Careful consideration should be taken when reusing IHAs in patients because this practice can result in permanent surface changes that might affect soft tissue integration during the healing period and promote bacterial colonization.

Early detection, early diagnosis and classification of the cancer type facilitates faster disease management of patients. Cervical cancer is fourth most pervasive cancer type which affects life of many people worldwide. The intent of this study is to automate cancer diagnosis and classification through deep learning techniques to ensure patients health condition progress timely. For this research, Herlev dataset was utilized which contains 917 benchmarked pap smear cells of cervical with 26 attributes and two target variables for training and testing phase. We have adopted combination of convolutional network with variational autoencoder for data classification. The usage of variational autoencoder reduces the dimensionality of data for further processing with involvement of softmax layer for training. The results have been obtained over 917 cancerous image type pap smear cells, where 70% (642) allocated for training and remaining 30% (275) considered for test data set. The proposed architecture achieved variational accuracy of 99.2% with 2*2 filter size and 99.4% with 3*3 filter size using different epochs. The proposed hybrid variational convolutional autoencoder approach applied first time for cervical cancer diagnosis and performed better than traditional machine learning methods.

With rapid development of voice control technology, making speech recognition more precisely in various IoT domains have been an intractable problem to be solved. Since there are various conversation scenes, understanding the context of a dialog scene is a key issue of voice control systems. However, the reality is available training data for dialog system are always insufficient. In this paper, we mainly solve the problem of data lacking in dialog systems by data augmentation technique. A Generative Adversarial Network(GAN)-based model is proposed and the data are augmented effectively. It can generate from text to text, enhance the original data with text retelling, and improve the robustness of parameter estimation of unknown data by using the sample data generated by GAN model. A new N-gram language model is used to evaluate multiple recognition candidates of speech recognition, and the candidate sentences with the highest evaluation scores are selected as the final result of speech recognition. Our data enhancement algorithm based on the Generative Model is verified by the experiments. In the result of model comparison test, the error rates of data set THCHS30 and AISHELL are 3.3% and 5.1% which are lower than that of the baseline system.

In addition to changing service management, smart devices connect people and objects around them and collect data from them on and on, in order to construct the notion of a smart city. Such data produced by embedded devices and automatically transmitted over the Internet provides people with the information to make decisions. A smart grid is one of the most popular applications for a smart city. Due to the insecurity of the wireless channels, the security of data transmission in a smart grid has become a hot issue nowadays. Many schemes for data protection have been proposed, but weaknesses exist generally. We present a new data transmission scheme for a smart grid among the smart meter (SM), the electricity utility (EU), and the trusted authority (TA). The EU can obtain the power consumption of each SM, but cannot get the real identity of the SM. To keep the privacy of the user, if the consumption value is over the threshold in special time span or identity of SM is required for public affairs, TA could track the identity in time. Formal proof with random oracle model and security analysis are expressed to show the security of the proposed scheme. Via the performance and network simulation, it is easy to see that our scheme is practical for a smart city.

This paper introduces a nature-inspired optimized algorithm called modified moth-flame optimization (MMFO) for usability feature selection. To determine quality of software usability plays a significant role. This model contains various usability factors that are divided into several features, which have some characteristics, thus making a hierarchical model. Here, the authors have introduced MMFO (Modified Moth-flame optimization algorithm) for the selection of usability features to get an optimal solution MMFO is an extension of moth-flame optimization algorithm (MFO), which is based on the navigation method of moths called transverse orientation and to the best of our knowledge; this algorithm is introduced in software engineering practices. The selected features and accuracy of proposed MMFO is compared with the original MFO and other related optimization techniques. The results shows that the proposed nature-inspired optimization algorithm outperforms the other related optimizers as it generates a fewer number of selected features and having low accuracy.

Routing in Opportunistic Internet of Things networks (OppIoTs) is a challenging task because of intermittent connectivity between devices and the lack of a fixed path between the source and destination of messages. Recently, machine learning (ML) and reinforcement learning (RL) have been used with great success to automate processes in a number of different problem domains. In this paper, we seek to fully automate the OppIoT routing process by using the Policy Iteration algorithm to maximize the possibility of message delivery. Moreover, we model the OppIoT environment as a Markov decision process (MDP) replete with states, actions, rewards, and transition probabilities. The proposed routing protocol, RLProph, is able to optimize the routing process via the optimal policy obtained by solving the MDP using Policy Iteration. Through extensive simulations, we show that RLProph outperforms a number of ML-based and context-aware routing protocols on a multitude of performance criteria.

Non-random oviposition preference by target pest species on Bt and non-Bt refuge plants may increase the proportion of the population under selection pressure, reducing the value of the refuge strategy for resistance management. Here, we tested the oviposition preference for Bt over non-Bt plants and the damage-avoiding oviposition behavior of moths of the fall armyworm (FAW), Spodoptera frugiperda, a worldwide pest species with three reported cases of field-evolved resistance to Bt maize. In the greenhouse, choice assays indicated no oviposition preference for Cry1F Bt maize over its non-Bt isoline whatever intact or injured by FAW larvae. In a field experiment in a region of intensive agriculture in Brazil, the number of egg masses and FAW larvae was recorded on seven Bt maize varieties and two non-Bt maize varieties during vegetative and reproductive growth stages. There was only a weak oviposition preference by FAW moths for less damaged plants in the V10 stage. When using Cry1A.105 + Cry2Ab Bt maize and 50% refuge in a field experiment in another region in Southeastern Brazil, the number of egg masses on Bt and non-Bt plants also indicated no oviposition preference by the moths. Consistently, these results indicate that FAW moths do not distinguish Bt and non-Bt plants for oviposition and that conspecific larval damage has minor interference in the oviposition choice. Therefore, there seems to be random egg-laying preference of FAW moths in Bt maize fields, an oviposition behavior typical of generalist moth species and unlikely to be associated with the rapid selection of Bt-toxin-resistant FAWs.

Parkinson’s disease (PD) is a neurological disease that has been reported to have affected most people worldwide. Recent research pointed out that about 90% of PD patients possess voice disorders. Motivated by this fact, many researchers proposed methods based on multiple types of speech data for PD prediction. However, these methods either face the problem of low rate of accuracy or lack generalization. To develop an approach that will be free of these issues, in this paper we propose a novel ensemble approach. These paper contributions are two folds. First, investigating feature selection integration with deep neural network (DNN) and validating its effectiveness by comparing its performance with conventional DNN and other similar integrated systems. Second, development of a novel ensemble model namely EOFSC (Ensemble model with Optimal Features and Sample Dependant Base Classifiers) that exploits the findings of recently published studies. Recent research pointed out that for different types of voice data, different optimal models are obtained which are sensitive to different types of samples and subsets of features. In this paper, we further consolidate the findings by utilizing the proposed integrated system and propose the development of EOFSC. For multiple types of vowel phonations, multiple base classifiers are obtained which are sensitive to different subsets of features. These features and sample-dependent base classifiers are integrated, and the proposed EOFSC model is constructed. To evaluate the final prediction of the EOFSC model, the majority voting methodology is adopted. Experimental results point out that feature selection integration with neural networks improves the performance of conventional neural networks. Additionally, feature selection integration with DNN outperforms feature selection integration with conventional machine learning models. Finally, the newly developed ensemble model is observed to improve PD detection accuracy by 6.5%.

Neuronal excitation enables identifying the features of an electroencephalogram (EEG) signal for motor imagery detection. We propose a novel feature extraction algorithm supported by short-term cepstrum-based inverse filtration of neuronal excitation of the active synapse. The maximum power of the estimated neuronal excitation is subjected to a two-class Bayesian probabilistic classifier. The feature extraction algorithm with the Bayesian probabilistic classifier significantly improves the brain–computer interface performance compared with that of other conventional methods of EEG signal processing such as wavelet with a Bayesian classifier, autocorrelation and CSP filter with a naive Bayes classifier over the BCI competition II and IV datasets. Consequently, this neuronal excitation feature allows the authors to develop a motor imagery neuro-feedback system; the performance of which achieves 87.2% average classification accuracy, which is 14% greater than that of the wavelet-based algorithm and 6.2% greater than that of the TRSP-based algorithm, with 53 ms of processing time allotted for each instruction in a real-time experiment. However, brain signal variation across different subjects and sessions significantly impairs decision accuracy. Our neuronal excitation base feature extraction algorithm minimizes these variations in classification accuracy.

The aim of this study was to investigate the effect of altering design variables like cross-linking and freezing temperature (at a time) on morphology of freeze-dried chitosan scaffolds and modulation of release of Diclofenac sodium (model drug). Freeze-dried chitosan scaffolds produced at − 80 °C, cross-linked with genipin, showed swelling of 163.52 ± 9.95% with sustained drug release of 26.37 ± 10.47% over 24 h (P

Video captioning is currently considered to be one of the simplest ways to index and search data efficiently. In today’s era, suitable captioning of video images can be facilitated with deep learning architectures. The focus of past research has been on providing image captions; however, the generation of high-quality captions with suitable semantics for different scenes has not yet been achieved. Therefore, this work aims to generate well-defined and meaningful captions to images and videos by using convolutional neural networks (CNN) and recurrent neural networks in combination. Beginning with the available dataset, features of images and videos were extracted using CNN. The extracted feature vectors were then utilized to generate a language model with the involvement of long short-term memory for individual word grams. The generated meaningful captions were trained using a softmax function, for performance computation using some predefined evaluation metrics. The obtained experimental results demonstrate that the proposed model outperforms existing benchmark models.

Among the numerous alternatives used in the world of risk balance, it highlights the provision of guarantees in the formalization of credit agreements. The objective of this paper is to compare the achievement of fuzzy sets with that of artificial neural network-based decision trees on credit scoring to predict the recovered value using a sample of 1890 borrowers. Comparing with fuzzy logic, the decision analytic approach can more easily present the outcomes of the analysis. On the other hand, fuzzy logic makes some implicit assumptions that may make it even harder for credit-grantors to follow the logical decision-making process. This paper leads an initial study of collateral as a variable in the calculation of the credit scoring. The study concludes that the two models make modelling of uncertainty in the credit scoring process possible. Although more difficult to implement, fuzzy logic is more accurate for modelling the uncertainty. However, the decision tree model is more favourable to the presentation of the problem.

A recent trend of peering at geo-diversified Internet exchange points (IXPs) has empowered decades-old proposal of inter-networking and opened up new avenues of business ventures. IP-transit, cloud direct and remote peering are a few important amongst numerous proposals of service provisioning capitalizing on this peering infrastructure support across domains. Enduring these business proposals becomes a challenging task, especially when the increased dependency of enterprises over the Internet is affirmed. Volatile traffic priorities necessitate different strategies of flow management for each pattern of enterprise traffic. Providing diverse service guarantees to each traffic class require careful selection of resource allocation and compliance of inter-domain policies. In this paper, we propose a novel orchestration framework that helps to stitch end-to-end traffic engineering compliant multiple paths. The framework enables prioritized management of various traffic classes in a centralized manner by employing software defined networking paradigm. Abstraction of multi-graph from the inter-connectivity of peering anchors helps to gear service provisioning spanning across multiple domains. Beside presenting details of our framework, we have articulated use cases highlighting the efficacy of our proposal. We have observed a maximum increase of 26.52% in throughput using proposed model compared with an optimization formulation from literature. Our results imply transparent utility of this formulation for various network topologies and traffic loads.

We carried out synthesis of shape-controlled ZnO nanoparticles following a polyol route using either ethylene glycol (EG) or polyethylene glycol (PEG) as solvent, which exhibited wurtzite structures as identified by XRD patterns. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) analyses of the synthesized structures showed that the size and the shape are strongly dependent on the reaction medium, resulting in nanospheres, rods, hexagonal plates or sheets, which were characterized by different spectroscopy techniques such as: Raman scattering, x-ray photoelectron spectroscopy (XPS), UV–Vis and photoluminescence (PL). The Raman analysis showed that the resulting surface is passivated with acetate molecules and also monitored the presence of superficial defects, whose spectroscopic patterns (Raman spectroscopy) indicated that the passivation with acetate molecules reduces the number of defects, such as oxygen vacancies. This result was confirmed by XPS analyses that identified chemisorbed oxygen species onto the oxide surface and an oxygen-deficient component in the sample prepared as reference, without a passivation with EG or PEG. Photoluminescence results showed that the passivation, size and shape of the particles influenced the optical features, mainly at the emission at the green region of spectrum that has been related with surface defects. This green emission is favoured at the ZnO sample prepared without passivation and with large amount of defects. Current–voltage characteristic (J–V) of an inverted organic solar cell showed the potential application of these ZnO nanostructures as electron transport material in organic photovoltaic devices.

The incomplete anthocyanin extraction during the industrial processes turns grape pomace into an inexpensive source of phenolic compounds. The effects of temperature and enzyme preparation percentage (% E/S) on the anthocyanin recovery from grape pomaces of eight grape varieties were evaluated by their physicochemical characteristics and phenolic composition. A factorial 22 design with center point was used to select the preferred conditions for extraction, and the variables of temperature, enzyme preparation, and their interaction were assessed. The grape skin characteristics affected the anthocyanins’ content and their recovery yield, and different improvement conditions were found for each variety of grape. Anthocyanin extraction from Cabernet Sauvignon—the variety which showed the highest percentage of anthocyanin recovery (over 50%)—was improved. The lowest tested temperature (40 °C) and percentage of preparation enzymatic (0.25% E/S) promoted higher anthocyanin extraction, resulting in a natural food colorant with 2.67 g anthocyanins/100 g grape skin dry basis (db). The extraction improvement allowed for a non-toxic natural extract rich in anthocyanins to be obtained; a natural additive which can be considered for potential food industry use in place of synthetic dyes, especially into acidic matrices.

Even in specialized centers, suboptimal aneurysm clipping can be as high as 12%. Intraoperative fluorescence angiography with indocyanine green and, more recently, fluorescein sodium have been shown to be a good method for intraoperative flow assessment. However, the cost with the apparatus it entails limits its widespread use. We have developed a low-cost universal fluorescence module (FM) designed to visualize fluorescein and perform intraoperative angiography. The purpose of this paper is to describe this device as well as to present our early experience with its use in the treatment of cerebral aneurysms. A FM was designed and built using a cyan-blue narrow bandpass (460 to 490 nm) excitation filter and a yellow-orange longpass (blocking wavelengths under 520 nm) barrier filter mounted on a 3D-printed holding tray in a specific disposition to perfectly match the light source and the objective lens of the surgical microscope. It allowed switching from white light to fluorescence mode in a simple and sterile fashion. Its perfect attachment to the microscope was possible by reusing the lens fittings extracted from used original drape sets that would otherwise be discarded. Four patients underwent aneurysm clipping using the FM at two institutions from April to September 2018. A bright green fluorescence against a dark background was observed after intravenous bolus of fluorescein. Blood vessels became obviously distinct from non-contrast-filled structures such as clipped aneurysms and the brain. Vascular anatomy could be appreciated without any distortion, including perforating arteries. Intraoperative fluorescence angiography was successfully performed with the use of this universal FM after intravenous injection of fluorescein sodium. This simple and low-cost device may be useful in resource-limited centers, where other sorts of intraoperative angiography are not available.

Heterozygous loss-of-function mutations in SHANK2 are associated with autism spectrum disorder (ASD). We generated cortical neurons from induced pluripotent stem cells (iPSC) derived from neurotypic and ASD-affected donors. We developed Sparse coculture for Connectivity (SparCon) assays where SHANK2 and control neurons were differentially labeled and sparsely seeded together on a lawn of unlabeled control neurons. We observed increases in dendrite length, dendrite complexity, synapse number, and frequency of spontaneous excitatory postsynaptic currents. These findings were phenocopied in gene-edited homozygous SHANK2 knockout cells and rescued by gene correction of an ASD SHANK2 mutation. Dendrite length increases were exacerbated by IGF1, TG003, or BDNF, and suppressed by DHPG treatment. The transcriptome in isogenic SHANK2 neurons was perturbed in synapse, plasticity, and neuronal morphogenesis gene sets and ASD gene modules, and activity-dependent dendrite extension was impaired. Our findings provide evidence for hyperconnectivity and altered transcriptome in SHANK2 neurons derived from ASD subjects.

According to the World Health Organization (WHO), a significant reduction in mortality and maternal morbidity has occurred in developed countries over the past decades. In contrast, these rates remain high in developing countries. Smart mobile-health (m-health) applications that use machine learning (ML) approaches are necessary tools for pregnancy monitoring in an accessible, reliable, and cost-efficient manner, making the prediction of high-risk situations possible during gestation. This paper, therefore, proposes the development, performance evaluation, and comparison of ML algorithms based on Bayesian networks capable of identifying at-risk pregnancies based on the symptoms and risk factors presented by the patients. A performance comparison of several Bayes-based ML algorithms determined the best-suited algorithm for the prediction, identification, and accompaniment of hypertensive disorders during pregnancy. The contribution of this study focuses on finding a smart classifier for the development of novel mobile devices, which presents reliable results in the identification of problems related to pregnancy. Through the well-known cross-validation method, this proposal is evaluated and compared with other recent approaches. The averaged one-dependence estimators presented better results on average than the other approaches. These findings are key to improving the health monitoring of women suffering from high-risk pregnancies around the world. Thus, this study can contribute to a reduction of both maternal and fetal deaths.

Orthopedic devices are often associated with increased risk for diabetic patients due to impaired wound healing capabilities. Adverse biological responses for immunocompromised patients at the implant-tissue interface can lead to significant bone resorption that may increase failure rates. The goal of this study was to characterize the surface of implants removed from diabetic patients to determine underlying mechanisms of diabetes-induced impaired osseointegration. Thirty-nine retrieved titanium and stainless-steel orthopedic devices were obtained from diabetic and non-diabetic patients, and compared to non-implanted controls. Optical Microscopy, Scanning Electron Microscopy, Energy Dispersive X-ray Spectroscopy, and X-ray Photoelectron Spectroscopy revealed changes in morphology, chemical composition, oxidation state, and oxide thickness of the retrieval specimens, respectively. Additionally, titanium disks were immersed for 28 days in simulated in vitro diabetic conditions followed by Inductively Coupled Plasma-Optical Emission Spectroscopy to quantify metal dissolution. Electrochemical testing was performed on specimens from retrievals and in vitro study. Aside from biological deposits, retrievals demonstrated surface discoloration, pit-like formations and oxide thinning when compared to non-implanted controls, suggesting exposure to unfavorable acidic conditions. Cyclic load bearing areas on fracture-fixation screws and plates depicted cracking and delamination. The corrosion behavior was not significantly different between diabetic and non-diabetic conditions of immersed disks or implant type. However, simulated diabetic conditions elevated aluminum release. This elucidates orthopedic implant failures that potentially arise from diabetic environments at the implant-tissue interface. Design of new implant surfaces should consider specific strategies to induce constructive healing responses in immunocompromised patients while also mitigating corrosion in acidic diabetic environments.

The concept 'the retina as a window to the brain' has been increasingly explored in Alzheimer´s disease (AD) in recent years, since some patients present visual alterations before the first symptoms of dementia. The retina is an extension of the brain and can be assessed by noninvasive methods. However, assessing the retina for AD diagnosis is still a matter of debate. Using the triple transgenic mouse model of AD (3xTg-AD; males), this study was undertaken to investigate whether the retina and brain (hippocampus and cortex) undergo similar molecular and cellular changes during the early stages (4 and 8 months) of the pathology, and if the retina can anticipate the alterations occurring in the brain. We assessed amyloid-beta (Aβ) and hyperphosphorylated tau (p-tau) levels, barrier integrity, cell death, neurotransmitter levels, and glial changes. Overall, the retina, hippocampus, and cortex of 3xTg-AD are not significantly affected at these early stages. However, we detected a few differential changes in the retina and brain regions, and particularly a different profile in microglia branching in the retina and hippocampus, only at 4 months, where the number and length of the processes decreased in the retina and increased in the hippocampus. In summary, at the early stages of pathology, the retina, hippocampus, and cortex are not significantly affected but already present some molecular and cellular alterations. The retina did not mirror the changes detected in the brain, and these observations should be taking into account when using the retina as a potential diagnostic tool for AD.

Detoxification with citric acid is an effective method of biofilm removal from titanium (Ti) dental implant surfaces affected by peri-implantitis, but its effects on Ti surface properties is not well understood. This study aimed at evaluating the surface characteristics and biological response to Ti after bacterial biofilm formation followed by citric acid detoxification. Ti specimens were immersed with oral bacteria under aerobic (Streptococcus mutans, Streptococcus sanguinis, Streptococcus salivarius and Aggregatibacter actinomycetemcomitans) and anaerobic (aerobic polyculture with Fusobacterium nucleatum and Porphyromonas gingivalis) conditions for 4 h or 7 days (n = 3). Immersion was followed by rubbing treatment with 0.9% saline or 40% citric acid for 8 min. Post-treatment, the surface morphology and microstructure were studied by optical microscopy and Raman spectroscopy, respectively. Osteoblast viability after 3 days on Ti post-treatment was assessed. Electrochemical testing revealed corrosion behavior post-treatment while X-ray photoelectron spectroscopy indicated oxide layer state. While signs of pitting and corrosion attack on Ti exposed to bacteria and/or detoxification were evident, no surface oxide phase changes were detected. Samples treated with citric acid had lower polarization resistance and higher corrosion rate after aerobic and anaerobic immersion. Samples exposed to bacteria and citric acid treatment had higher oxide thickness under aerobic but not anaerobic immersion after 4 h and 7 days. Osteoblast viability was not significantly affected by immersion and treatment. Within the study’s limitations, citric acid detoxification on Ti post-bacterial exposure is not expected to adversely change oxide composition, thickness, and corrosion behavior while maintaining host cell growth.