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Sudoku is a very popular mathematical and logical curiosity with abundant variations. Many artificial intelligence (AI) programs that imitate human expertise have been developed to solve sudoku puzzles. However, most popular methods, such as Recurrent Relational Networks and Double Q-learning, are primarily reinforcement learning (RL) algorithms. These algorithms are rather complex since they consist of two or more fundamental models for achieving high accuracy. To circumvent this drawback, this paper proposes a simple Multi-Layer Perceptron (MLP) model that not only produces correct results but is easy enough for implementation as well. Since the MLP model is a supervised learning method and is widely used in classification problems, this paper first discusses how the training data is generated. It then demonstrates a coding method that can be used to convert 4 × 4 sudoku puzzles to classification problems. Afterward, it shows the MLP settings and how the predictions from the model can be transformed into legal solutions. This model has been thoroughly tested on sudoku puzzles with various levels of difficulty, and the resulting data illustrate that its accuracy is no worse than those reinforcement learning methods and other non-AI algorithms. Moreover, with slight modification, the MLP model can be utilized on larger scales, such as 9 × 9 sudoku puzzles, or even harder ones.

Multi-level AC-AC converter has the advantages of reducing switch voltage stress, adapting to high voltage input and reducing waveform distortion. In this paper, a symmetrical switched capacitor multi-level (SSCML) AC-AC converter is studied. The switched capacitor (SC) technology is introduced into the AC-AC converter, which has the advantages of small size, high efficiency and high power density. Firstly, the topology of SSCML AC-AC converter and its control strategy are introduced. Then, the working principle of the topology is analyzed, and the relationship between input voltage and output voltage is derived in detail, as well as the expression of switch voltage stress. Finally, a 300W prototype is built to verify the proportional relationship between input voltage and output voltage. The prototype and experimental results prove that the multi-level AC-AC converter using SC technology has the advantages of small size and low voltage stress. The experimental results also prove that the voltage stress is proportional to the modulation ratio, which is the key to reducing the voltage stress.

Analysing the traversability from the point cloud input is a critical task for mobile robot navigation. However, there is no mature method for mobile robots to exploring complicated 3D environments, especially those with multi-layered roads. This paper proposes a new traversability map based on the point cloud input, and the map will help analyze where the mobile robot can move in the whole space. The proposed 3D traversability map consists of nodes and edges generated according to the mobile robot’s geometry and driving performance. Nodes represent where the robot can be placed, and edges indicate the cost of moving to another node. The traversability map is stored in digraph form and is convenient to be applied to path planning. Experiments show the successful results of traversability map generation and path planning with D algorithm in different situations.

IoT contains a large amount of data as well as privacy sensitive information. This information can be easily identified by the various cyber-attackers. Attackers may steel the data or they can introduce viruses and other malicious software to damage the network. So to overcome these issues, we need to establish an efficient security mechanism for data protection in IoT systems. Advanced Encryption standard is one of the popular security protocols for IoT systems. The main disadvantage of this standard is power consumption for large sized IoT networks. In this paper we proposed an novel mechanism for secure communication in large sized IoT systems. In our design, we use a Hash based Message Authentication Code (HMAC) and a privacy-preserving communication protocol for chaos-based encryption. We observe that proposed mechanism consumes relatively less power over AES with less storage resources.

Purpose The purpose of this study is to examine the state of research into knowledge management within local government in developing countries, to identify themes that have been studied and to identify important gaps in the literature that can inform a research agenda going forward. Design/methodology/approach A structured literature review method was used in this study. The authors reviewed academic literature published between 2009 and 2019. A total of 15 articles were identified and reviewed. Findings The findings reveal that, few authors have contributed to knowledge management within local government in developing countries and there is little international collaboration between authors. The authors found limited methodological and theoretical diversity and rigor in the reviewed work. The review identified themes that have been researched but evidenced a lack of guidelines and frameworks developed to promote knowledge management initiatives within local governments in developing countries. Overall, it was evident that this area was still little explored. As a result, there are many possibilities for future research. Originality/value This study provides a comprehensive systematic literature review of papers published on knowledge management in local governments in developing countries, identifying themes that have been studied and gaps for further research.

Background This paper attempts to evaluate the primary health care system by evaluating health centres in Greece. Methods Malmquist Index Data Envelopment Analysis is applied to study the total productivity of 155 health centres in Greece during 2016–2018. The data were collected from the Ministry of Health and submitted to quality tests to ensure validity and avoid bias. Results This paper measures the productivity of each of the 155 health centres in Greece and how it shifted during 2016–2018. In addition, the overall productivity change of the 155 health centres over time is calculated and analysed as being due to technical efficiency or technological efficiency. The analysis of the mean values showed a decrease of 0.9% in the overall productivity factor from 2016 to 2017 and a decrease of 5.2% from 2017 to 2018. The overall decrease in the productivity of the 155 health centres was 3.1%. From 2016 to 2018, 59 health centres changed their productivity mainly due to technological change and 91 changed mainly due to technical efficiency change. One health centre showed regression to its total productivity due to equal regression of its technical efficiency and technology. Conclusions The method used is nonparametric data envelopment analysis along with the Malmquist index to include panel data in the analysis. Meaningful results were extracted by indicating the number of health centres that improved their productivity, regressed in productivity, or remained constant through the period 2016–2018. This paper may contribute to improving health centres’ efficiency and productivity. Furthermore, valuable results can be extracted for the National Health Care System to match available resources that correspond to each health centre’s needs, as well as for manager planners and stakeholders in primary health care.

Low power VLSI circuit design faces the challenging issues of static power dissipation as transistor count doubles for every couple of years. static power dissipation also known as leakage power dissipation which increases in scaled down threshold voltage circuits. Downsizing of CMOS innovation improved the speed and simultaneously leakage currents are left over as struggle. This unconstrained leakage current ought to be decreased for untroubled working of the circuit. This paper proposed novel static power reduction strategy with the literature survey. The research study mainly concentrated on circuit performance parameters like speed, power and power delay product of the specific circuit. This work presents logic gates designed and evaluated by mentor graphics tools with 22 nm CMOS Technology.

Deep neural network (DNN) has become increasingly popular in industrial IoT scenarios. Due to high demands on computational capability, it is hard for DNN-based applications to directly run on intelligent end devices with limited resources. Computation offloading technology offers a feasible solution by offloading some computation-intensive tasks to the cloud or edges. Supporting such capability is not easy due to two aspects: (1) Adaptability: offloading should be dynamically occur among computation nodes. (2) Effectiveness: it needs to be determined which parts are worth offloading. This paper proposed a novel approach, called DNNOff. For a given DNN-based application, DNNOff first rewrites the source code to implement a special program structure supporting on-demand offloading, and at runtime, automatically determines the offloading scheme. We evaluated DNNOff on a real-world intelligent application, with three DNN models. Our results show that, compared with other approaches, DNNOff saves response time by 12.4%-66.6% on average.

Deep neural networks (DNNs) technique has achieved impressive performance on semantic segmentation,while its training process requires a large amount of pixel-wise labelled data. Domain adaptation,as a promising solution,can break the restriction by training the model on synthetic data,and generalising it in real-world data. However,there is still a lack of attention paid to the imbalance problems on semantic segmentation adaptation,including the imbalance problem between i) source and target data,ii) different classes. To solve these problems,a progressive hierarchical feature alignment method is proposed in this paper. To alleviate the data imbalance problem,the network is progressively trained by the data from multisource domains,so as to obtain domain-invariant features. To address the class imbalance problem,the features are aligned hierarchically across domains. According to the experimental results,our method shows the competitive adapted segmentation performance on three benchmark datasets.

Objective: To propose a deep-learning network for the diagnosis of two corneal diseases: Fuchs’ endothlelial dystrophy and keratoconus, based on optical coherence tomography (OCT) images of the cornea. Methods: In this paper, we propose a novel network with parallel resolution-specific encoders and composite classification features to directly diagnose Fuchs’ endothelial dystrophy and keratoconus using OCT images. Our proposed network consists of a multi-resolution input, multiple parallel encoders, and a composite of convolutional and dense features for classification. The purpose of using parallel resolution-specific encoders is to perform multi-resolution feature fusion. Also, using composite classification features enhances the dense feature learning. We implemented other related networks for comparison with our network and performed k-fold cross-validation on a dataset of 16,721 OCT images. We used saliency maps and sensitivity analysis to visualize our proposed network. Results: The proposed network outperformed other networks with an image classification accuracy of 0.91 and a scan classification accuracy of 0.94. The visualizations show that our network learned better features than other networks. Significance: The proposed methods can potentially be a step towards the early diagnosis of corneal diseases, which is necessary to prevent their progression, hence, prevent loss of vision.

The future for science gateways warrants exploration as we consider the possibilities that extend well beyond ’science’ and high-performance computing into new interfaces, applications and user communities. In this paper, we look retrospectively at the successes of representative gateways thus far. This serves to highlight existing gaps gateways need to overcome in areas such as accessibility, usability and interoperability, and in the need for broader outreach by drawing insights from technology adoption research. We explore two particularly promising opportunities for gateways - computational social sciences and virtual reality – and make the case for the gateway community to be more intentional in engaging with users to encourage adoption and implementation, especially in the area of educational usage. We conclude with a call for focused attention on legal hurdles in order to realize the full future potential of science gateways. This paper serves as a roadmap for a vision of science gateways in the next ten years.

This paper seeks to examine the concept of “Merdeka Belajar” or “Freedom of Learning” which was initiated by the Indonesian Minister of Education and Culture, Nadiem Makarim. The study of freedom of learning uses text study with content analysis method. The concept of “Freedom of Learning” is very interesting when studied with a multicultural education perspective. Moreover, the two concepts have the same relevance and spirit, namely freedom and justice in the human resources development context, particularly through the educational process. Based on the results, the implementation of “MerdekaBelajar” program so far includes four aspects, namely financing, teachers / educators, assessment, and graduates. In a multicultural perspective, problems in management and assessment aspects basically caused by a misunderstanding of the meaning of standards and standardization. Meanwhile, the teacher and graduate aspects lie in the importance of developing multicultural competencies.Integrating a multicultural education perspective in the “Freedom of Learning” policy also transforms an education system that is creative, innovative, and character.

Ground-moving objects in synthetic aperture radar (SAR) images appear defocused and azimuthally displaced using conventional SAR image formation algorithms. In this paper, a novel regression method based on convolutional neural networks (CNNs) for the estimation of radial velocity and slant range components of ground moving targets is proposed. Motion parameters estimation can be helpful for designing additional matched filters to focus and relocate moving targets. We have generated the training and the test data in such a way that each image is indeed a 2D data matrix of a moving target. In other words, each complex image contains the range-compressed signal of only one moving target with a specified pair of (range, radial velocity). To further decrease the estimation error, we employed transfer learning by fine-tuning the pretrained AlexNet architecture in a regression problem. To verify the effectiveness of the proposed method, simulations have been performed. The results demonstrate the effectiveness of the proposed method.

Next Point-of-interest (POI) recommendation has been recognized as an important technique in location-based services, and existing methods aim to utilize sequential models to return meaningful recommendation results. But these models fail to fully consider the phenomenon of user interest drift, i.e. a user tends to have different preferences when she is in out-of-town areas, resulting in sub-optimal results accordingly. To achieve more accurate next POI recommendation for out-of-town users, an adaptive attentional deep neural model HOPE is proposed in this paper for modeling user’s out-of-town dynamic preferences precisely. Aside from hometown preferences of a user, it captures the long and short-term preferences of the user in out-of-town areas using “Asymmetric-SVD” and “TC-SeqRec” respectively. In addition, toward the data sparsity problem of out-of-town preference modeling, a region-based pattern discovery method is further adopted to capture all visitor’s crowd preferences of this area, enabling out-of-town preferences of cold start users to be captured reasonably. In addition, we adaptively fuse all above factors according to the contextual information by adaptive attention, which incorporates temporal gating to balance the importance of the long-term and short-term preferences in a reasonable and explainable way. At last, we evaluate the HOPE with baseline sequential models for POI recommendation on two real datasets, and the results demonstrate that our proposed solution outperforms the state-of-art models significantly.

Due to the expansion and modernization of the human population, the construction area has gradually developed. As innovations develop, a large portion of the fiber waste is thermosetting plastics but this thermosetting plastic causes hazards. Subsequently affordable, a vital step forward is to further improve waste management and recycling without damaging the earth. In this paper, Silkworm Cocoon Waste Fibers (SCWF) are introduced in the concrete mixtures, which increase robustness and control the utilization of common sand. The addition of distinctive fibers to concrete would decrease shrinkage cracks and construction costs. The experimental test results of the concrete compressive strength reinforced by SCWF volume portions from 0 to 2%. The use of SCWF in ordinary concrete will enhance concrete’s mechanical and toughness characteristics and increase the dual benefit of minimizing the risks to the earth. Similarly, to predict the structural behavior of SCWF fortified concrete, simulation modeling, i.e., the Fuzzy Neural Network (FNN), is proposed. The Enhanced Monarch Butterfly Algorithm (EMBA) is used to boost the FNN algorithm’s efficiency. The test results showed an exceptional increase in the mechanical properties of SWCF-containing concrete compared to traditional concrete.

Background The COVID-19 pandemic has amplified the role of telehealth in health care delivery. Regional variation in internet access and telehealth use are well-documented, but the effect of neighborhood factors, including the pervasiveness of broadband internet, on older adults’ telehealth usage in the context of internet access is not known. Objective This study aimed to investigate how individual and neighborhood characteristics, including the pervasiveness of neighborhood broadband internet subscription, are associated with engagement in telehealth among older adults with internet access. Methods In this cross-sectional study, we included 5117 community-living older adults aged ≥65 years, who participated in the 2017 National Health and Aging Trends Study with census tract–level data for participants’ places of residence from the American Community Survey. Results Of an estimated 35.3 million community-living older adults, 21.1 million (59.7%) were internet users, and of this group, more than one-third (35.8%) engaged in telehealth. In a multivariable regression model adjusted for individual- and neighborhood-level factors, age, education, income, and the pervasiveness of neighborhood broadband internet subscription were associated with engagement in telehealth, while race, health, county metropolitan status, and neighborhood social deprivation were not. Among internet users, living in a neighborhood at the lowest (versus highest) tertile of broadband internet subscription was associated with being 40% less likely to engage in telehealth (adjusted odds ratio 0.61, 95% CI 0.42-0.87), all else equal. Conclusions Neighborhood broadband internet stands out as a mutable characteristic that is consequential to engagement in telehealth.

Key summary points Aim To investigate the association between psychotropic drug use and handgrip strength in older hospitalized patients. Findings Psychotropic drug use was linearly associated with handgrip strength, with the greatest reduction in handgrip strength between zero and two psychotropic drugs. Message Psychotropic drug use should be kept as low as possible in treatment of older patients. Supplementary Information The online version contains supplementary material available at 10.1007/s41999-021-00511-6., Purpose Handgrip strength is an indicator of frailty and longevity in older adults. The association between psychotropic drug use and handgrip strength in older hospitalized patients was investigated in this study. Methods A total of 488 patients were included in this retrospective cross-sectional study, 333 women and 155 men, from two different cohorts of older (mean age 84 years) hospitalized in-patients. We used multiple linear regression models to explore the association between psychotropic drug use and handgrip strength. We adjusted for factors known to affect handgrip strength: Age, gender, body mass index (BMI) and comorbidity (Charlson comorbidity index). Results Both unadjusted and adjusted analyses showed that psychotropic drug use was associated with handgrip strength (β = − 0.183, p

In this paper, I show that a common way that platforms display firms' quality ratings incentivize firms to strategically take costly short-run actions that improve their ratings. Most review platforms display star ratings of goods and services rounded to a half star, rather than display the exact average rating. Since the true average rating is not shown, firms have an incentive to remain just above the rounding threshold in order to have a higher displayed rating. However, once a firm's rating passes the rounding threshold, the incentive to improve the ratings drops as their rating moves farther from the threshold. I study this phenomena in the context of auto repair. I find that there is an excessive amount of bunching around ratings thresholds. The firms' actions toward improving their ratings are typically unobserved, but due to my novel data and the discontinuity of displayed ratings, I can model and infer firm behavior. Specifically, I provide evidence that firms change the services they provide and exert extra effort when they are close to rounding thresholds. Finally, I provide a theoretical framework in order to quantify the actions and provide optimal policies for firm actions depending on their rating and number of reviews in a variety of counterfactual settings.

In this paper, we explore an extension of the Item Response Theory (IRT) model to predict student responses using dichotomous data and formulate approaches to improve the predictive accuracy of the traditional algorithm. We present a simple extension to the IRT modelling approach called IRT++, which combines both the 1-parameter and 2-parameter IRT models and modulates parameter optimisation through simple machine learning techniques like adaptive gradient descent and random-normal initialisation of parameters. By experimentation on real-world education data, we show how the IRT++ modelling framework other baselines at predicting student responses, and achieves better performance while sacrificing very little in model interpretability and rate of convergence.

Introduction/Aims Mean retirement age for UK doctors is 59.6 years giving the average OMFS consultant approximately 20 years of practice. Current pension tax regulations, new consultant posts having 10 sessions (40 hours), increasing proportions of consultants working less than full time (LTFT) all combined with the backlog of elective care created by COVID-19 will create a significant gap between workforce capacity and clinical demand. Materials and Methods The age of current OMFS consultants was estimated using the date of their primary medical/dental qualification. Changes in job plans were estimated using data from the BAOMS Workforce Census and from recently advertised posts. Reports of unfilled posts were collated by OMFS Regional Specialty Professional Advisors (RSPAs). Results First degree dates were identified for 476 OMFS substantive consultant posts. Estimated current average age was 52.7 years (minimum 35.9, maximum 72.1), 75th centile age 59.0 with 23% of the current consultant workforce above the average retirement age for doctors. New consultant posts consisted of 10 sessions (40 hours), whereas existing consultants average 12.1 sessions (48.4 hours). Unfilled consultant posts in Great Britain are 13 % of total compared to 20% in Northern Ireland and Ireland. Conclusions Many (23%) of the OMFS consultant workforce are above average retirement age. Forty-hour contracts; new consultants working LTFT; and early loss of senior colleagues because of pension pressure will reduce NHS’ capacity to treat OMFS disorders and injuries. This paper suggests increasing consultant posts, increasing trainee numbers and actively retaining senior surgeons to maintain capacity.