Reviewerly/exHarmony huggingface dataset:
Ten Years Trend Analysis of Malaria Prevalence in Ziway Dugda District, Arsi Zone, Ethiopia

Background: Malaria has been one of the major public health problems in Ethiopia for decades.Recent reports from different part of the country showed that the trend of malaria is declining.However, there is a need for periodic assessing the trend of malaria in different malaria endemic areas of the country as part of the planed malaria elimination strategies.Methods: Ten years retrospective data of blood film examination was collected from the laboratory registration book and monthly report of Ogolcho health center and analyzed to assess the trend of malaria prevalence in Ziway dugda distric, one of the malaria endemic areas in Arsi zone, Ethiopia.Result: From the total of 38,094 malaria suspected patients, 4,863 (12.8%) patients were malaria positive microscopically, with 3,301 (67.9%)P. vivax, 1545 (31.8%)P. falciparum and 17 (0.35%) mixed infection.There was a fluctuating trend of malaria within the last ten years, with annual total cases of malaria ranged from 1685 in 2013 to 103 in 2020 and there was successive reduction in malaria prevalence from 2013 onwards.Conclusion: Trend of malaria was declining in Ziway dugda district with shift in dominancy of the circulating species (P.vivax).
What is behind the gender gap in economics distance education: Age, work-life balance and COVID-19

There is an ongoing debate about whether gender equality in education has been achieved or not. Research efforts have focused on primary and secondary education, while there are fewer studies on higher education, and few studies refer to distance education. To contribute to address this gap, this article presents a gender analysis of educational outcomes in economics at Spain's leading distance university, UNED, which is also the largest university in the European Union in terms of enrolment. The aim of the article is to assess whether there is a gender gap in academic results and to identify the sociodemographic and academic variables that may be causing such a gap by analysing how they shape such differences. Finally, the impact of COVID-19 is also considered. The results confirm that women underperformed significantly in our sample in terms of passing and scoring, especially among those between 30 and 45 years of age, who are more likely to have young children. When considering a distribution of family tasks biased against women, along with the higher average age of distance learning university students, gender gaps could probably be greater in nonface-to-face education. COVID-19 narrowed the gender gap during the lockdown period, as some men and women staying at home together were able to improve task sharing capabilities. After the lockdown, however, women's results worsened compared to pre-COVID-19 levels. A possible explanation is that they had to continue performing the same family duties in addition to substituting education and caring services (e.g., nurseries and day centres for the elderly) that did not resume activity immediately or continuously.
Heuristic Method for Minimizing Model Size of CNN by Combining Multiple Pruning Techniques

Network pruning techniques have been widely used for compressing computational and memory intensive deep learning models through removing redundant components of the model. According to the pruning granularity, network pruning can be categorized into structured and unstructured methods. The structured pruning removes the large components in a model such as channels or layers, which might reduce the accuracy. The unstructured pruning directly removes mainly the parameters in a model as well as the redundant channels or layers, which might result in an inadequate pruning. To address the limitations of the pruning methods, this paper proposes a heuristic method for minimizing model size. This paper implements an algorithm to combine both the structured and the unstructured pruning methods while maintaining the target accuracy that is configured by its application. We use network slimming for the structured pruning method and deep compression for the unstructured one. Our method achieves a higher compression ratio than the case when the individual pruning method is applied. To show the effectiveness of our proposed method, this paper evaluates our proposed method with actual state-of-the-art CNN models of VGGNet, ResNet and DenseNet under the CIFAR-10 dataset. This paper discusses the performance of the proposed method with the cases of individual usage of the structured and unstructured pruning methods and then proves that our method achieves better performance with higher compression ratio. In the best case of the VGGNet, our method results in a 13× reduction ratio in the model size, and also gives a 15× reduction ratio regarding the pruning time compared with the brute-force search method.
Informative Language Encoding by Variational Autoencoders Using Transformer

In natural language processing (NLP), Transformer is widely used and has reached the state-of-the-art level in numerous NLP tasks such as language modeling, summarization, and classification. Moreover, a variational autoencoder (VAE) is an efficient generative model in representation learning, combining deep learning with statistical inference in encoded representations. However, the use of VAE in natural language processing often brings forth practical difficulties such as a posterior collapse, also known as Kullback–Leibler (KL) vanishing. To mitigate this problem, while taking advantage of the parallelization of language data processing, we propose a new language representation model as the integration of two seemingly different deep learning models, which is a Transformer model solely coupled with a variational autoencoder. We compare the proposed model with previous works, such as a VAE connected with a recurrent neural network (RNN). Our experiments with four real-life datasets show that implementation with KL annealing mitigates posterior collapses. The results also show that the proposed Transformer model outperforms RNN-based models in reconstruction and representation learning, and that the encoded representations of the proposed model are more informative than other tested models.
THE EFFECT OF BOARD GAMES ON STUDENTS’ COMMUNICATIVE COMPETENCE

Being able to use English is the value of communication that students must have. Many students’ especially Indonesian students are still lacking in communicating using English. As teachers, we must facilitate students to be able to learn students' communicative competence. Therefore, in this study the researchers tried to use board games as learning media for students' communicative competence. Board games can create conditions that make students communicate easier to do. In this game, students are divided into groups so that students find it easier to share their opinions with their friends in small groups. The formulation of the problem in this study is there a significant effect of board games on students' communicative competence at the 10th grade of SMK Swadhipa 2 Natar? This research method uses quantitative methods. The results of this study are the average value of the control class is 0.17, while the average value of the experimental class is 0.71. It can be concluded that board games are effective for teaching students' communicative competence.
An investigation of the influence of microstructure surface topography on the imaging mechanism to explore super-resolution microstructure

Abstract Vision-based precision measurement is limited by the optical resolution. Although various super-resolution algorithms have been developed, measurement precision and accuracy are difficult to guarantee. To achieve nanoscale resolution measurement, a super-resolution microstructure concept is proposed which is based on the idea of a strong mathematical mapping relationship that may exist between microstructure surface topography features and the corresponding image pixel intensities. In this work, a series of microgrooves are ultra-precision machined and their surface topographies and images are measured. A mapping relationship model is established to analyze the effect of the microgroove surface topography on the imaging mechanism. The results show that the surface roughness and surface defects of the microgroove have significant effects on predicting the imaging mechanism. The optimized machining parameters are determined afterward. This paper demonstrates a feasible and valuable work to support the design and manufacture super-resolution microstructure which has essential applications in precision positioning measurement.
Factorization by quantum annealing using superconducting flux qubits implementing a multiplier Hamiltonian

Prime factorization (P = M × N) is a promising application for quantum computing. Shor's algorithm is a key concept for breaking the limit for analyzing P, which cannot be effectively solved by classical computation; however, the algorithm requires error-correctable logical qubits. Here, we describe a quantum annealing method for solving prime factorization. A superconducting quantum circuit with native implementation of the multiplier Hamiltonian provides combinations of M and N as a solution for number P after annealing. This circuit is robust and can be expanded easily to scale up the analysis. We present an experimental and theoretical exploration of the multiplier unit. We demonstrate the 2-bit factorization in a circuit simulation and experimentally at 10 mK. We also explain how the current conditions can be used to obtain high success probability and all candidate factorized elements.
Case Adaptation with Neural Networks: Capabilities and Limitations

Neural network architectures for case adaptation in case-based reasoning (CBR) have received considerable attention. However, architectural gaps and general questions remain. First, existing architectures focus on adaptation of numeric attributes alone. Second, some proposed neural network adaptation architectures operate directly on pairs of cases, so could be performing direct prediction instead of adaptation. Third, it is unclear how the effectiveness of CBR systems with neural network components compares to that of networks alone. This paper addresses these questions. It extends a neural network-based case difference heuristic (NN-CDH) approach to handle both numeric and nominal attributes, in an architecture that applies to both regression and classification domains. The network predicts solution difference based on problem difference, ensuring that it learns adaptations. The paper presents experiments for both classification and regression tasks that compare performance of a neural network to a baseline CBR system and CBR variants with different retrieval schemes and adaptation schemes, on both real data and controlled artificial data sets. In these tests, CBR with the extended NN-CDH generally performs comparably to the baseline neural network, and NN-CDH consistently improves the results from naive retrieval but may worsen the results of network-based retrieval.
Improving students’ ability to analyze discourse through the moodle-based blended learning method

The Covid-19 pandemic period requires online learning in Discourse Analysis Courses that are not bound by place and time by providing interactive activities and supported by complete teaching materials. The learning method is the Moodle-based Blended Learning method. This study aims to describe the results of increasing the ability to analyze discourse through the moodle-based blended learning method in the Master Program of Indonesia Language Education, University of Bengkulu. The research method used is a classroom action research method with the model of Kemmis, McTaggart, Nixon. Data collection techniques used are tests, observation, interviews, and documentation. The data analysis technique used qualitative data analysis and quantitative data analysis and the determination of the indicators for the success of the action. Test the validity of the data used triangulation of data sources and member check. The results showed an increase in activeness, motivation, independence, and student learning outcomes, especially in analyzing discourse after the application of the moodle-based blended learning method. Increasing active, motivated students and the emergence of independence through synchronous activities used Zoom Cloud Meeting and asynchronous activities through Moodle on the website https://elearning.unib.ac.id/ and assisted by Whatsapp. The increase was seen in learning outcomes in analyzing discourse, especially the ability to analyze the description, interpretation, and explanation of cycle 1 with an average value of 77.3 with the criteria B + increased in cycle 2 of 80.75 with criteria A-.
Improving End-to-End Traceability and Pharma Supply Chain Resilience with Blockchain

Regulating and monitoring a traditionally fragmented pharma supply chain has been a global challenge for decades. Without a trusted system and strong collaboration between stakeholders, threats such as counterfeits can easily intercept the supply chain and cause monumental disruptions. Today, the COVID-19 pandemic has accelerated the need for greater data transparency, better deployment of technology, and improved ways of connecting stakeholder information along the supply chain. There is a need for improved ways of working to help build up supply chain resilience, and one way is by implementing better end-to-end traceability using blockchain technology such as Hyperledger Fabric. This paper will explore the business value that blockchain brings to the pharma supply chain with better end-to-end traceability, using the example of an industry-grade blockchain solution called eZTracker. Through six key features, pharmaceutical manufacturers, patients, and Healthcare Practitioners (HCPs) can now participate in data sharing, with extended use cases of integrating blockchain with warehouse platforms, a patient-facing mobile application, and an interactive dashboard for real-time verification and data transparency. Beyond anti-counterfeit verification, other potential use cases include effective product recall management, cold chain monitoring, e-product information, and more. The effectiveness of a traceability solution is heavily dependent on the amount of data collected and is affected by poor adoption and scalability. Existing limitations that need to be addressed include the lack of mandated serialization in Asia and blockchain interoperability. To maximize the value of blockchain, collaboration is the key. Pharmaceutical manufacturers need to invest in new technologies, such as blockchain, to help them break out of data silos and operationalize data to build supply chain resilience. Pharmaceutical supply chain is the backbone of a US$1.27 trillion industry,1 but because of its highly complex and fragmented nature, it is hard to regulate and protect, and this makes it a valuable target for opportunistic parties such as counterfeiters looking to profit.2 As a result of the COVID-19 pandemic, there has been greater emphasis on transparency of data and connecting stakeholders along the pharma supply chain in real-time in the last few years. With the introduction of blockchain technology, companies are now able to implement solutions with more effective track and trace results, providing quality assurance to pharmaceutical manufacturers, patients, and Healthcare Practitioners (HCPs), and even improving operational efficiencies. This paper seeks to explore the positive business impact of end-to-end traceability using blockchain technology, and the effects it brings about, such as improving supply chain resilience and combating counterfeits, as seen in successful live use cases in Asia.
Statistical Study of User Perception of Smart Homes during Vital Signal Monitoring with an Energy-Saving Algorithm

Sensor networks are deployed in people’s homes to make life easier and more comfortable and secure. They might represent an interesting approach for elderly care as well. This work highlights the benefits of a sensor network implemented in the homes of a group of users between 55 and 75 years old, which encompasses a simple home energy optimization algorithm based on user behavior. We analyze variables related to vital signs to establish users’ comfort and tranquility thresholds. We statistically study the perception of security that users exhibit, differentiating between men and women, examining how it affects the person’s development at home, as well as the reactivity of the sensor algorithm, to optimize its performance. The proposed algorithm is analyzed under certain performance metrics, showing an improvement of 15% over a sensor network under the same conditions. We look at and quantify the usefulness of accurate alerts on each sensor and how it reflects in the users’ perceptions (for men and women separately). This study analyzes a simple, low-cost, and easy-to-implement home-based sensor network optimized with an adaptive energy optimization algorithm to improve the lives of older adults, which is capable of sending alerts of possible accidents or intruders with the highest efficiency.
The Experience of Using a New e-Learning Tool in Architectural Studies

The COVID-19 pandemic has accelerated the use of e-Learning technologies as an alternative to classical face-to-face teaching. Teachers have had to quickly adapt to virtual technologies and transform their material to online content in order to minimise the impact on students’ education. In studies such as Architecture, where visual and graphical representations are fundamental, such an adaptation is challenging. The aim of this paper is to evaluate a new e-learning methodology in architectural studies by using SAPIENS, a specifically designed e-learning platform with content creation tools and automatic correction. This platform was used to create an e-learning course on building systems. To evaluate and measure the effectiveness and acceptance of the proposed methodology in architectural education, an experiment with 108 students was carried out. Participants were divided into two homogeneous groups, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G1</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G2</i> , and only <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G1</i> was given access to the e-learning platform to take the course. The results were obtained by means of pre- and posttesting and a questionnaire. Significant differences were found between pre- and posttest scores, and nearly significant differences were found between the posttest scores of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G1</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G2</i> . The mean of all questionnaire scores exceeded 3.5 on a five-point Likert scale except for one question. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G1</i> rated better overall, and there were no significant differences by age, degree, subject or e-learning experience. Teacher’s opinion was very satisfactory as well. Therefore, the proposed methodology, with high image interaction and adapted to all participant profiles, helps in the process of learning and is highly accepted by learners, specially those who are engaged.
Watermark based Image Authentication using Integer Wavelet Transform

Digital watermarking has immense importance in protection of copyright as well as authentication. In this work, a watermarking process utilising S transform is proposed with Haar wavelet that generate only integer coefficients. Initially, the forward transform of cover image generates four coefficient bands – HH, HL, LH and LL. The HH sub-band location map is randomized and watermark image is embedded using least significant bits (LSB) substitution of the coefficient. Additionally 256 bit SHA hash code of the watermark image is generated for authentication and embedded in the HL sub-band in a similar fashion. During extraction, watermark is extracted and SHA256 hash code is computed again. This new hash code is compared with extracted hash code for authenticity. The average PSNR (Peak Signal to Noise Ratio) observed for SIPI dataset is 65 dB and 59 dB with 8192 bit and 32768 bit watermark respectively with embedding rate of 2 bits per pixel. The results of the proposed merthod are checked against existing methods and it has been found superior in many cases.
LA-ICP-MS Trace Element Composition of Sphalerite and Galena of the Proterozoic Carbonate-Hosted Morro Agudo Zn-Pb Sulfide District, Brazil: Insights into Ore Genesis

The metal-rich Vazante-Paracatu Mineral Belt, in central Brazil, hosts the Zn-Pb sulfide Morro Agudo District in the Mesoproterozoic (1.3–1.1 Ga) upper carbonate sequence of the Vazante Group. The Morro Agudo district is comprised of the Morro Agudo deposit and the Bento Carmelo, Sucuri, and Morro do Capão occurrences. This carbonate sequence also hosts the Fagundes, Ambrósia and Bonsucesso Zn-Pb sulfide deposits (northern part) and the zinc silicate Vazante and North Extension deposits (southern part). The structurally controlled, stratabound and stratiform styles of mineralization in the Morro Agudo orebodies have been variably classified as sedimentary exhalative, Irish-type and Mississippi Valley-type. In this study, laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) spot analyses of sphalerite and galena from the Morro Agudo district revealed that red sphalerite (interpreted as the last stage) has higher Fe and Mn and lower Bi, Co, Cu, Ge, Hg, Tl compared to the other types of sphalerite, whereas the first generation of galena (Gn-I) is enriched in Ag, Cd, and Se and depleted in Cu and Mn relative to later galena (Gn-II). Mineral paragenesis and principal component analysis (PCA) of ore mineral composition suggest that the Morro Agudo, Sucuri and Morro do Capão mineralized zones formed by similar processes involving Zn-Pb mineralizing fluids with various compositions, comprising two main elemental associations: (1) Fe, As, In, Mn, Sb, Ag; and (2) Cd, Bi, Co, Ga, and Se. Bento Carmelo is distinguished in PCA by its unique dolomite-hosted sphalerite composition with elevated concentrations of Cu, Ge, Hg and likely formed from distinct fluids or processes. Temperatures of the mineralizing fluids for the Morro Agudo district range from 82 to 320 °C, calculated based on the trace element composition of sphalerite. The styles of mineralization and ore compositions are consistent with MVT deposits; however, fluid temperatures are hotter than typical MVT mineralizing fluids and may reflect a higher geothermal gradient or active advective fluid flow during the Brasiliano orogeny.