Reviewerly/exHarmony huggingface dataset:
Validation of decomposition models for solar irradiance at high latitudes: A preliminary study

Abstract The accurate estimation of buildings’ solar potential contributes to boost the exploitation of solar energy at high latitudes. The decomposition of global irradiation into the direct and diffuse fractions is a fundamental step of the solar irradiance model chain. Diffuse and direct irradiation are, in fact, rarely measured. Previous works recommended Yang4 as the decomposition model with the best overall performance. However, in geographically limited applications, quasi-universal decomposition models such as Yang4 and Engerer4 can be outperformed by local models (i.e., models parametrized with climate-specific data) such as Skartveit3 and Starke3. This makes necessary to perform local validation studies to verify the findings from worldwide validation studies. In this study, the four decomposition models are implemented in Python and experimentally validated against one-minute solar irradiance data (i.e., direct and diffuse irradiance) of Trondheim (Lat. 63°26’ N, Norway). Two months representative of clear sky (August) and overcast (October) conditions are considered. The study confirms that the Yang4 model performed the best for high-latitude application: the nMBE ranged from -0.54% (August) to 0.65% (October), the nRMSE from 17.18% (August) to 22.29% (October), and the R 2 from 0.96 (August) to 0.97 (October). However, Skartveit3 combines a level of performance close to Yang4 with the lower number of input parameters.
Morphophonemic parser: A study of teachers and students

The objectives of this study were to design a morphophonemic parser (MPP) and to get feedback from teachers and students after using the designed parser. Drastic changes have occurred in the field of education after the advancement of technology. New methods and techniques with digital content are widely used in this field. One of them is the use of parsers in educational institutions. Parser is a tool that helps to facilitate teaching and learning. The MPP is a parser that can be used in the teaching and learning process. The study used a mixed mode of research which includes qualitative and quantitative research methods. 400 school teachers and students were selected as informants for this study using a simple random sampling method. The study used a questionnaire as a data collection tool and analyzed using the survey analysis method. The main findings of the study are, that an MPP is designed with a set of morphophonemic rules and most of the teachers and students are satisfied and excited to use the designed MPP in their teaching and learning. The results show that 87% of the teachers and students strongly agree that they are satisfied and 64.5% of them said that the parser helps them to learn easily. These results suggest the use of MPPs in educational institutions.
A Closer Look at the Expressive Power of Logics Based on Word Equations

Abstract Word equations are equations $$\alpha \doteq \beta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>α</mml:mi> <mml:mo>≐</mml:mo> <mml:mi>β</mml:mi> </mml:mrow> </mml:math> where $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> and $$\beta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>β</mml:mi> </mml:math> are words consisting of letters from some alphabet $$\Sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Σ</mml:mi> </mml:math> and variables from a set X . Recently, there has been substantial interest in the context of string solving in logics combining word equations with other kinds of constraints on words such as (regular) language membership (regular constraints) and arithmetic over string lengths (length constraints). We consider the expressive power of such logics by looking at the set of all values a single variable might take as part of a satisfying assignment for a given formula. Hence, each formula-variable pair defines a formal language, and each logic defines a class of formal languages. We consider logics arising from combining word equations with either length constraints, regular constraints, or both. We also consider word equations with visibly pushdown language membership constraints as a generalisation of the combination of regular and length constraints. We show that word equations with visibly pushdown membership constraints are sufficient to express all recursively enumerable languages and hence satisfiability is undecidable in this case. We then establish a strict hierarchy involving the other combinations. We also provide a complete characterisation of when a thin regular language is expressible by word equations (alone) and some further partial results for regular languages in the general case.
Blockchain-Powered Fish Industry: Trust Perceptions of Final Consumers on Traceable Information Availability

Blockchain is being actively discussed for business applications to digitalise supply chains. The still nascent level of blockchain adoption creates a difficulty to see the potential value that it may add to products and final consumers. Through a quantitative method, this study explores the impact of the traceability feature of products on the trust in the seller supermarket chain from the perspective of final consumers. Data from 417 questionnaires applied to buyers of fish was used to analyse such relation. Findings reveal the interest of final consumers towards traceable products and a positive relationship between the traceable information availability and the trust towards product.
The integration of artificial intelligence with contrast-enhanced mammogram in the work up of suspicious breast lesions: what do you expect?

Abstract Background The enhancement overlaps at contrast-enhanced mammogram (CEM) between benign and malignant breast abnormalities presents a high probability of false-positive lesions and subjects females’ candidate for screening and diagnostic mammograms to unnecessary biopsy and anxiety. The current work aimed to evaluate the ability of mammograms scanned by artificial intelligence (AI) to enhance the specificity of CEM and support the probability of malignancy in suspicious and malignant looking breast lesions. Methods The study included 1524 breast lesions. The AI algorithm applied to the initial mammograms and generated location information for lesions. AI scoring suggested the probability of malignancy ranged from 100% (definite cancers) and &lt; 10% (definite non-cancer) and correlated with recombinant contrast enhanced images. Results The malignant proved abnormalities were 1165 (76.5%), and the benign ones were 359 (26.5%). BI-RADS 4 category was assigned in 704 lesions (46.2%) divided into 400 malignant (400/704, 56.8%) and 304 benign (304/704, 43.2%). BI-RADS 5 category presented by 820 lesions (53.8%), 765 of them were malignant (765/820, 93.3%) and 55 were benign (55/820, 6.7%). The sensitivity of digital mammogram whether supported by AI (93.9%) or contrast media (94.4%) was significantly increased to 97.2% ( p &lt; 0.001) when supported by both methods. Improvement of the negative predictive value (from 80.6% and 79.6% to 89.8%, p &lt; 0.05) and the accuracy (from 91.1 and 88.8 to 94.0%, p &lt; 0.01) was detected. Conclusions Contrast-enhanced mammogram helps in specification of different breast lesions in view of patterns of contrast uptake and morphology descriptors, yet with some overlap. The use of artificial intelligence applied on digital mammogram reduced the interpretational variability and limited attempts of re-biopsies of suspicious looking breast lesions assessed by contrast-enhanced mammograms.
Blockchain-Based Solution for Charitable Supply Chains: Network Proposal Architecture for Portuguese Tax Consignment Program

This paper presents a blockchain-based solution for enhancing transparency, accountability, and efficiency in charitable supply chains, with a focus on the Portuguese Tax Consignment Program, but adaptively to other tax systems. Charitable organizations often face challenges in managing and tracking donations, leading to potential inefficiencies and distrust among stakeholders. Leveraging blockchain technology, this research proposes a network proposal architecture that enables secure and immutable recording of donation transactions, ensures proper allocation of funds, and fosters transparency in the charitable supply chain ecosystem.
Fast computation of cyclic convolutions and their applications in code-based asymmetric encryption schemes

The development of fast algorithms for key generation, encryption and decryption not only increases the efficiency of related operations. Such fast algorithms, for example, for asymmetric cryptosystems on quasi-cyclic codes, make it possible to experimentally study the dependence of decoding failure rate on code parameters for small security levels and to extrapolate these results to large values of security levels. In this article, we explore efficient cyclic convolution algorithms, specifically designed, among other things, for use in encoding and decoding algorithms for quasi-cyclic LDPC and MDPC codes. Corresponding convolutions operate on binary vectors, which can be either sparse or dense. The proposed algorithms achieve high speed by compactly storing sparse vectors, using hardware-supported XOR instructions, and replacing modulo operations with specialized loop transformations. These fast algorithms have potential applications not only in cryptography, but also in other areas where convolutions are used.
Semantic rule-based sentiment detection algorithm for Russian publicism sentences

The article is devoted to the task of sentiment detecton of Russian sentences, which is understood as the author’s attitude on the sentence topic expressed through linguistic expression features. Today most studies on this subject utilize texts of colloquial style, limiting the applicability of their results to other styles of speech, particularly to the publicism. To fill the gap, the authors developed a novel publisism sentences oriented sentiment detection algorithm. The algorithm recursively applies appropriate rules to sentence parts represented as constituency trees. Most of the rules were proposed by a philology expert, based on knowledge on the expression features from Russian philology, and algorithmized using constituency trees generated by the algorithm. A decision tree and a sentiment vocabulary are also used in the work. The article contains the results of evaluation of the algorithm on the publicism sentences corpus OpenSentimentCorpus, F-measure is 0.80. The results of errors analysis are also presented.
Towards Reliable Federated Learning Using Blockchain-Based Reverse Auctions and Reputation Incentives

In recent years, the explosion of big data has presented unparalleled opportunities for the advancement of machine learning (ML). However, the vast size and sensitive nature of these datasets present significant challenges in terms of privacy and security. Federated Learning has emerged as a promising solution that enables a group of participants to train ML models without compromising the confidentiality of their raw data. Despite its potential, traditional federated learning faces challenges such as the absence of participant incentives and audit mechanisms. Furthermore, these challenges become more significant when dealing with the scale and diversity of big data, making efficient and reliable federated learning a complex task. These limitations may compromise model quality due to potential malicious nodes. To address the above issues, this paper proposes a BlockChain-based Decentralized Federated Learning (BCD-FL) model. In BCD-FL, we design a smart contract approach based on the reverse auction-based incentive mechanism and a reputation mechanism to promote the participation of reliable and high-quality data owners. Theoretical analysis shows that the BCD-FL model satisfies several desirable properties, such as individual rationality, computational efficiency, budget balance, and truthfulness. In addition, experimental results also show that the proposed model enables more efficient federated learning and provides some level of protection against malicious nodes. Therefore, the BCD-FL model presents a potential solution to the challenges in federated learning and opens up new possibilities for achieving efficient large-scale machine learning.
Keyphrase generation for the Russian-language scientific texts using mT5

In this work, we applied the multilingual text-to-text transformer (mT5) to the task of keyphrase generation for Russian scientific texts using the Keyphrases CS&amp;Math Russian corpus. The automatic selection of keyphrases is a relevant task of natural language processing since keyphrases help readers find the article easily and facilitate the systematization of scientific texts. In this paper, the task of keyphrase selection is considered as a text summarization task. The mT5 model was fine-tuned on the texts of abstracts of Russian research papers. We used abstracts as an input of the model and lists of keyphrases separated with commas as an output. The results of mT5 were compared with several baselines, including TopicRank, YAKE!, RuTermExtract, and KeyBERT. The results are reported in terms of the full-match F1-score, ROUGE-1, and BERTScore. The best results on the test set were obtained by mT5 and RuTermExtract. The highest F1-score is demonstrated by mT5 (11,24 %), exceeding RuTermExtract by 0,22 %. RuTermextract shows the highest score for ROUGE-1 (15,12 %). According to BERTScore, the best results were also obtained using these methods: mT5 — 76,89 % (BERTScore using mBERT), RuTermExtract — 75,8 % (BERTScore using ruSciBERT). Moreover, we evaluated the capability of mT5 for predicting the keyphrases that are absent in the source text. The important limitations of the proposed approach are the necessity of having a training sample for fine-tuning and probably limited suitability of the fine-tuned model in cross-domain settings. The advantages of keyphrase generation using pre-trained mT5 are the absence of the need for defining the number and length of keyphrases and normalizing produced keyphrases, which is important for flective languages, and the ability to generate keyphrases that are not presented in the text explicitly.
Examining the Effect of Anthropomorphic Design Cues on Healthcare Chatbots Acceptance and Organization-Public Relationships: Trust in a Warm Human Vs. a Competent Machine

AI-powered healthcare chatbots demonstrate great promise in enhancing communication and fostering stronger relationships between healthcare organizations and their publics. This study conducted an experiment to examine how different anthropomorphic design cues shape chatbot acceptance and organization-public relationships by influencing trust in the chatbot. Results revealed an interesting dynamic: when users interacted with healthcare chatbot featuring human-like cues, they tend to place greater trust in the chatbot that showed warmth rather than competence. Conversely, in interactions with a chatbot embodying machine-like cues, a competent conversation style generated higher levels of trust compared to a warm style. This trust in human-computer interactions further enhanced users’ intentions to continue using the chatbot and follow its recommendations, alongside bolstering relational trust and satisfaction with the healthcare organization. These findings contribute to the theoretical understanding of human-chatbot interaction and offer practical insights by aligning chatbot design nuances with the broader strategic health communication goals.
Digital Literacy in the Palestinian Public Schools: The Influence of Gamification-Based Learning

Digital literacy refers to a set of attitudes, knowledge, and skills, which assist individuals to access digital information effectively and ethically. Moreover, it refers to the use of ICT tools that enable learners to communicate with each other and to prepare and assess digital educational resources. Both teachers and students should employ different learning methods to improve their digital skills and acquire an acceptable level of digital literacy. Game-based learning offers a good means to achieve this objective. The present research paper investigates the impact of gamification on digital literacy among students in public schools. A combined quantitative and qualitative research method was used based on quasi-experimental pre/post-test as well as semi-structured interviews. The researchers used Minetest game as an intervention to develop ICT skills on a sample of students who were underwent the suggested program developed by Alnayzak non-profit organization specialized in education in collaboration with the Palestinian Ministry of Education (MoE).
Anemia and Microvascular Complications in Type 2 Diabetes Mellitus

Background: In Type 2 Diabetes Mellitus, anemia affects tissue oxygenation and glycemic homeostasis, which promotes the premature onset of microvascular damage. Objective: To determine the association between the degree of anemia and microvascular complications in patients with type 2 diabetes mellitus. Methods: This is a cross-sectional, analytical study. Patients with diabetes mellitus and any complications were included. A review of electronic medical records of patients who met the selection criteria was conducted. The information was captured in a database previously structured in the SPSS statistical software and analyzed using inferential statistics. Results: With a total of 113 patients, with an average age of 62.23±10.3 years. Of the patients with Type 2 Diabetes Mellitus, 54.9% (62) presented with anemia. Of these, 46.9% (53) presented with Grade I, 5.3% (6) with Grade II, and 2.7% (3) with Grade III anemia, according to the World Health Organization. In terms of microvascular complications, only 5.3% (6) of those with diabetic retinopathy presented with anemia, the most frequent being Grade I. In the group of patients with diabetic neuropathy, Grade I was found in 19.5% (22), and with nephropathy it was 35.4% (40), with 30.1% (34) being Grade I.
A Contemporary Systematic Review on Meta-heuristic Optimization Algorithms with Their MATLAB and Python Code Reference

Abstract Optimization is a method which is used in every field, such as engineering, space, finance, fashion market, mass communication, travelling, and also in our daily activities. In every field, everyone always wants to minimize or maximize something called the objective function. Traditional and modern optimization techniques or Meta-Heuristic (MH) optimization techniques are used to solve the objective functions. But the traditional optimization techniques fail to solve the complex and real-world optimization problem consisting of non-linear objective functions. So many modern optimization techniques have been proposed exponentially over the last few decades to overcome these challenges. This paper discusses a brief review of the different benchmark test functions (BTFs) related to existing MH optimization algorithms (OA). It discusses the classification of MH algorithms reported in the literature regarding swarm-based, human-based, physics-based, and evolutionary-based methods. Based on the last half-century literature, MH-OAs are tabulated in terms of the proposed year, author, and inspiration agent. Furthermore, this paper presents the MATLAB and python code web-link of MH-OA. After reading this review article, readers will be able to use MH-OA to solve challenges in their field.
A Regression Model for Finding Optimal Solar Panel Inclination Angles

Energy efficiency is at the top of the measures created against the ever-increasing energy need for supply. Moreover, the global climate crisis, the protection of the environment, and the improvement of the economy are only possible by optimizing active energy systems. For this purpose, it is a requirement that the solar panels have the appropriate tilt angles and correct positions in order to maximize the efficiency of solar energy systems. In this study, for the province of Van (43°38’ N-38°50’ E), whose solar energy potential is higher than the average of Turkey, the optimum solar panel angle according to the months of the year was determined for the south-facing solar panels. It has been determined that the annual panel inclination angle is 30.540 and in the case of monthly change of panel angles, the annual yield increases up to 14% compared to the horizontal plane, reaching an average annual radiation value of 5090 Wh/m2-day. 8 different mathematical models have been developed from 4 different variables for estimating the optimum angle value specific to the latitude of the region. The statistical coefficient of certainty (R2) range of these models varies between 0.9068 and 0.9964. As a result of these findings, the regression model, whose applicability and reliability have been proven, can serve as examples of locations at different coordinates in the same latitude. Furthermore, the results set an example for academics and industrialists in terms of the proposed model's engineering design, eco-energy analysis, and use in optimal design processes.
Exploring Global Solar Radiation: Enhancing Ground-Level Solar Radiation Prediction using Hottel's Semi-Empirical Model and Sunshine Duration Analysis

The effective utilization of solar energy at a specific geographical locale is contingent upon the acquisition and assimilation of comprehensive and meticulous solar radiation data pertinent to that specific site. A profound understanding of these datasets constitutes a pivotal factor in the precision-driven design and dimensioning of solar energy systems. It ensues that the attainment of accurate system dimensioning is contingent upon the continual availability of spatially and temporally resolved measurements. The principal objective of this research endeavor is to expound upon the methodological approach employed in the computation of solar energy parameters, alongside the delineation of the pertinent dataset by extrapolating salient insights. Prior to the initiation of any optimization endeavor, a methodical scrutiny of the geospatial and temporal distribution of solar insolation stands as a preeminent prerequisite, indispensably contributing to the efficacious implementation of solar infrastructure. The assessment of solar energy generation potential within the examined region necessitates a meticulous investigation of the theoretical solar resource inherent to Khouribga. Through a meticulous computation regimen encompassing insolation and solar irradiance metrics, this investigation facilitates the discernment of the optimal incident angle for maximal energy absorption by solar photovoltaic cells.