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Since learning theories have been often ignored in translation education, the present study aimed to explore the impact of implementing principles of connectivism learning theory in translation training using an AI-powered translation tool, Matecat. Participants were thirty third-year students who enrolled in a course on the translation of Islamic texts from English. Before the commencement of the course, a pretest was given to the students to assess their translation skills. Then, on the basis of the results, two groups of experimental and control were formed. The homogeneity of the two groups was further checked by using independent samples t-test in SPSS. Unlike the control group, the experimental group was trained on the basis of the principles of connectivism and the tailored model designed for the present study. At the end of the program a posttest was administered, and the scores were subjected to statistical analysis using independent samples t-test. The results showed although both groups had started at more or less the same level, the quality of translations produced by the experimental group improved significantly more than that of the control group. Furthermore, the experimental group outperformed the control group in cohesion and coherence, structure, style and cultural aspects. In fact, the findings indicated that employing AI-powered translation tools per se will not lead to a significant improvement in learners' translation quality unless the training is integrated with pedagogical application of a digital age learning theory, such as connectivism.

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This study investigates the potential of interactive English as a Foreign Language (EFL) learning tools powered by Artificial Intelligence (AI) to enhance digital competence among higher education students. The primary aim is to detail the learning process in several aspects: Interactive EFL Learning Based on AI, its impact and implications, and Students' Digital Competence. The research employs a qualitative approach, utilizing qualitative interviews and classroom observation. A sample of 80 students from three different universities participated in using AI-powered EFL tools (ChatGPT) over a semester. The study employed intervention assessments to measure changes in digital competence and English writing proficiency. Additionally, student feedback was collected through structured interviews and observation to explore their experiences and perceptions. The collected data were processed and analyzed using manual coding techniques by compiling every response and organizing the summary.The results indicate that AI-based EFL tools significantly improve students' digital competence, including their ability to use technology effectively for writing tasks. Students demonstrated enhanced engagement, better language skills, and increased confidence in using digital tools for academic purposes. However, challenges such as technological anxiety and varying levels of tool effectiveness were also identified. This study highlights AI's efficacy in enhancing digital competence within the EFL environment, adding to the expanding corpus of research on the topic. The results offer important insights for educators and policymakers aiming to improve English language education using innovative AI-based approaches.
 

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This study explores teachers’ readiness to implement generative artificial intelligence (GenAI) in their teaching and learning processes, alongside the benefits and challenges related to its utilizations in the Omani context. The data analysis process involved analyzing responses from the 5-point Likert scale questionnaire using descriptive statistics. A sample of 61 teachers with different qualifications from different educational institutions in the Sultanate of Oman participated in the study. The findings revealed that teachers had a positive level of readiness to implement GenAI, highlighting a spectrum of readiness levels, such as attending training sessions about GenAI, and a significant willingness of utilizing GenAI tools in their classes. On the other hand, teachers reported a positive benefit and experience in improving their teaching, stating that GenAI enables them to save their time, improves their teaching experience and job satisfaction, and offers them adaptive learning and instant feedback. However, findings revealed number of challenges for teachers such as a lack of awareness about policies and ethics in implementing GenAI tools, and their cost. Moreover, teachers indicated a moderate concern regarding the challenges of integrating GenAI tools into their teaching practices. Based on the findings, the study provides significant insight for teachers, policymakers, and syllabi designers, stressing the significant importance of preparing teachers to efficiently integrate GenAI in their pedagogical duties to make the most educational potential while mitigating related risks.

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This study applies artificial neural networks (ANNs) to assess the impact of climate factors on the collaborative development of agriculture and logistics in Zhejiang, China. The ANN model investigates how average temperature and rainfall from 2017-2022 influence crop yield, water usage, energy demand, logistics efficiency, and economic growth at yearly and seasonal scales. By training the neural network using temperature and rainfall data obtained from ten weather stations, alongside output indicators sourced from statistical yearbooks, the ANN demonstrates exceptional precision, yielding an average R² value of 0.9725 when compared to real-world outputs through linear regression analysis. Notably, the study reveals climate-induced variations in outputs, with peaks observed in crop yield, water consumption, energy usage, and economic growth during warmer summers that surpass historical norms by 1-2°C. Furthermore, the presence of subpar rainfall ranging from 20-30 mm also exerts an influence on these patterns. Seasonal forecasts underscore discernible reactions to climatic factors, especially during the spring and summer seasons. The findings underscore the intricate relationship between environmental and economic factors, indicating progress in agricultural practices but vulnerability to short-term climate fluctuations. The study emphasizes the necessity of adapting supply management to address increased water demands and transitioning to clean energy sources due to rising energy consumption. Moreover, optimizing logistics requires strategic seasonal infrastructure planning.

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Aims: Today, the use of artificial intelligence has grown significantly, and is developing as a new field. The main goal of this research is to know the capabilities of artificial intelligence in advancing the design and implementation process in the artificial environment. The practical goal of research is the development and application of the most important achievements of machine learning in the field of design.
Methods: The main research method is "meta-analysis" research in the paradigm of "free research" with a critical approach and basic design, which examines the general knowledge field of this field using broad techniques. Then, to consolidate the literature on the topic, through searching three reliable knowledge bases of this field, we collected articles related to machine learning in the fields of unsupervised learning methods, semi-supervised learning, and reinforcement learning; The most important capacities and shortcomings, and strengths and weaknesses are reviewed.
Findings: Quantitative findings from the combined data indicate that supervised machine learning and directed deep learning can be the best option to recommend in the future of design. While the learning process in deep learning is gradual and slower, supervised machine learning works faster in the testing phase.
Conclusion: The research emphasizes that supervised machine learning is the best option for predicting answers in the design process. But if, in addition to prediction, the issue of creativity in design is desired, deep learning is more efficient.

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Subject
In this study, the steam reforming of the methanol process was analyzed based on three different inputs including temperature, pressure, and H₂O/CH₃OH ratio with the use of different Artificial Intelligence methods.
Methodology
In the first step, Cu-Zn/ZrO₂ catalysts were synthesized via the co-precipitation method, and then experimental tests of steam reforming of methanol were performed at a temperature range of 180 –500 °C, the pressure of 1-11 bar, and the H₂O/CH₃OH ratio of 0.75-3.75 on the Cu-Zn/ZrO₂ catalyst in a fixed bed reactor. Afterward, three different methods of Mamdani fuzzy type-1, Mamdani fuzzy type-2, and Sugeno fuzzy were applied in order to develop the models. Using these methods, the developed models only required the heuristics derived from the expert’s knowledge and some experimental data, without needing the calculation of complex kinetic as well as thermodynamic parameters related to the corresponding process. In addition, the structures of the developed fuzzy models were optimized to improve the model performance according to the analysis of the initial results. The model developments didn’t require a high number of experimental data, and this feature is especially interesting when dealing with the process conditions in which data gathering is expensive or the accuracy of data is low.
The main results
The overall accuracy as well as the properties of the developed models were compared. The type-2 Mamdani fuzzy model proved to be the best model, using which, the methanol conversion, H₂ yield, and CO yield were predicted with accuracies of 67%, 91%, and 83%, respectively.
 

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Data and information play a special role in the transparency of water governance. On the other hand, witnessing contradictions in water resources data and information, inconsistent readings and narratives about water assets, outdated hardware equipment, and to some extent software enhancement in the preparation and presentation of water resources information compared to global advances, necessitates a serious review of water resources data collection and processing systems. In this regard, artificial intelligence methods, sensors, and remote sensing technologies are considered in accurate water resources accounting. This article is a systematic review of about 100 international articles that present the latest findings related to software and hardware equipment for monitoring hydrological cycle meta-indicators. These meta-indicators include precipitation, water depth/water level/flow velocity and discharge of rivers, and groundwater level. In each case, while providing a list of the most important technologies, the application level of these technologies in monitoring surface and groundwater resources in Iran was evaluated. The conducted studies prove the unfavorable application technologies in monitoring hydrological cycle in Iran. For example, out of a total of twenty-six known technologies related to surface flow measurements, only two technologies have been widely used Iran; four technologies have reached the knowledge frontier and widespread production by domestic knowledge-based companies, and eleven technologies have not yet reached the knowledge frontier Iran. In this paper, suggestions were presented to outline the path for developing new technologies for water cycle data collection and transformation in the modernization of Iran's water resources data collection and data processing infrastructure.

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Aims: New developments in artificial intelligence offer promising prospects for transforming therapeutic approaches and enhancing outcomes for individuals with a range of abilities. Therefore, the aim of this systematic review was to investigate the applications of artificial intelligence in occupational therapy.
Information & Methods: In this systematic review, adhering to the PRISMA guidelines, we searched English-language studies regarding the use of artificial intelligence in occupational therapy, on February 18, 2024, using the databases PubMed, Embase, Scopus, and Web of Science.
Findings: Six eligible studies were included in this review. The artificial intelligence approaches used in these studies included artificial neural networks, multi-core learning models, deep learning models, machine learning models, and classification and regression trees. All the studies reported promising results regarding the use of artificial intelligence in evaluating and predicting return to work, alleviating symptoms, recovering social function, reducing disease recurrence, improving re-employment rates, and enhancing the overall health level of patients.
Conclusion: One of the most common issues with artificial intelligence models is their low accuracy and the potential for errors.

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Aims: Artificial intelligence (AI) and machine learning (ML) are revolutionizing healthcare by enhancing the prediction of learning needs and enabling tailored educational interventions for patients and staff. This study explores the application of AI and ML models to predict learning needs from the patient's perspective.
Instruments & Methods: Three ML models (Linear Regression, Random Forest, and Gradient Boosting) were trained on health literacy, demographic, and treatment data from 218 cancer patients at Sultan Qaboos Comprehensive Cancer Center. Evaluation metrics included Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R² Score, and Area Under the Curve (AUC). Classification models (Random Forest, Gradient Boosting, Decision Tree, and Extra Trees) were assessed for accuracy, precision, recall, F1-score, and AUC in categorizing learning needs.
Findings: Gradient Boosting had the best predictive performance (MAE:0.0534, RMSE: 0.0788, R²:0.9844, AUC:0.96), followed by Random Forest (AUC:0.93). Linear Regression was less effective (AUC: 0.85). Key predictors included literacy level in chemotherapy, hormonal therapy, and treatment experiences, while demographic factors had minimal impact. For classification, Gradient Boosting and Decision Tree models achieved the highest accuracy (96.51%) and AUC (0.96). Random Forest showed 94.19% accuracy, while Extra Trees had 90.70%, indicating variability in model performance.
Conclusion: AI and ML, particularly Gradient Boosting, demonstrate strong potential in predicting and categorizing learning needs.

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These days biosensors have worthy applications in different fields such as biomedicine, disease diagnosis, treatment monitoring, various aspects of the environment, food control, drug production, and assorted sides of medical science. Recently, different types of biosensors such as enzyme biosensors, immune, tissue, DNA, and thermal biosensors have been studied precisely by some research groups. These biosensors have many advantages such as simplicity in implementation, very high sensitivity, automatic performance, intrinsic and natural small size. Another valuable benefit of biosensors is that their high-affinity paring with biomolecules allows sensitive (high-sensitivity) and selective detection from a wide range of analytes. Artificial intelligence (AI) due to its high potency, if combined with biotechnology, like biosensors, can be effective in accurate prediction, diagnosis and treatment of some diseases, including cancer. Today, Machine learning (ML) as one of the branches of AI has become a beneficial tool in analyzing and categorizing obtained data from biosensors for bioanalysis. Using ML algorithms automates the complicated processes of extraction, processing, and assaying data achieved from biosensors. This article is a review for introducing and survey of various biosensors, their applications, and ways to apply them, focusing on cancer and Covid19 which are important diseases in the world obtained from previous studies, as a summary and providing information for researchers which working in this field.
 

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After adventing of computers in the Middle of the 20th century and extensive production of miraculous literature and artistic works by it without any direct intervention of human being, the ways of legal protection of these kinds of works have been challenged. Questions like authorship, duration of protection and moral right in the computer-generated-works (CGW) should be decided in copyright laws. Although some statute laws such as England Copyright, Designs and Patents Act 1988 have specified obviously some legal aspects of these kinds of works, but it's position has yet to be determined in many countries like USA, France and Germany. Current Iran’s copyright law which is under the influence of France intellectual property law, also has not yet been proceeding to this issue. However, considering the expeditious increasing of CGW productions and its economic value, it seems that nowadays the suitable reaction of legislatures and protection of them at least under »sui generis right« are necessary.

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Aims: AI an emerging phenomenon revolutionized the the interior architecture design process, especially in the post-corona era, when the concept of "healthy building" has become more important. The research aims to show the privileged role of AI in creating interaction between "interior architecture" and the concept of healthy building.

Methods: The methodology of the research is based on meta-analysis based on the theory of master architecture. Meta-analysis or meta-analysis, emphasizing the statistical combination of the results of several studies, covers a large part of the analytical literature in the field of the role of artificial intelligence in interior architecture. Based on the selected research approach, in data extraction, combined methods of machine calculations such as hybrid meta-simulation, clustering, prospective interpretation of variables and extraction of effect size, variance and regression have been used.

Findings: Numerical results and quantitative findings in the review of tools developed in the field of interior architecture show that the most developed tools are related to the initial stages of design, followed by the tools related to the operation stage, and then the related tools. to the final stages of architectural design.

Conclusion: The qualitative results of the research show that the set of tools developed in the field of interior architecture do not have high analytical accuracy, for this reason, it is more logical to use them in the idea generation stage. Also, the tools developed in the second part are related to the field of building chemistry, residents' health, biocomputing, etc.

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"English Language and Literature" courses are essential components of university education. They provide a significant avenue for understanding the politics, economics, and customs of English-speaking countries. These courses facilitate a mastery of English grammar, which in turn enhances students' comprehension of spoken and written English content. However, traditional modes of instruction in English Language and Literature often lack engagement and interactivity, thereby limiting the effectiveness of learning in this field. In order to boost learners' interest and efficiency in studying English, it is imperative to shift away from conventional teaching approaches. With the rapid advancement of artificial intelligence in various domains, its integration with English Language and Literature education can yield intelligent learning experiences. This study employs a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) to reform the teaching model in English Language and Literature. The results indicate that CNN and GRU methodologies offer substantial support in realizing intelligent approaches to teaching this field. These methods exhibit a high degree of similarity and accuracy in predicting linguistic features in English Language and Literature. They excel in terms of predictive and scatter error distribution, showcasing superior performance.


 

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Aims: The development of AI tools in interior architecture has brought both opportunities and challenges. The primary objective of this research is to highlight the weaknesses and shortcomings in the professional application of artificial intelligence-based tools in interior architecture. The second objective is to introduce and explain the role of agent-based systems in enhancing the efficiency of artificial intelligence tools in interior architecture.

Methods: This research employs a descriptive-analytical method as its primary approach. In the descriptive phase, data were collected from existing sources to examine the impact of artificial intelligence on the interior design process. Subsequently, a questionnaire was designed to gather designers' opinions regarding the influence of artificial intelligence on interior architecture and the role of designers. The collected data were then statistically analyzed using SPSS.

Findings: More than 80% of the surveyed interior architects were familiar with artificial intelligence tools, with most identifying Midjourney as a key tool that reduces time and increases efficiency. The most significant challenges identified pertain to the healthy building domain, particularly in relation to indoor air quality, environmentally friendly materials, and the comfort and ergonomics of spaces.

Conclusion: The qualitative findings of this research indicate that agent-based systems play a crucial role in enhancing the efficiency of artificial intelligence tools in the interior architecture process. This is particularly important in the healthy building domain, as it provides a comprehensive model for understanding the interaction between Bauphysik, Bauchemie, Baubiologie, environmentally friendly materials, and occupant health.
 

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Blood pressure monitoring is a vital component of maintaining overall health. High blood pressure values, as a risk factor, can lead to heart attacks, strokes, and heart and kidney failures. Similarly, low blood pressure values can also be dangerous, causing dizziness, weakness, fainting, and impaired oxygen delivery to organs, resulting in brain and heart damage. Consequently, continuous monitoring of blood pressure levels in high-risk individuals is very important. A Holter blood pressure monitoring device is prescribed for many patients due to its ability to provide long-term and valuable blood pressure data. The pursuit of software techniques and the development of cuffless blood pressure measurement devices, while ensuring patient comfort and convenience, are among the significant challenges that researchers are focusing on. In this study, a deep learning framework based on the UNet network is proposed for continuous blood pressure estimation from photoplethysmography signals. The proposed model was evaluated on the UCI database, involving 942 patients under intensive care, and achieved mean absolute errors of 8.88, 4.43, and 3.32, with standard deviations of 11.01, 6.18, and 4.15, respectively, for systolic, diastolic, and mean arterial blood pressure values. According to the international BHS standard, the proposed method meets grade A criteria for diastolic and mean blood pressure estimations and grade C for systolic blood pressure estimation. The results of this study demonstrate that the suggested deep learning framework has the necessary potential for blood pressure estimation from PPG signals in real-world applications.
 

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When groundwater is contaminated, removal of contaminants and the restoration of quality may be slow and sometimes, impractical. It can be harmful for human health, the ecosystem and can result in water shortage. Thus, simulation of contaminant transport can be an important task in hydro-environmental studies and consequently, it is necessary to develop the robust models which can determine the temporal forecast of pollution. For temporal modeling groundwater level and contaminant concentration (GLCC), several computational methods, namely, finite difference method, finite volume method, finite element method and boundary element method have been applied for numerical solution of governing physical-based partial differential equation (PDE). Although the physical-based numerical technique are widely used for temporal and/or spatial modeling of systems, some real-world conditions such as anisotropy and heterogeneity can have meaningful impacts on GLCC and restrict the usefulness of such methods. As a result, these method may be replaced by other techniques. In situation where there is no sufficient field data and output accuracy is preferred over perception of phenomena, a data-driven or black box model can be proper subsided. The uncertainty and complexity of the groundwater process have caused data-driven models such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) are widely used by hydrogeologists. Several studies have been performed to examine the susceptibility of artificial intelligence (AI) models for GFCT modeling. Wavelet transform coherence (WTC) is a technique for examination the localized correlation coefficient and their phase lag between non-stationary time series as a function of both time-frequency spaces. Furthermore, the cross-wavelet power is indicated as high common power of two time series and is found in time-frequency space by cross wavelet transform (XWT). Specifically, XWT investigates the regions in time-frequency space with large common power about a consistent phase relationship, and accordingly suggestion for causality between and time series. On the other hand, the WTC explores the regions in time-frequency apace in which and time series co-vary, but not essentially with high power. So, while analyzing two time series for evaluating both causality and local co-variance, the WTC is more suitable. In order to examine the applicability of the proposed AI-meshless model in real world conditions, the contaminant transport problem in Miandoab plain located in the northwest of Iran was considered as the case study. Miandoab plain, is located in a delta region of Zarrineh and Simineh Rivers. Urmia Lake in north of Miandoab plain, the largest salt-water lake in the Middle East, has been experienced climate change in early 2 decades. The wavelet transform coherence used in this study can be considered as a novel method for spatial clustering of piezometers, for detecting the interaction of aquifers in the plain and relationship between water level of the lake and GLs and CCs of piezometers located near the lake shore witch can present helpful information in GL and CC modeling. The results showed that the efficiency of ANFIS model was more than ANN model up to 30%. Reliability of ANFIS model is more than ANN model in both calibration and verification stages duo to the efficiency of fuzzy concept to overcome the uncertainties of the phenomenon.

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All of the financial institutions for gaining the best profit of their investment are always looking for the best investors, consulters, and borrowers. Besides, different sciences attempt to represent accurate methods for the separation of the customers. For that reason, sciences such as psychology, management sciences, mathematics, financial and etc…seek to achieve this aim. The subject that comes into consideration in this paper is the necessity of using the new methods in data mining in mixture with artificial intelligence techniques in order to deal with the sophisticated issue and answer to this question that do the usage of combined approach predict the customer rating well? If we want this process occurs, another dimension must not be forgotten that is the select measurement criteria and in this regard, the researcher has used judging journalist and non-parametric analysis in order to rank criteria thatfinally, select the number of indicatorsin order to implement the hybrid model will lead the researcher to answer this question: do the journalist’s ideas selection criteria result in a good prediction of the credit status of customers? The three indicators “age”, “previous relationship with the bank”, and “credit”to implement a fuzzy neural hybrid model are chosen. The model has been implemented in three layers and results suggest that 89.67% times the system can accurately estimate the proportion of customers provide ratings.To optimize the fuzzy neural network, the ant colony algorithm was used which results in improved performance of the model was 90.5%.

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Two-way slabs are one of the common structural systems. The benefits of such systems have led to extensive use of them in building construction. However, these systems are prone to pushing shear problem which causes sudden failure. There are lots of equations to predict punching shear of slabs. The main proportion of the existing equations are based on statistical results from previous experimental studies. However, these equations are approximate and have large errors. Therefore, more exact and reliable equations that can estimate punching shear capacity are desirable. The aim of this study is to propose an applicable method to predict punching shear in thin and thick slabs using artificial intelligence. For this reason Genetic Programming (GP) and Biogeography-Based Programming (BBP) are employed to find a relationship between punching shear and the corresponding effective parameters. GP that is inspired by natural genetic process, searches for an optimum population among the various probable ones. Two main operations of GP are crossover and mutation which make it possible to form new generations with better finesses. Unlike the GP, BBP is a Biogeography-Based Optimization (BBO) technique which is inspired by the geographical distribution in an ecosystem. BBP employs principles of biogeography to create computer programs. First, 267 experimental data is collected from the past studies. Next, using the aforementioned algorithms, a relationship to predict punching shear is proposed. To evaluate the error of prediction, several error functions including RMSE, MAE, MAPE, R, and OBJ are utilized. Matlab software is used to build the models of prediction. 10 different models are built and the one with the minimum error is selected. Based on the results, GP3 and BBP9 models could reach the best fitness. These models contain 3 sub-trees that use operators of plus, minus, multiplication, division, ln, sin, power 2, power 5 power 0.5, power 0.33, power 0.2, and power 0.25. Overall, the final tree includes several variables and integers, the variables are inputs of column dimension, effective depth, rebar ratio, compressive strength of concrete, and yielding strength of the rebars, and the output of punching shear capacity. The results of modeling are compared with recommended values of the ACI318 and EC2 codes. Comparison shows that code equations are scattered and therefore are not very reliable. Maximum error for both model and code equations occurs when the yielding strength of the rebars is low. Minimum estimation is related to GP and ACI codes with the ratio of 0.485 and 0.52, respectively which is due to very low thickness of the slab (41 to 55 mm). The maximum estimated shear belongs to ACI code in which the estimated value is two times the real one. Also, standard deviation of ACI values is about two times the others. Among the code equations, EC2 values yield more accurate results. However, GP and BBP models give much less mean error. Also, standard deviation of these methods is less than code values. In total, results show that the methods based on artificial intelligence are able to estimate pushing shear with around 2% error, compared to existing code equations which give 14-28% error.

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Extracting the required information from the design file is one of the main steps in the computer aided process planning. In previous methods of extracting machining features, various methods such as graph-based method, volume analysis method, logic rules method and other methods have been used. In all the previous methods, whether traditional methods or methods based on artificial intelligence, the input data to the machine feature identification system is the output information of a computer-aided design system. Converting the output information of a computer-aided design system to input data of a machining feature identification system is faced with limitations such as the variety of format and type of data arrangement, deleting some data from the design file due to geometric interference of features, slow extraction of features due to extensive information in the design file and the limitation of identifying different types of machining features by a unity feature identification system. In the present study, using artificial intelligence techniques based on deep learning, machining features are extracted directly from the two-dimensional image of a workpiece. The image may be prepared by a computer-aided design file, or it can be taken by a camera.