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The objective was to investigate the embryo morphokinitics using a time-lapse monitoring (TLM) system and assessment of clinical outcomes following intracytoplasmic sperm injection (ICSI) with zona pellucida (ZP)-bound sperm selection and conventional methods. A total of 371 metaphase II (MII) oocytes from 50 ICSI cycles were studied. Sibling oocytes were randomly divided into control (n = 199) and ZP-bound group (n = 172). All resulting zygotes were cultured and monitored in the TLM system up to Day 3 after ICSI. Fertilization rate, early embryo development, and clinical outcomes were evaluated. No significant differences were found in fertilization rate, time-lapse qualitative and quantitative measures, pronuclear fading time (PNF) t2, t3, t4, t5, t6, and t7 (times of cleavage to 2, 3, 4, 5, 6, and 7 cells), respectively. However, the t8 (time of cleavage to eight cells) and cc3 (duration of third cell cycle) revealed a significant difference between control and ZP-bound groups (p < .05). A significant difference between the two groups (p < .05) in the rates of Grade A embryos (according to Basile algorithm), chemical pregnancy, clinical pregnancy, and implantation was observed. Sperm selection using biological materials, such as ZP, improved both embryo quality and pregnancy outcomes, despite not affecting the early embryo development and morphokinetic parameters up to t8. This prospective randomized sibling oocyte trial was registered in October 2020 to January 2022 (IRCT20200705048021N1).

In the PDF of this article, the pages were incorrectly numbered as ‘2303–2327’ when it should have been ‘3637–3661’. The page range was found to be just correct in the HTML version of the article. The original article has been corrected., Software Engineering

The copyright holder in the original publication of this article was incorrect. The original article has been corrected., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Software Engineering

Code comments are a key resource for information about software artefacts. Depending on the use case, only some types of comments are useful. Thus, automatic approaches to clas-sify these comments have been proposed. In this work, we address this need by proposing, STACC, a set of SentenceTransformers- based binary classifiers. These lightweight classifiers are trained and tested on the NLBSE Code Comment Classification tool competition dataset, and surpass the baseline by a significant margin, achieving an average Fl score of 0.74 against the baseline of 0.31, which is an improvement of 139%. A replication package, as well as the models themselves, are publicly available., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Software Engineering, Software Technology

In recent years, Large Language Models (LLMs) have gained significant popularity due to their ability to generate human-like text and their potential applications in various fields, such as Software Engineering. LLMs for Code are commonly trained on large unsanitized corpora of source code scraped from the Internet. The content of these datasets is memorized and emitted by the models, often in a verbatim manner. In this work, we will discuss the security, privacy, and licensing implications of memorization. We argue why the use of copyleft code to train LLMs is a legal and ethical dilemma. Finally, we provide four actionable recommendations to address this issue., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Software Engineering

We report on the organization and results of the second edition of the tool competition from the International Workshop on Natural Language-based Software Engineering (NLBSE'23). As in the prior edition, we organized the competition on automated issue report classification, with a larger dataset. This year, we featured an extra competition on au-tomated code comment classification. In this tool competition edition, five teams submitted multiple classification models to automatically classify issue reports and code comments. The submitted models were fine-tuned and evaluated on a benchmark dataset of 1.4 million issue reports or 6.7 thousand code comments, respectively. The goal of the competition was to improve the classification performance of the baseline models that we provided. This paper reports details of the competition, including the rules, the teams and contestant models, and the ranking of models based on their average classification performance across issue report and code comment types., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Software Engineering

In today’s environment of growing class sizes due to the prevalence of online and e-learning systems, providing one-to-one instruction and feedback has become a challenging task for teachers. Anyhow, the dialectical integration of instruction and assessment into a seamless and dynamic activity can provide a continuous flow of assessment information for teachers to boost and individualize learning. In this regard, adaptive learning technology is one way to facilitate teacher-supported learning and personalize curriculum and learning experiences. This study aimed to investigate the potential of an adaptive Computerized Dynamic Assessment (C-DA) tool applicable as a language diagnostician and assistant. The study tried to get insight into 75 Iranian EFL learners’ listening development by focusing on the learning potential exhibited through learners’ assessment and the degree of internalization of mediation. To achieve these, a C-DA tutor including two dynamic listening comprehension tests, each comprising 20 items, arranged in the order of difficulty was developed. The test takers unable to answer an item correctly were provided with graduated hints for different comprehension- and production-type items and the overall difficulty level of the test was adapted to the test takers’ proficiency level. In order to have a full diagnosis of each individual’s listening development, the adaptive C-DA automatically generated five test scores on each learner’s performance: actual (unmediated) score, mediated score, gain score, Learning Potential Score (LPS), and transfer score. The results of paired-sample t-tests revealed a significant development from the actual to the mediated scores. Furthermore, the LPSs indicated that the tutor was capable of revealing learners’ potential for learning. Moreover, learners with high LPS gained a higher mean for transfer scores followed by transfer scores of medium and low levels. The results of Mann-Whitney tests revealed a significant dif, Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Software Engineering

Transformer-based pre-trained models have recently achieved great results in solving many software engineering tasks including automatic code completion which is a staple in a developer’s toolkit. While many have striven to improve the code-understanding abilities of such models, the opposite – making the code easier to understand – has not been properly investigated. In this study, we aim to answer whether making code easier to understand through using contextual data improves the performance of pre-trained code language models for the task of code completion. We consider type annotations and comments as two common forms of additional contextual information that often help developers understand code better. For the experiments, we study code completion in two granularity levels; token and line completion and take three recent and large-scale language models for source code: UniXcoder, CodeGPT, and InCoder with five evaluation metrics. Finally, we perform the Wilcoxon Signed Rank test to gauge significance and measure the effect size. Contrary to our expectations, all models perform better if type annotations are removed (albeit the effect sizes are small). For comments, we find that the models perform better in the presence of multi-line comments (again with small effect sizes). Based on our observations, we recommend making proper design choices when training, fine-tuning, or simply selecting such models given the intended data and application. Better evaluations and multimodal techniques can also be further investigated to improve the practicality and accuracy of auto-completions., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Software Engineering, Software Technology

Binary reverse engineering is used to understand and analyse programs for which the source code is unavailable. Decompilers can help, transforming opaque binaries into a more readable source code-like representation. Still, reverse engineering is difficult and costly, involving considering effort in labelling code with helpful summaries. While the automated summarisation of decompiled code can help reverse engineers understand and analyse binaries, current work mainly focuses on summarising source code, and no suitable dataset exists for this task. In this work, we extend large pre-trained language models of source code to summarise de-compiled binary functions. Further-more, we investigate the impact of input and data properties on the performance of such models. Our approach consists of two main components; the data and the model. We first build CAPYBARA, a dataset of 214K decompiled function-documentation pairs across various compiler optimisations. We extend CAPYBARA further by removing identifiers, and deduplicating the data. Next, we fine-tune the CodeT5 base model with CAPYBARA to create BinT5. BinT5 achieves the state-of-the-art BLEU-4 score of 60.83, 58.82 and, 44.21 for summarising source, decompiled, and obfuscated decompiled code, respectively. This indicates that these models can be extended to decompiled binaries successfully. Finally, we found that the performance of BinT5 is not heavily dependent on the dataset size and compiler optimisation level. We recommend future research to further investigate transferring knowledge when working with less expressive input formats such as stripped binaries., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Software Engineering, Software Technology

Software-related platforms such as GitHub and Stack Overflow, have enabled their users to collaboratively label software entities with a form of metadata called topics. Tagging software repositories with relevant topics can be exploited for facilitating various downstream tasks. For instance, a correct and complete set of topics assigned to a repository can increase its visibility. Consequently, this improves the outcome of tasks such as browsing, searching, navigation, and organization of repositories. Unfortunately, assigned topics are usually highly noisy, and some repositories do not have well-assigned topics. Thus, there have been efforts on recommending topics for software projects, however, the semantic relationships among these topics have not been exploited so far. In this work, we propose two recommender models for tagging software projects that incorporate the semantic relationship among topics. Our approach has two main phases; (1) we first take a collaborative approach to curate a dataset of quality topics specifically for the domain of software engineering and development. We also enrich this data with the semantic relationships among these topics and encapsulate them in a knowledge graph we call SED-KGraph. Then, (2) we build two recommender systems; The first one operates only based on the list of original topics assigned to a repository and the relationships specified in our knowledge graph. The second predictive model, however, assumes there are no topics available for a repository, hence it proceeds to predict the relevant topics based on both textual information of a software project (such as its README file), and SED-KGraph. We built SED-KGraph in a crowd-sourced project with 170 contributors from both academia and industry. Through their contributions, we constructed SED-KGraph with 2,234 carefully evaluated relationships among 863 community-curated topics. Regarding the recommenders’ performance, the experiment results indicate that our sol, Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Software Engineering

Previous work has shown that Large Language Models are susceptible to so-called data extraction attacks. This allows an attacker to extract a sample that was contained in the training data, which has massive privacy implications. The construction of data extraction attacks is challenging, current attacks are quite inefficient, and there exists a significant gap in the extraction capabilities of untargeted attacks and memorization. Thus, targeted attacks are proposed, which identify if a given sample from the training data, is extractable from a model. In this work, we apply a targeted data extraction attack to the SATML2023 Language Model Training Data Extraction Challenge. We apply a two-step approach. In the first step, we maximise the recall of the model and are able to extract the suffix for 69% of the samples. In the second step, we use a classifier-based Membership Inference Attack on the generations. Our AutoSklearn classifier achieves a precision of 0.841. The full approach reaches a score of 0.405 recall at a 10% false positive rate, which is an improvement of 34% over the baseline of 0.301., Software Engineering, Software Technology

Code completion is an essential feature of IDEs, yet current auto-completers are restricted to either grammar-based or NLP-based single token completions. Both approaches have significant draw-backs: grammar-based autocompletion is restricted in dynamically-typed language environments, whereas NLP-based autocompleters struggle to understand the semantics of the programming language and the developer's code context. In this work, we present CodeFill, a language model for autocompletion that combines learned structure and naming information. Using a parallel Transformer architecture and multi-task learning, CodeFill consumes sequences of source code token names and their equivalent AST token types. Uniquely, CodeFill is trained both for single-token and multi-token (statement) prediction, which enables it to learn long-range dependencies among grammatical and naming elements. We train CodeFill on two datasets, consisting of 29M and 425M lines of code, respectively. To make the evaluation more realistic, we develop a method to automatically infer points in the source code at which completion matters. We compare CodeFill against four baselines and two state-of-the-art models, GPT-C and TravTrans+. CodeFill surpasses all baselines in single token prediction (MRR: 70.9% vs. 66.2% and 67.8%) and outperforms the state of the art for multi-token prediction (ROUGE-L: 63.7% vs. 52.4% and 59.2%, for n=4 tokens). We publicly release our source code and datasets., Software Engineering, Software Technology

Users use Issue Tracking Systems to keep track and manage issue reports in their repositories. An issue is a rich source of software information that contains different reports including a problem, a request for new features, or merely a question about the software product. As the number of these issues increases, it becomes harder to manage them manually. Thus, automatic approaches are proposed to help facilitate the management of issue reports. This paper describes CatIss, an automatic Categorizer of Issue reports which is built upon the Transformer-based pre-trained RoBERTa model. CatIss classifies issue reports into three main categories of Bug report, Enhancement/feature request, and Question. First, the datasets provided for the NLBSE tool competition are cleaned and preprocessed. Then, the pre-trained RoBERTa model is fine-tuned on the preprocessed dataset. Evaluating CatIss on about 80 thousand issue reports from GitHub, indicates that it performs very well surpassing the competition baseline, TicketTagger, and achieving 87.2% F1-score (micro average). Additionally, as CatIss is trained on a wide set of repositories, it is a generic prediction model, hence applicable for any unseen software project or projects with little historical data. Scripts for cleaning the datasets, training CatIss and evaluating the model are publicly available., Software Engineering

NLP-based models have been increasingly incorporated to address SE problems. These models are either employed in the SE domain with little to no change, or they are greatly tailored to source code and its unique characteristics. Many of these approaches are considered to be outperforming or complementing existing solutions. However, an important question arises here: Are these models evaluated fairly and consistently in the SE community?. To answer this question, we reviewed how NLP-based models for SE problems are being evaluated by researchers. The findings indicate that currently there is no consistent and widely-accepted protocol for the evaluation of these models. While different aspects of the same task are being assessed in different studies, metrics are defined based on custom choices, rather than a system, and finally, answers are collected and interpreted case by case. Consequently, there is a dire need to provide a methodological way of evaluating NLP-based models to have a consistent assessment and preserve the possibility of fair and efficient comparison., Software Engineering

Code completion is an essential feature of IDEs, yet current auto-completers are restricted to either grammar-based or NLP-based single token completions. Both approaches have significant draw-backs: grammar-based autocompletion is restricted in dynamically-typed language environments, whereas NLP-based autocompleters struggle to understand the semantics of the programming language and the developer's code context. In this work, we present CodeFill, a language model for autocompletion that combines learned structure and naming information. Using a parallel Transformer architecture and multi-task learning, CodeFill consumes sequences of source code token names and their equivalent AST token types. Uniquely, CodeFill is trained both for single-token and multi-token (statement) prediction, which enables it to learn long-range dependencies among grammatical and naming elements. We train CodeFill on two datasets, consisting of 29M and 425M lines of code, respectively. To make the evaluation more realistic, we develop a method to automatically infer points in the source code at which completion matters. We compare CodeFill against four baselines and two state-of-the-art models, GPT-C and TravTrans+. CodeFill surpasses all baselines in single token prediction (MRR: 70.9% vs. 66.2% and 67.8%) and outperforms the state of the art for multi-token prediction (ROUGE-L: 63.7% vs. 52.4% and 59.2%, for n=4 tokens). We publicly release our source code and datasets., Software Engineering, Software Technology

The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an Interval Type-2 fuzzy image processing expert system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with four steps of preprocessing, segmentation, feature extraction and approximate reasoning is used in inference engine module to enhance the quality of MRI scans, segment them into desired regions, extract the required features, and finally diagnose and differentiate Astrocytomas. The performance of this system is evaluated using 100 MRI scans and the results show good improvement in diagnosing and differentiating Astrocytomas., IEEE

on time diagnosis of spinal cord problems is essential because of patient pain and cost of treatment. To alleviate this hazard, in this research a fuzzy rule-based expert system is proposed to diagnosis spinal cord problems and clarify whether a MRI scan is necessary or not. The knowledge representation of this system is provided from high level, based on lifestyle of the patient and historical data about his/her problem and some of the clinical examination. It organized to 5 sub problem, called Red flag, Spinal stenosis, Scoliosis Lordosis kyphosis, and Mechanical and Spinal disc herniation. Spinal disc herniation is composed of 2 sub problem including Lumbar and cervical. Inference engine of the system is a combination of backward and forward chaining. To reduce diagnosis time, it switches from forward to backward mode and vice versa based on direct and indirect approach. Results of this system is the degree of each problem the patient has and it reduces complexity in image processing for analysis the M.R.I of the patient., IEEE

The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an interval Type-2 fuzzy automated tumor detection system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with three steps of preprocessing, segmentation and feature extraction, and approximate reasoning is used in inference engine module to enhance the quality of MRI scans, segment them into desired regions, extract the required features, and finally diagnose and differentiate Astrocytomas. However, brain tumors have different characteristics in different planes, so considering one plane of patient's MRI scan may cause inaccurate results. Therefore, in the developed system, several consecutive planes are processed. The performance of this system is evaluated using 95 MRI scans and the results show good improvement in diagnosing and differentiating Astrocytomas.

Fuzzy functions are used to identify the structure of system models and reasoning with them. Fuzzy functions can be determined by any function identification method such as Least Square Estimates (LSE), Maximum Likelihood Estimates (MLE) or Support Vector Machine Estimates (SVM). However, estimating fuzzy functions using LSE method is structurally a new and unique approach for determining fuzzy functions. By using this approach, there is no need to know or to develop an in-depth understanding of essential concepts for developing and using the membership functions and selecting the t-norms, co-norms and implication operators. Furthermore, there is no need to apply fuzzification and defuzzification methods. The goal of this paper is to improve the Type-2 fuzzy image processing expert system based on Type-2 fuzzy function to diagnose the Astrocytoma tumors (most important category of brain tumors) in T-1-weighted MR Images with contrast. This expert system has four steps, Pre-processing, Segmentation, Feature extraction and Approximate reasoning. The focus of this paper is to improve the last step, Approximate reasoning step, by using fuzzy function strategy instead of fuzzy rule-base approach. The results show that Type-2 fuzzy function approach requires less computation steps with less computational complexity and could provide better results., IEEE, IEEE Computat Intelligence Soc, Int Neural Network Soc, Evolut Program Soc, IET

The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an Interval Type-2 fuzzy image processing expert system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with four steps of preprocessing, segmentation, feature extraction and approximate reasoning is used in inference engine module to enhance the quality of MRI scans, segment them into desired regions, extract the required features, and finally diagnose and differentiate Astrocytomas. The performance of this system is evaluated using 100 MRI scans and the results show good improvement in diagnosing and differentiating Astrocytomas., IEEE

on time diagnosis of spinal cord problems is essential because of patient pain and cost of treatment. To alleviate this hazard, in this research a fuzzy rule-based expert system is proposed to diagnosis spinal cord problems and clarify whether a MRI scan is necessary or not. The knowledge representation of this system is provided from high level, based on lifestyle of the patient and historical data about his/her problem and some of the clinical examination. It organized to 5 sub problem, called Red flag, Spinal stenosis, Scoliosis Lordosis kyphosis, and Mechanical and Spinal disc herniation. Spinal disc herniation is composed of 2 sub problem including Lumbar and cervical. Inference engine of the system is a combination of backward and forward chaining. To reduce diagnosis time, it switches from forward to backward mode and vice versa based on direct and indirect approach. Results of this system is the degree of each problem the patient has and it reduces complexity in image processing for analysis the M.R.I of the patient., IEEE

The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an interval Type-2 fuzzy automated tumor detection system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with three steps of preprocessing, segmentation and feature extraction, and approximate reasoning is used in inference engine module to enhance the quality of MRI scans, segment them into desired regions, extract the required features, and finally diagnose and differentiate Astrocytomas. However, brain tumors have different characteristics in different planes, so considering one plane of patient's MRI scan may cause inaccurate results. Therefore, in the developed system, several consecutive planes are processed. The performance of this system is evaluated using 95 MRI scans and the results show good improvement in diagnosing and differentiating Astrocytomas.

Fuzzy functions are used to identify the structure of system models and reasoning with them. Fuzzy functions can be determined by any function identification method such as Least Square Estimates (LSE), Maximum Likelihood Estimates (MLE) or Support Vector Machine Estimates (SVM). However, estimating fuzzy functions using LSE method is structurally a new and unique approach for determining fuzzy functions. By using this approach, there is no need to know or to develop an in-depth understanding of essential concepts for developing and using the membership functions and selecting the t-norms, co-norms and implication operators. Furthermore, there is no need to apply fuzzification and defuzzification methods. The goal of this paper is to improve the Type-2 fuzzy image processing expert system based on Type-2 fuzzy function to diagnose the Astrocytoma tumors (most important category of brain tumors) in T-1-weighted MR Images with contrast. This expert system has four steps, Pre-processing, Segmentation, Feature extraction and Approximate reasoning. The focus of this paper is to improve the last step, Approximate reasoning step, by using fuzzy function strategy instead of fuzzy rule-base approach. The results show that Type-2 fuzzy function approach requires less computation steps with less computational complexity and could provide better results., IEEE, IEEE Computat Intelligence Soc, Int Neural Network Soc, Evolut Program Soc, IET

The majority of people have experienced pain in their low back or neck in their lives. In this paper, a type-2 fuzzy rule-based expert system is presented for diagnosing the spinal cord disorders. The interval type-2 fuzzy logic system permits us to handle the high uncertainty of diagnosing the type of disorder and its severity. The spinal cord disorders are studied in five categories using historical data and clinical symptoms of the patients. The main novelty of this paper lies in presentation of the interval type-2 fuzzy hybrid rule-based system, which is a combination of the forward and backward chaining approaches in its inference engine and avoids unnecessary medical questions. Use of parametric operations for fuzzy calculations increases the robustness of the system and the compatibility of the diagnosis with a wide range of physicians' diagnosis. The outputs of the system are comprised of type of disorder, location, and severity as well as the necessity of taking an M.R. Image. A comparison of the performance of the developed system with the expert shows an acceptable accuracy of the system in diagnosing the disorders and determining the necessity of the M.R. Image.

The majority of people have experienced pain in their low back or neck in their lives. In this paper, a type-2 fuzzy rule-based expert system is presented for diagnosing the spinal cord disorders. The interval type-2 fuzzy logic system permits us to handle the high uncertainty of diagnosing the type of disorder and its severity. The spinal cord disorders are studied in five categories using historical data and clinical symptoms of the patients. The main novelty of this paper lies in presentation of the interval type-2 fuzzy hybrid rule-based system, which is a combination of the forward and backward chaining approaches in its inference engine and avoids unnecessary medical questions. Use of parametric operations for fuzzy calculations increases the robustness of the system and the compatibility of the diagnosis with a wide range of physicians' diagnosis. The outputs of the system are comprised of type of disorder, location, and severity as well as the necessity of taking an M.R. Image. A comparison of the performance of the developed system with the expert shows an acceptable accuracy of the system in diagnosing the disorders and determining the necessity of the M.R. Image.

Objective: The peroxisome proliferator-activated receptor-gamma coactivator-1 alpha 1 (PGC-1 alpha 1) regulates genes involved in energy metabolism. Increasing adipose tissue energy expenditure through PGC-1 alpha 1 activation is potentially beneficial for systemic metabolism. Pharmacological PGC-1 alpha 1 activators could be valuable tools in the fight against obesity and metabolic disease. Finding such compounds has been challenging partly because PGC-1 alpha 1 is a transcriptional coactivator with no known ligand-binding properties. While, PGC-1 alpha 1 activation is regulated by several mechanisms, protein stabilization is a crucial limiting step due to its short half-life under unstimulated conditions. Methods: We designed a cell-based high-throughput screening system to identify PGC-1 alpha 1 protein stabilizers. Positive hits were tested for their ability to induce endogenous PGC-1 alpha 1 protein accumulation and activate target gene expression in brown adipocytes. Select compounds were analyzed for their effects on global gene expression and cellular respiration in adipocytes. Results: Among 7,040 compounds screened, we highlight four small molecules with high activity as measured by: PGC-1 alpha 1 protein accumulation, target gene expression, and uncoupled mitochondrial respiration in brown adipocytes. Conclusions: We identify compounds that induce PGC-1 alpha 1 protein accumulation and show that this increases uncoupled respiration in brown adipocytes. This screening platform establishes the foundation for a new class of therapeutics with potential use in obesity and associated disorders.

Due to environmental issues and the upward trend of fossil fuel prices, the study of renewable energy (RE) based generation and their effects on the electrical system has become an important part of the government's energy policies and university projects. In RE generation, as solar photovoltaic (PV) systems are modular, silent, and transportable and demonstrate ease of installation, they have attracted a greater amount of attention specifically in those areas which receive considerable average solar radiation per day such as Malaysia. However, connecting solar PV farms to the grid like any other distributed generation (DG) units poses serious issues which arise in the distribution network. This paper presents a novel fault location algorithm based on the recording of short circuit power values at the primary substation of unbalanced radial distribution networks including PV systems. The recorded values are evaluated by a designed and tuned multi-layer feed forward neural network and the fault distances from the source are estimated accordingly. In order to highlight the accuracy of the presented method, the scenario is also repeated by recording the peak values of short circuit current which have been mostly used in the published intelligent fault location studies and the obtained results via two different values are compared with each other. The results reveal that the presented algorithm using a small scale input set is able to precisely locate different fault types in the unbalanced distribution networks including DG units.

Due to environmental issues and the upward trend of fossil fuel prices, the study of renewable energy (RE) based generation and their effects on the electrical system has become an important part of the government's energy policies and university projects. In RE generation, as solar photovoltaic (PV) systems are modular, silent, and transportable and demonstrate ease of installation, they have attracted a greater amount of attention specifically in those areas which receive considerable average solar radiation per day such as Malaysia. However, connecting solar PV farms to the grid like any other distributed generation (DG) units poses serious issues which arise in the distribution network. This paper presents a novel fault location algorithm based on the recording of short circuit power values at the primary substation of unbalanced radial distribution networks including PV systems. The recorded values are evaluated by a designed and tuned multi-layer feed forward neural network and the fault distances from the source are estimated accordingly. In order to highlight the accuracy of the presented method, the scenario is also repeated by recording the peak values of short circuit current which have been mostly used in the published intelligent fault location studies and the obtained results via two different values are compared with each other. The results reveal that the presented algorithm using a small scale input set is able to precisely locate different fault types in the unbalanced distribution networks including DG units.

To evaluate lightning current using measured electromagnetic fields, an inverse procedure algorithm is proposed in this article, where the variation of return velocity stroke velocity is considered as opposed to previous methods where the velocity is set at a constant value. The proposed algorithm considers all electromagnetic fields components directly in the time domain without needing to apply any extra conversions. Moreover, it can support the wide range of current models based on the general form of engineering current models. The proposed method is validated using a sample of triggered lightning data and measured electromagnetic fields at two stations with different distances from the lightning channel. The results illustrate that the proposed method could estimate the behavior of current along the channel and can thus be used for preparing a data bank to record lightning current parameters of a wide range of lightning occurrences based on measured fields.

In this paper, the effect of radiation component of electric field on the total electric field at different distances with respect to lightning channel is considered. Results showed by approximating the total electric field with the radiation component at far and intermediate distances, some percentages of electric fields at different time periods will be neglected and this percentage will be higher when decreasing the distance from lightning channel. Whilst the most common inverse procedure algorithms are based on field's radiation component and the total field as input data, this paper proposed the same procedure that based on measured field with the consideration on the effect of radiation component on the total field in the calculations. The results on the prediction of channel base current illustrated that the proposed method is in very good agreement with real lightning channel base current compared to the corresponding predicted current based on existing methods.

The return stroke velocity is an important factor for evaluation of lightning electromagnetic fields. Measurements showed that the return stroke velocity is varied at different heights along lightning channel while it is usually entered into field calculations with a constant value in previous studies. This paper presents the analytical electromagnetic fields expressions due to vertical lightning channel where velocity profile along channel is considered. The proposed fields expressions can be used to estimate electromagnetic fields directly in the time domain (without needing to apply any extra conversion to Frequency domain) whereas they are based on Heidler current function and can support widely used engineering current models. Likewise, the proposed field expressions are applied on a typical measured profile of velocity and also a function of velocity profile and the evaluated fields are compared to the corresponding simulated fields based on constant value of velocity and the results are discussed accordingly.

In this paper, an inverse procedure algorithm is proposed in the time domain to evaluate lightning return stroke currents along a lightning channel using measured magnetic flux density at an observation point while the current velocity along a lightning channel is assumed to be a height dependent variable. The proposed method considers all field components and it can evaluate the full shape of currents and the current velocity at different heights along a lightning channel. Moreover, a sample of measured magnetic flux density from a triggered lightning experiment is applied to the proposed algorithm and the evaluated currents and current velocities are validated using a measured channel base current and magnetic flux density at another observation point.

The lightning return stroke current is an important parameter for considering the effect of lightning on power lines. In this study, a numerical method is proposed to evaluate the return stroke current based on measured electromagnetic fields at an observation point in the time domain. The proposed method considers all field components and the full wave shape of the current without the use of a special current model as a basic assumption compared to previous methods. Furthermore, the proposed algorithm is validated using measured fields obtained from a triggered lightning experiment. The results show a good agreement between the simulated field based on the evaluated currents from the proposed method and the corresponding measured field at a remote observation point. The proposed method can determine current wave shapes related to a greater number of lightning occurrences compared to the direct measurement of the current.

Introduction: Informed consent is a critical issue especially in conducting clinical trials that expose human life to medical or surgical interventions. It necessitates a long and complex process through which the participant ispresented with all potential favorable and non-favorable consequences upon getting enrolled in the study.Review: The process of taking informed consent is wellunderstoodin developed countries, with every effort taken to enhance and maintain the autonomy of patients and their right to make an informed choice of whether to participate or not. This may not be the case in thedeveloping world. The information given to patients before the trial might not be properly developed and presented, an issue that can result in serious threat to the decision-making process. On the other hand, investigators should remember that enrolling people into a trial withno potential benefit for themselves cannot be considered ethical. In the current debate, we aim to address the issue of how respectfully and ethically clinical research trials can be done on human subjects and what we can do to enhance the practice in an ethical context.Conclusion: Development of a system through which we could warrant all rights of study participants in all cases around the world seems far from view. However, if we are in doubt about the ethics of a clinical trial, we can ask ourselves: “what would we do, if we were in the same position our patients are in now?”