Influence of Ethical Business Practices of Islam on the Formation of Turkish Social Business Networks

This paper is composed of a short discussion on the influence of Islamic business practices on the Islamically oriented emerging business groups that challenged the oligarchic secular business framework in Turkey. The paper addresses conceptual/theoretical aspects of Islamic Business practices, the main characteristics of conservative business groups, and formation of Islamically conservative non-governmental business organizations. Turkish business organizations are effective in establishing social business networks that open a wide area to do business for their members, mainly small and medium-sized company owners. Discussing the historical development of these business groups, the study argues that Islamic moral values, work ethics, morality, solidarity, and networking are very influential in Turkish business life

VBART: The Turkish LLM

We present VBART, the first Turkish sequence-to-sequence Large Language Models (LLMs) pre-trained on a large corpus from scratch. VBART are compact LLMs based on good ideas leveraged from BART and mBART models and come in two sizes, Large and XLarge. Fine-tuned VBART models surpass the prior state-of-the-art results in abstractive text summarization, title generation, text paraphrasing, question answering and question generation tasks. They allow fine-tuning for future text generation tasks and datasets, carving a new path for Turkish Natural Language Processing (NLP) research. Our work shows that having a pre-trained LLM for Turkish outperforms up to 3x multilingual models, improving existing results and providing efficient models for training and inference. Moreover, we show that our monolingual tokenizer is up to 11x more efficient than multilingual tokenizers. Last but not least, we introduce a method to enlarge an existing pre-trained LLM and question the relevancy of Chinchilla Scaling Law to sequence-to-sequence masked language models. Our fine-tuned models, tokenizer and cleaned vngrs-web-corpus of 135 GB are publicly available at huggingface.co/vngrs-ai

Digital Transformation for Sustainable Future - Agriculture 4.0: A review

In the last few years, while the COVID-19 pandemic affects food supply chains around the world, the agriculture sector also has faced many global problems, such as global warming, environmental pollution, climate change, and weather disasters. It has known that technological opportunities are available for human beings to get out of these predicaments, solving the interconnections between food-water-energy- climate nexus, and achieving agricultural transformation from traditional to digital.The aim of this review is to gain holistic solutions in a systematic view, based on water-energy-food resources to agricultural digital transformation that will play role in sustainable development. The transition from primitive to digital is given with road maps covering agricultural and industrial revolutions at four stages on timeline. Digital agriculture combined under precision agriculture and Agriculture 4.0 are handled based on domains covering monitoring, control, prediction, and logistics. Digital technologies are explained with application examples such as the Internet of Things (IoT), cloud computing, big data, artificial intelligence, decision support systems, etc. Wearable sensor technologies, real-time monitoring systems tracking whole conditions of animals in livestock, the IoT-based irrigation and fertilization systems that help enhance the efficiency of irrigation processes and minimize water and fertilizer losses in agricultural fields and greenhouses, blockchain-based electronic agriculture, and solutions based on drones and robotics that reduce herbicide and pesticide use are handled systematically. Moreover, renewable energy technologies to be provided synergy between technologies such as agrivoltaics and aquavoltaics combining food and energy production in rural are explained, besides solar-powered pivot and drip irrigation systems and environmental monitoring systems. As a result, for a sustainable future, technological innovations that increase crop productivity and improve crop quality, protect the environment, provide efficient resource use and decrease input costs can help us facing in agriculture of today overwhelm many the economic, social, and environmental challenges

Effect of Different Steel Fiber Type and Content in Flexural Behavior of Ultra High Performance Fiber Reinforced Concrete

In the research study, the effect of different fiber contents to flexural behavior of the Ultra-High Performance Fiber Reinforced Concrete (UHPFRC) was investigated experimentally. Various prismatic beam specimens with a dimension of 100×100×400 mm including two types of end-hooked steel fibers (aspect ratios: 30/0.55 and 60/0.75) in macro forms and one short straight steel fiber (aspect ratio: 13/0.16) in micro form were produced. The beam specimens corresponding to a total of 18 mixtures having two different volume fractions (1% and 1.5%) were subjected to series of four-point bending tests in accordance with the ASTM standard C 1609. The experimental test results were discussed in terms of the cracking patterns, flexural strengths and toughness (energy absorption ability). In addition, a parametric research was conducted to ensure an appropriate homogenous UHPFRC mixture as well as good workability for the steel fiber volume fraction of 1.0%. Hence the prism and cubic samples were produced by modified of the composition of matrix mixtures (i.e. aggregate, water/binder, cement, superplasticizer). The performance of mixtures was evaluated in terms of the slump flow, T 500, compressive strength and workability. It is apparent from the test results, the use of micro steel fiber significantly improves the flexural performance of the UHPFRC comparing to that of the macro form. It was also noted that the fiber type is decisive in characteristic of the load- deflection curve while the volume content amplifies it with an increasing trend after the first cracking region. When evaluating all UHPFRC matrixes, some of the mixtures under consideration ensured good fiber distribution, workability as well as target compressive strength

Expression of vascular endothelial growth factor and transforming growth factor alpha in rat testis during chronic renal failure

Introduction. Vascular endothelial growth factor (VEGF) is known to influence testis function. Transforming growth factor alpha (TGF-α) is expressed in the postnatal testis, and has been demonstrated to stimulate testis development. Systemic diseases such as chronic renal failure (CRF) interfere with hypothalamic-pituitary-go­nadal axis, which may cause defective steroidogenesis and gonadal functions. The aim of this study was to inve­stigate the expression and localization of VEGF and TGF-α in testicular tissues of experimental CRF model. Material and methods. Experimental CRF was induced in rats by the resection of more than 85% of renal mass. The expression of VEGF and TGF-α in testicular tissues were assessed by immunohistochemistry on paraffin sections of control, CRF-nondialysed and CRF-dialysed rats. Results. The microscopic evaluation of the testicular structure showed that CRF did not affect testicular histology. Immunohistochemical evaluation showed that VEGF was expressed in the cytoplasm of primary and secondary spermatocyte series as well as the early spermatids. Staining intensity was lower in sperma­tocytes going through the first meiotic division. TGF-α was expressed in the nuclei of spermatogonia and primary spermatocytes with stronger staining intensity in spermatogonia. The intensity of VEGF staining was similar in control and experimental animals, however, TGF-α expression was lower in the CRF group.Conclusions. The continuous expression of VEGF in spermatocytes and spermatids suggests that the applied model of CRF does not directly disrupt morphology of seminiferous epithelium, thus also spermiogenesis. However, difference between control rats and CRF group in TGF-α immunopositivity, which was localised in spermatogonial mitosis step, may suggest the interference of CRF with early stages of spermatogenesis.

Hybrid deep feature generation for appropriate face mask use detection

Mask usage is one of the most important precautions to limit the spread of COVID-19. Therefore, hygiene rules enforce the correct use of face coverings. Automated mask usage classification might be used to improve compliance monitoring. This study deals with the problem of inappropriate mask use. To address that problem, 2075 face mask usage images were collected. The individual images were labeled as either mask, no masked, or improper mask. Based on these labels, the following three cases were created: Case 1: mask versus no mask versus improper mask, Case 2: mask versus no mask + improper mask, and Case 3: mask versus no mask. This data was used to train and test a hybrid deep feature-based masked face classification model. The presented method comprises of three primary stages: (i) pre-trained ResNet101 and DenseNet201 were used as feature generators; each of these generators extracted 1000 features from an image; (ii) the most discriminative features were selected using an improved RelieF selector; and (iii) the chosen features were used to train and test a support vector machine classifier. That resulting model attained 95.95%, 97.49%, and 100.0% classification accuracy rates on Case 1, Case 2, and Case 3, respectively. Having achieved these high accuracy values indicates that the proposed model is fit for a practical trial to detect appropriate face mask use in real time

Super Neurons

Self-Organized Operational Neural Networks (Self-ONNs) have recently been proposed as new-generation neural network models with nonlinear learning units, i.e., the generative neurons that yield an elegant level of diversity; however, like its predecessor, conventional Convolutional Neural Networks (CNNs), they still have a common drawback: localized (fixed) kernel operations. This severely limits the receptive field and information flow between layers and thus brings the necessity for deep and complex models. It is highly desired to improve the receptive field size without increasing the kernel dimensions. This requires a significant upgrade over the generative neurons to achieve the “non-localized kernel operations” for each connection between consecutive layers. In this article, we present superior (generative) neuron models (or super neurons in short) that allow random or learnable kernel shifts and thus can increase the receptive field size of each connection. The kernel localization process varies among the two super-neuron models. The first model assumes randomly localized kernels within a range and the second one learns (optimizes) the kernel locations during training. An extensive set of comparative evaluations against conventional and deformable convolutional, along with the generative neurons demonstrates that super neurons can empower Self-ONNs to achieve a superior learning and generalization capability with a minimal computational complexity burden. PyTorch implementation of Self-ONNs with super-neurons is now publically shared.Peer reviewe

Applicability of pressure retarded osmosis power generation technology in Istanbul

In this study, the applicability of pressure retarded osmosis power generation was investigated in order to meet the electricity demand in Turkey. Pressure retarded osmosis (PRO) is a method that converting salinity gradients to power using a semi-permeable membrane against an applied pressure and PRO is one of the promising candidates to reduce fossil fuel dependency. In PRO, water is transported from a low concentrated feed solution to a high-concentrated draw solution. According to the literature findings, in order to produce 1MW of electricity 1m3/s fresh water flow is needed. Turkey is surrounded on three sides by water and has a big potential to develop this technology. Riva River is investigated in the scope this study. Currently Turkey’s total installed power capacity reached 85.200 MW at the end of 2017.Calculations of PRO power generation reveals that it is possible to generate 25,45 MW, If using 5% of total river flow

Most complicated lock pattern-based seismological signal framework for automated earthquake detection

BACKGROUND : Seismic signals record earthquakes and also noise from different sources. The influence of noise makes it difficult to interpret seismograph signals correctly. This study aims to develop a computationally lightweight, accurate, and explainable machine learning model for the automated detection of seismogram signals that could serve as an effective warning system for earthquake prediction. MATERIAL AND METHOD : We developed a handcrafted model for earthquake detection using a balanced dataset of 5001 earthquakes and 5001 non-earthquake signal samples. The model included multilevel feature extraction, selectorbased feature selection, classification, and post-processing. Input signals were decomposed using tunable Q wave transform and fed to a statistical and textural feature extractor based on the most complicated lock pattern (MCLP). Four feature selectors were used to choose the most valuable features for the support vector machine classifier. Additionally, voted vectors were generated using iterative hard majority voting. Finally, the best model was chosen using a greedy algorithm. RESULTS : The presented self-organized MCLP-based feature engineering model yielded 96.82% classification accuracy with 10-fold cross-validation using the seismic signal dataset. CONCLUSIONS : Our model attained high seismological signal detection performance comparable with more computationally expensive deep learning models. Our handcrafted explainable feature engineering model is computationally less expensive and can be easily implemented. Furthermore, we have introduced a competitive feature engineering model to the deep learning models for the seismic signal classification model.The South African National Library and Information Consortium (SANLiC).https://www.elsevier.com/locate/jagam2024Electrical, Electronic and Computer EngineeringSDG-09: Industry, innovation and infrastructureSDG-13:Climate actio