Miracle-dz/UniFAR-FTUs huggingface dataset:
EKOLOJİK AYAK İZİNİN KAVRAMSAL İÇERİĞİ VE AKDENİZ ÜNİVERSİTESİ İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ’NDE BİREYSEL EKOLOJİK AYAK İZİ HESAPLAMASI

Sanayilesme, kentlesme, teknolojik ilerlemeler ve hizli nufus artisi nedeniyle ekolojik sorunlar 1980’lerden beri tum dunyanin temel problemlerinden biri olmustur. Ekolojik ayak izi hesaplamalari ekolojik bilinci artirmak icin ekolojik yikimin farkli boyutlarina dikkat cekmektedir. Ekolojik ayak izi, dogal degerlerin surdurulebilmesi icin gerekli olan biyolojik uretken alan miktarini ortaya koymaktadir. Arastirmada bireysel ekolojik ayak izlerinin hesaplanmasi amaciyla Akdeniz Universitesi Iktisadi ve Idari Bilimler Fakultesi ogrencileri ve calisanlarina uygulanan anketin sonuclari sunulmustur. Yapilan calismada toplam 1886 bireyden olusan arastirma evreni icinden 241 kisiye bireysel ekolojik ayak izi anketi uygulanmistir. Arastirmada cinsiyet, yas, gelir, meslek, tuketim kalemleri bazinda hesaplanan yiyecek, atik, konut, ulasim ayak izleri T Testi ve Kruskal Wallis H Testi uygulanarak analiz edilmistir. Yapilan analizler sonucunda cinsiyete gore ekolojik ayak izi oranlarinin degismedigi, gelir arttikca tuketimin artmasina paralel olarak toplam ayak izinin arttigi, mulk ve otomobil sahibi olanlarin ve gelir duzeyi diger meslek gruplarina gore daha yuksek olan ogretim elemanlarinin ayak izi buyukluklerinin daha fazla oldugu saptanmistir
Predicting Heart Disease Using Machine Learning Techniques on Electronic Health Records Data

heart disease is a prevalent cause of mortality worldwide, and the ability to identify and prevent this ailment at an early stage is crucial for enhancing patient outcomes. Recently, researchers have focused on utilizing Machine-Learning (ML) techniques to predict risk factors and the occurrence of heart disease. This study aims to identify the optimal parameters for accurately predicting the occurrence of heart disease through the fine-tuning of ML models' parameters. This study incorporated a blend of datasets from Cleveland, Hungary, Switzerland, and Long Beach V, which consisted of patients' medical histories and laboratory test outcomes. The collected data were employed to predict the occurrence of heart disease through diverse ML algorithms, such as KNN, decision tree, and SVM. Bayesian optimization was also utilized to fine-tune the hyperparameters of the ML algorithms for heart disease prediction. The findings demonstrated that the ML models achieved high accuracy in predicting the occurrence of heart disease, with the optimized KNN model achieving the highest accuracy rate of 100%.
‘Our favourite science’: Lord Bute and James Parkinson searching for a Theory of the Earth

Abstract John Stuart, the third earl of Bute and the British Prime Minister from 1762 to 1763, and the apothecary surgeon James Parkinson both amassed large and important geological collections; both believed in the biblical Deluge; both admired the work of Jean André de Luc; and both were fascinated by the study of geology. Each sought a theory that would explain the geological phenomena they observed but which also allowed them to maintain their religious integrity. They were men of their time, struggling to come to terms with a new science that challenged their strongly held religious beliefs. Bute's Observations on the Natural History of the Earth, never published, provides us with a snapshot of his thinking about prevailing theories of the Earth. He dismissed all except those that fitted the geological facts as understood at the time, but was nevertheless unable to progress from a rigid belief in the biblical Flood having been a miracle. Parkinson's Organic Remains of a Former World reveals a man fully conversant with contemporary geological ideas being propounded elsewhere in Europe. Also highly religious, Parkinson oscillated between his deeply held beliefs and the contradictory evidence provided by the fossils he held in his hand.
More Accuracy Less Fingerprints: Wi-Fi Indoor Localization via Generative Adversarial Networks

High-cost site survey is one of the bottlenecks of Wi-Fi fingerprinting indoor localization. Rather than leverage inertial sensors or floorplan, we propose LocGAN to generate virtual fingerprints (VFPs) with only a small number of labeled (with locations) and yet a large number of unlabeled ones. To this end, LocGAN is a semi-supervised deep generative model consisting of TriReg, encoder, generator, and discriminator. As a tri-net based regressor, TriReg provides pseudo-labels for unlabeled fingerprints. Under a generative adversarial network (GAN) framework, LocGAN is able to learn underlying distributions of fingerprints from both labeled and unlabeled ones thus generating high-accuracy VFPs. We also design several effective training strategies to further improve its performance. To evaluate LocGAN, we prototype a Wi-Fi indoor localization system based on it. Extensive experiments are carried out in real-world scenarios with areas over 8,200m2. The experiment results demonstrate that compared with the state-of-the-art counterparts, LocGAN achieves more accuracy with less labeled fingerprints, reducing the cost of site survey significantly.
A Study of Effective Strategies for Personal Development and Success for Software Engineers

Our goal was to read the book “Bounce: Mozart, Federer, Picasso, Beckham, and the Science of Success” by Matthew Syed, and study the strategies for success in order to find out whether any of the strategies could be used for success and personal growth of software engineers. To achieve this goal, we conducted a comprehensive literature review and performed an experiment. The aim of the experiment was to test one of the strategies for success. This strategy talks about the importance of feedback on individuals' approach to challenging tasks, and claims that a growth mindset can be developed through deliberate feedback. As a result of our research, we provide insights into the factors that contribute to success in software engineering field, and suggest strategies that can help software engineers achieve their goals and maximize their potential.
Asymptotic Analysis and Truncated Backpropagation for the Unrolled Primal-Dual Algorithm

Algorithm unrolling combines the advantages of model based optimization with the flexibility of data-based methods by adapting a parameterized objective to a distribution of problem instances from a finite sample from that distribution. At inference time, a fixed number of iterations of a suitable optimization algorithm is used to make predictions on unseen data. To compute gradients for learning, the last iterate is differentiated with respect to the parameters by backpropagation schemes that get expensive when the number of unrolled iterations gets large. Therefore, only few unrolled iterations are used which compromises the claimed interpretability in terms of the underlying optimization objective. In this work, we consider convex objective functions, derive an explicit limit of the parameter gradients for a large number of unrolled iterations, derive a training procedure that is computationally tractable and retains interpretability, and show the effectiveness of the method using the example of speech dequantization.
Synthetic-to-Real Dataset Transfer Learning for Non-Intrusive Load Monitoring

The deep neural networks (DNNs) have been widely adopted in non-intrusive load monitoring (NILM) to analyze appliance-level power usage based on aggregate power consumption. Recently, transfer learning has gained attention in NILM as it improves the generalization of DNN models, particularly when transferring knowledge from one real-world dataset to another. However, due to the significant cost and time required for real-world data collection, synthetic datasets have emerged as a viable alternative for NILM research. Nevertheless, it remains unclear whether a well-trained DNN model on synthetic data can be effectively transferred to a real dataset, and which types of networks perform well when using transfer learning strategies. In this paper, we explore these crucial issues by investigating the generalization of different DNNs between synthetic and real datasets using two transfer learning schemes: direct transfer learning and transfer learning with strategy. Our results demonstrate that the sequence-to-point network shows promise for the transfer strategy from a synthetic dataset to a real dataset, and its performance relies on the similarity of probability mass functions between the two datasets.
STLformer: Exploit STL Decomposition and Rank Correlation for Time Series Forecasting

The challenge of time series forecasting has been the focus of research in recent years, with Transformer-based models using various self-attention mechanisms to uncover long-range dependencies. However, complex trends and nonlinear serial dependencies presented in some specific datasets may not always be captured properly. To address these issues, we present STLformer, a novel Transformer-based model that utilizes an STL decomposition architecture and the rank correlation function to improve long-term time series forecasting. STLformer outperforms four state-of-the-art Transformers and two RNN models across multiple datasets and forecasting horizons.
Geochemical features and tectonic significance of granitic gneiss of Laguigangri metamorphic core complexes in southern Tibet

The granitic gneiss of Laguigangri metamorphic core complexes in southern Tibet is characterized by high SiO2 and K2O, low Fe2O3, MgO and MnO, with alumina saturation index (A/CNK) of 1.07～1.19 and peraluminous minerals (such as muscovite and garnet). These features suggest that the granitic gneiss belongs to the high-K calci-alkaline peraluminous rocks. It is obvious that the chondrite-normalized REE shows right inclined patterns with obvious negative Eu abnormity, simultaneously, the primitive mantle normalized trace element spidergrams are characterized by enriched LILE (such as Rb, Th and U) and depleted HFSE (such as Sr and Nb), as well as evidently low contents of Ba and Hf, which are compatible with the characteristics of crustal derived S-type granites. The initial values of 87Sr/86Sr(ISr) are in a range of 0.709～0.7306, the eSr(t) in a range of 80.65～379 and eNd(t=514Ma) values of -7.13～-8.97, without significant correlation between eNd(t) and ISr. The ratios of (206Pb/204Pb)t(=18.1062～18.8085), (207Pb/204Pb)t(=15.7～15.8) and (208Pb/204Pb)t(=37.5～38.2) also reveal crustal Pb isotopic features. Therefore, the granitic magma is originated from partially melting of clay and sandstone sedimentary rocks. With the U-Pb isotopic age of 514Ma, we believe that these rocks in Laguigangri formed during the transition period from syncollision (compressional) to post collision (extensional environment) in Pan African orogenic events in the north Himalayan. This magmatic activity may play an important role in formation of the Laguigangri metamorphic core complex.
Multiscale Soil Salinity Assessment at the Southern Margin of the Venice Lagoon, Italy

Saltwater intrusion affects many coastlands around the world contaminating fresh-groundwater and decreasing soil quality. In order to manage saline soils one should understand the spatiotemporal dynamics of salinity in the soil profile and its spatial variability at field scale. In the last decades, soil and pore-water salinity have been assessed using geophysical techniques, most commonly with the use of apparent electrical conductivity (ECa) measurements. At point-scale, pore-water salinity can be estimated once its relationship with ECa, soil properties, and water content is understood. Moreover, most sensors for water content estimation normally provide biased readings in saline conditions and in soil with high clay and organic carbon contents. At field-scale proximal-sensing can be used to characterize large portions of land from a relatively small number of soil samples. Sometimes, characterizing salinity is however not sufficient to understand crop yield spatial variability, which can be also influenced by other soil properties. Understanding the influence of salinity and other soil properties on crop productivity can be useful in the identification of areas that can be managed site-specifically. The general aim of this dissertation is to evaluate some sensor-based methodologies for monitoring and characterizing salinity and other related soil properties both at point- and field-scale. In particular, at point-scale the dissertation will deal with the issues regarding the use of capacitive-resistive technology for water content and pore-water salinity estimation. At field-scale some methodologies will be proposed in order to characterize the spatial variability of salinity and other soil properties influencing maize (Zea mais L.) yield using soil proximal-sensing. All the material presented in this manuscript regard the soils of an area affected by saltwater intrusion located at the southern edge of the Venice Lagoon (Italy). The dissertation is structured in five chapters. The first one includes a review on commonly used methodologies for point- and field-scale salinity assessment. An overview on the environmental issues concerning the coastland at the southern margin of the Venice Lagoon is also presented. The second chapter deals with the calibration of a low-cost capacitance-resistance probe for simultaneous monitoring of soil water content and salinity. In the third chapter an ECa-directed soil sampling scheme optimization procedure is proposed. The forth chapter analyzes maize yield as a function of soil chemical and physical properties and investigates on the use of soil-proximal sensing correlated to soil spatial variability for site-specific management units. The final chapter presents the general conclusions of the work.
Clinics in Surgery

Background: Patients with brain tumor experience multiple complications, including impaired consciousness and delayed extubation of endotracheal tubes, after craniotomy. The purpose of the present study was to determine the effects of foot reflexology on the time of full consciousness recovery and weaning from mechanical ventilation in patients with brain tumor after craniotomy. Methods: This single blind clinical trial was performed on 60 craniotomy patients. The participants were assigned to the control and intervention groups using a block randomization method. Two checklists, including the time of full consciousness and withdrawal from the ventilator, were completed for the two groups after the intervention. In the intervention group, in addition to routine care for these patients, the first author performed foot reflexology for 30 min (15 min for each foot) twice a day, in the morning and evening, for a week. The control group received routine care for patients with brain tumor and craniotomy. Data was analyzed using SPSS version 16 software. Results: The results showed that there was no significant difference in terms of demographic characteristics between the two groups (p>0.05). The results indicated that the mean scores regarding the time needed for regaining full consciousness and weaning from mechanical ventilation were significantly lower in the intervention group compared to the control group (p<0.001). Conclusion: The results showed that foot reflexology was effective in reducing the time needed for full consciousness recovery and weaning from mechanical ventilation in craniotomy patients.
Deciphering records of geomagnetic reversals

Polarity reversals of the geomagnetic field are a major feature of the Earth's dynamo. Questions remain regarding the dynamical processes that give rise to reversals and the properties of the geomagnetic field during a polarity transition. A large number of paleomagnetic reversal records have been acquired during the past 50 years in order to better constrain the structure and geometry of the transitional field. In addition, over the past two decades, numerical dynamo simulations have also provided insights into the reversal mechanism. Yet despite the large paleomagnetic database, controversial interpretations of records of the transitional field persist; they result from two characteristics inherent to all reversals, both of which are detrimental to an ambiguous analysis. On the one hand, the reversal process is rapid and requires adequate temporal resolution. On the other hand, weak field intensities during a reversal can affect the fidelity of magnetic recording in sedimentary records. This paper is aimed at reviewing critically the main reversal features derived from paleomagnetic records and at analyzing some of these features in light of numerical simulations. We discuss in detail the fidelity of the signal extracted from paleomagnetic records and pay special attention to their resolution with respect to the timing and mechanisms involved in the magnetization process. Records from marine sediments dominate the database. They give rise to transitional field models that often lead to overinterpret the data. Consequently, we attempt to separate robust results (and their subsequent interpretations) from those that do not stand on a strong observational footing. Finally, we discuss new avenues that should favor progress to better characterize and understand transitional field behavior.
Frequency Fluctuations in European Isolated Systems: A Review on Standards, Available Recordings and Grid Code Requirements

Frequency control in the Continental Europe (CE) synchronous area will become more challenging in the future with decreasing levels of rotational inertia, especially after system splits. For isolated power systems, e.g. on remote islands, the European standard EN 50160 specifies much larger possible frequency fluctuations than for large synchronous areas. In this contribution, we locate 28 specific examples of European isolated grids. By taking into account available frequency recordings and grid codes, we identify which of those grids are actually operated in a wider frequency range than the CE system (47.5-51.5 Hz). As a result, we suggest that following the example of isolated systems and allowing for larger dynamic frequency excursions might be a feasible approach to handle future system splits.
Provisioning of Disaster Recovery with Terraform and Kubernetes: A Case Study on Software Defect Prediction

Natural disasters and device failures can damage organizational infrastructure and data, giving substantial issues for modern firms that rely on information systems and technology, especially cloud-based solutions. To maintain firm operations, information and technology systems must be protected. The conventional catastrophic recovery technique trades speed, complexity, and cost. Infrastructure as a Code (IaC) may improve complex disaster recovery operations in firms. This study analyzes Infrastructure as Code as a potential solution to cloud disaster recovery issues. The study examines how Terraform and Kubernetes affect cloud-based disaster recovery procedures and durations by simulating a disaster event. The findings of the study indicate that the implementation of Terraform for constructing intricate infrastructure, such as a deployment pipeline, has a notable impact on the reduction of required steps and the time required. The number of steps is significantly reduced to a single step and reduce the time required by 235% when compared to manual manufacturing. Terraform demonstrates its ability to optimise disaster recovery. The utilization of Kubernetes has been found to significantly enhance the efficiency of software deployment. The use of Kubernetes resulting in a notable reduction in deployment time by approximately 530%. This improvement in deployment speed is expected to have implications for the software recovery process in the event of a disaster. The duration of catastrophe recovery using Terraform and Kubernetes is 190% shorter compared to the duration of manual recovery. It reduces the use of unutilized surplus infrastructure resources and makes rebuilding easier, but it takes longer than standby infrastructure.
PROCEEDINGS, INDONESIAN PETROLEUM ASSOCIATION Thirty-First Annual Convention and Exhibition, May 2007 CRETACEOUS TO LATE MIOCENE STRATIGRAPHIC AND TECTONIC EVOLUTION OF WEST JAVA

Palaeogeographic maps for intervals between the Cretaceous and Late Miocene illustrate the complex evolution of West Java. Basement is of Mesozoic age and in West and Central Java there are ophiolitic and arc rocks accreted to the margin of Sundaland in the Late Cretaceous. The oldest Cenozoic rocks in West Java are Middle Eocene formations in the Ciletuh Bay area that formed in quite different settings. There are volcanogenic turbidites and breccias containing abundant basaltic material that we suggest are deep water deposits, associated with the onset of subduction, formed close to a new arc or in its forearc. Nearby are quartz-rich sandstones deposited predominantly in a shallow marine shelf edge environment interpreted to be derived from basement highs. We assign these rocks to different formations and their present juxtaposition is suggested to be due to thrusting. We interpret there to have been a large southerly prograding delta system in SW Java during the Late Eocene. There is a considerable thickness of quartzrich sandstones, forming an overall shallowing-up sequence, sourced from the north and probably derived from Sundaland. The Oligocene of West Java includes terrestrial quartz-rich sandstones, reefal and foraminiferal limestones and volcanogenic sediments deposited in fluvial to deeper water marine environments. The Early Miocene saw an important phase of explosive arc volcanism in south Java. By the Middle Miocene volcanism had diminished or ceased, allowing carbonates to be deposited on the arc rocks. In the Late Miocene volcanism resumed further to the north resulting in a new phase of volcanogenic turbidite deposition.
Performance Evaluation of High Efficient based Soft Switching Step Down DC-DC Converter for Battery Charging in Electric Vehicles

This topology deals with ZVS soft switching based synchronous step-down DC-DC converter during switching transitions. The implementation of an active unidirectional auxiliary circuit allows for the achievement of soft switching conditions in the synchronous leg switches. As a result, this effectively reduces the stress or burden on the switches, thus ameliorating overall converter efficiency. This proposed topology can be used for battery charging in EV applications. The paper provides an intricate analysis and explanation of the operational aspects of the proposed topology. Additionally, the results are assessed via Simulink/MATLAB platform and verified experimentally.
A Single-Source 13-level Switched-Capacitor Multilevel Inverter with a Lower Switch Count

This paper presents a 13-level switched capacitor multilevel inverter based on a single source. The design does not require an H-bridge to generate polarity, resulting in a reduction in maximum switch voltage. The structure can generate 6 times more voltage than the input voltage using the switched-capacitor technique. Furthermore, the structure provides more capacitor charging possibilities than others which reduces the capacitor size as compared to others. The working principle, the analysis of voltage stress, and the capacitor selection procedure are all thoroughly discussed. An extensive comparison study demonstrates that, in comparison to similar inverters, the suggested structure requires fewer components and offers a higher boosting ability. An extensive simulation study considering various loads has been conducted to validate the performances of the proposed inverter.