Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of the laminar combustion velocity in methane- air mixtures by means of deep learning algorithms

Prediction of the laminar combustion velocity in methane-air mixtures by means of deep learning algorithms The need to reduce environmental pollution has led to a search for alternative, low-emission fuels. In fact, internal combustion engines, are one of the main source of pollution in the urban environment. Methane is one of the best alternatives to the main and most common fossil fuels, because it is one of the fuels with the lowest CO2 and hydrocarbon emissions. In addition to being one of the cleanest fuels, it has remarkable chemical and physical properties, such as high anti-knocking, which would allow high efficiency in monovalent methane-fuelled heat engines with an high compression ratio. However, compared to petrol, methane burns slowly, which leads to a variation in efficiency and not complete stability cycle by cycle, reducing power and increasing fuel consumption. Low flame front propagation speed and poor burning capacity in poor mixture conditions can be improved by the addition of hydrogen, due to its higher burning speed. The aim of this thesis is to develop a deep learning algorithms for predicting the laminar speed of combustion in methane/air mixtures, providing as input the conditions of the mixture (pressure, temperature, percentage of EGR, equivalent ratio). The objective of this methodology is to see if it is possible to construct a neural network capable of understanding the non-linear relationships of the phenomenon under analysis, allowing a faster simulation, by virtue of the reduction of computational costs, but at the same time must be accurate, respecting what is the physics and kinetics of the combustion process. The results obtained by training our neural network separately on the combustion tables obtained with the main chemical kinetics mechanisms, i.e. Aramco 2.0 and GRImech 3.0, were also compared with the laminar flame speed experimental data available in the literature. Another important objective of this thesis is to continue research in the field of heat engines, in particular to generate combustion tables using LOGEresearch, software for simulating the chemical kinetics of the combustion process, for air/methane mixtures with and without the addition of hydrogen. The work focuses on finding the mixture dosage, with a value that differs from one, to observe how the value of the laminar speed varies, compared to the other input values, which are used to start the simulation.
A simple one-step PCR assay for SNP detection

Polymerase Chain Reaction (PCR) is a powerful tool to detect natural variation or experimentally introduced variation in research and clinical settings and a widely-used method for genotyping. Single nucleotide polymorphisms (SNP) detection is challenging by PCR as the variant and wild type alleles differ by only one nucleotide. Traditional methods to detect SNPs, including Sanger sequencing and commercial kits, are usually time-consuming. Here we describe a simple primer design strategy that enables specific variant detection through regular one-step PCR. The strategy employs the differential efficiency of genomic PCR using a primer that has a single mismatch with the chromosome that contains the SNP to be detected (typically the variant allele) versus two mismatches with the corresponding alternative allele (typically the wild type allele). To date, we have successfully employed this approach to detect more than 20 SNPs. The simplicity and robustness of the approach allows rapid application to legacy mutations as well as newly discovered or generated SNPs.
Unveiling the Ionic Diels–Alder Reactions within the Molecular Electron Density Theory

The ionic Diels–Alder (I-DA) reactions of a series of six iminium cations with cyclopentadiene have been studied within the Molecular Electron Density Theory (MEDT). The superelectrophilic character of iminium cations, ω > 8.20 eV, accounts for the high reactivity of these species participating in I-DA reactions. The activation energies are found to be between 13 and 20 kcal·mol−1 lower in energy than those associated with the corresponding Diels–Alder (DA) reactions of neutral imines. These reactions are low endo selective as a consequence of the cationic character of the TSs, but highly regioselective. Solvents have poor effects on the relative energies, and an unappreciable effect on the geometries. In acetonitrile, the activation energies increase slightly as a consequence of the better solvation of the iminium cations than the cationic TSs. Electron localization function (ELF) topological analysis of the bonding changes along the I-DA reactions shows that they are very similar to those in polar DA reactions. The present MEDT study establishes that the global electron density transfer (GEDT) taking place at the TSs of I-DA reactions, and not steric (Pauli) repulsions such as have been recently proposed, are responsible for the features of these types of DA reactions.
The Role of Cooperate Responsibility Image in Finnish Cacao Industry

Tampereen ammattikorkeakoulu Tampere University of Applied Sciences Degree Programme in Energy and Environmental Engineering TRAN DANG: The Role of Cooperate Responsibility Image in Finnish Cocoa Industry Study focus area: eco-efficiency and responsible sourcing Bachelor's thesis 42 pages, appendices 2 pages May 2021 Over centuries, cocoa products are widely consumed all over the world. The demand for this commodity is rising day by day. Cocoa farming is mostly conducted as a small family business and 70% of cocoa plantation are located in Africa. Make Chocolate Fair revealed on their website that even though cocoa deliver high revenue for many businesses around the world, cocoa farmer can only earn 6.6% out from this profit per ton of cocoa sold. Hence, different issues rose up from this poverty and mainly related to human rights and safety at work. Finland, on the other hand, has a cocoa consumption level above an average of 5.5kg per person per year, plus, Finland is part of the Nordic region, where the human rights and morality are highly defined and prioritized. The objective of the thesis was to find out whether cooperate socially responsible (CSR) image of Finnish cocoa firms would affect consumer choices. The thesis aimed to answer the three following questions: (1) How Finnish cocoa companies are implementing CSR into their businesses? (2) What are the preferences of Finnish consumers towards cocoa products? (3) Does CSR practices of a firm influences consumer choice in purchasing cocoa products? Literature review was used as the main method to address the topic. Several research papers, articles and books related to cocoa industry, cooperate socially responsible image both in Finland and in the whole world was revised and topic related information was filtered out and presented in the thesis. In addition, around 10 companies’ websites were also reviewed in order to get an overall picture on how cocoa businesses in Finland are coping with CSR issues. Without regards to some research limitation, it was revealed that consumer preferences towards purchasing cocoa products are affected by different reasons: color of packaging, taste, brand popularity, or even prices and promotion. Hence, it cannot be concluded that the social image of a cocoa brand would affect significantly to the consumer choice but only affect indirectly to the consumer preferences. In addition, the finding stated that most of the Finnish cocoa firms are well-aware of the social issue related to responsible sourcing and eco-efficiency. Hence, most of the Finnish businesses and firms are putting their efforts to minimize their impacts to the environment and supporting human rights. Due to some thesis limitations, further study suggestion are also mentioned. One of these suggestions was to conduct a quantitative research using surveys and interviews methods to get more insight into the Finnish consumer
Investigating the Aerodynamic Surface Roughness Length over Baghdad City Utilizing Remote Sensing and GIS Techniques

في هذه الدراسة تم حساب طول خشونة السطح (Zo), طول الازاحة الصفرية (Zd) وارتفاع عناصرالخشونة (ZH) باستخدام تطبيقات GIS. الفائدة العملية من هذه الدراسة هي تصنيف تطور بغداد ,اختيار الأماكن المناسبة لتركيب توربينات الرياح, التخطيط الحضري, إيجاد معدلات الاضطراب والتلوث وغيرها. تم تقدير طول خشونة السطح (Zo) فوق مدينة بغداد بناءا على بيانات سرعة الرياح التي تم الحصول عليها من محطة أرصاد جوية آلية مثبتة في الجامعة المستنصرية ، بيانات صور الأقمار الصناعية نموذج الارتفاع الرقمي (DEM) ، ونموذج السطح الرقمي (DSM) ، باستخدام تقنيات الاستشعار عن بعد. تم تقسيم منطقة الدراسة إلى 15 بلدية (الرشيد ، المنصور ، الشعلة ، الكرادة ، الشعب ، الأعظمية ، الصدر 2 ، الصدر 1 ، الرصافة ، الغدير ، بغداد الجديدة ، الكرخ ، الكاظمية ، المنطقة الخضراء ، الدورة). أظهرت النتائج إلى أن الاختلافات في Zo تعتمد بشدة على طول الإزاحة الصفرية (Zd) وارتفاع عنصر الخشونة (ZH) وسرعة الرياح. وأظهرت نتائج البحث أن منطقة بغداد الجديدة لديها أكبر قيمة Zo بحدود (.430 م) واقل قيمة الى Zo كانت في بلدية الرشيد وبحدود (0.19 م). حين بلغ معدل (Zo) لمدينة بغداد (0.32 م).
PF-DRAM: A Precharge-Free DRAM Structure

Although DRAM capacity and bandwidth have increased sharply by the advances in technology and standards, its latency and energy per access have remained almost constant in recent generations. The main portion of DRAM power/energy is dissipated by Read, Write, and Refresh operations, all initiated by a Precharge phase. Precharge phase not only imposes a large amount of energy consumption, but also increases the delay of closing a row in a memory block to open another one. By reduction of row-hit rate in recent workloads, especially in multi-core systems, precharge rate increases which exacerbates DRAM power dissipation and access latency. This work proposes a novel DRAM structure, called Precharge-Free DRAM (PF-DRAM), that eliminates the Precharge phase of DRAM. PF-DRAM uses the charge on bitlines from the previous Activation phase, as the starting point for the next Activation. The difference between PF-DRAM and conventional DRAM structure is limited to precharge and equalizer circuitry and simple modifications in sense amplifier, which are all limited to subarray level. PF-DRAM is compatible with the mainstream JEDEC memory standards like DDRx and HBM, with minimum modifications in memory controller. Furthermore, almost all of the previously proposed power/energy reduction techniques in DRAM are still applicable to PF-DRAM for further improvement. Our experimental results on a 8GB memory system running SPEC CPU2017 and PARSEC2.1 workloads show an average of 35.3% memory power consumption reduction (up to 54.2%) achieved by the system using PF-DRAM with respect to the system using conventional DRAM. Moreover, the overall performance is improved by 8.6%, in average (up to 24.3%). According to our analysis, all such improvements are achieved for less than 9% area overhead.
Prognostic value of cardiac sympathetic nerve activity evaluated by [I]m-iodobenzylguanidine imaging in patients with ST-segment elevation myocardial infarction

Background Many studies have shown that cardiac sympathetic nerve activity evaluated by [I] m-iodobenzylguanidine ([I]MIBG) scintigraphic study during a stable period is useful for determining the prognosis of patients with chronic heart failure. Objective To examine whether results of this imaging method performed 3 weeks after the onset of ST-segment elevation myocardial infarction (STEMI) are a reliable prognostic marker for patients with STEMI. Methods The study analysed findings for 213 consecutive patients with STEMI undergoing [I]MIBG scintigraphy. The left ventricular (LV) end-diastolic and end-systolic volume and LV ejection fraction (EF) were determined by left ventriculography or echocardiography 3 weeks after the onset of STEMI. The delayed total defect score, heart-to-mediastinum ratio and washout rate (WR) were also determined from [I]MIBG scintigraphy at the same time. Results Of the 213 patients, 46 experienced major adverse cardiac events (MACE) during the study. The median follow-up period was 982 days. Patients were divided into an event-free group (n1⁄4167; 78.4%) and a MACE group (n1⁄446; 21.6%). The LV and [I]MIBG scintigraphic parameters in the event-free group were better than those in the MACE group. Multivariate Cox regression analyses revealed that WR was a significant predictor of MACE along with oral nicorandil (ATPsensitive potassium channel opener) treatment and undergoing percutaneous coronary intervention. On KaplaneMeier analysis, the event-free rate of patients with a WR<40% was significantly higher than that in patients with a WR$40% (p<0.001). Even when confined to patients with LVEF>45%, WR was a predictor of MACE, pump failure death, cardiac death and progression of heart failure in patients with STEMI. Conclusion WR evaluated by [I]MIBG scintigraphy 3 weeks after the onset of STEMI is a significant predictor of MACE in patients with STEMI, independent
PolarDB Serverless: A Cloud Native Database for Disaggregated Data Centers

\beginabstract The trend in the DBMS market is to migrate to the cloud for elasticity, high availability, and lower costs. The traditional, monolithic database architecture is difficult to meet these requirements. With the development of high-speed network and new memory technologies, disaggregated data center has become a reality: it decouples various components from monolithic servers into separated resource pools (e.g., compute, memory, and storage) and connects them through a high-speed network. The next generation cloud native databases should be designed for disaggregated data centers. In this paper, we describe the novel architecture of \name, which follows thedisaggregation design paradigm: the CPU resource on compute nodes is decoupled from remote memory pool and storage pool. Each resource pool grows or shrinks independently, providing \revon-demand provisoning at multiple dimensions while improving reliability. We also design our system to mitigate the inherent penalty brought by resource disaggregation, and introduce optimizations such as optimistic locking and index awared prefetching. Compared to the architecture that uses local resources, \name achieves better dynamic resource provisioning capabilities and 5.3 times faster failure recovery speed, while achieving comparable performance. \endabstract
Phoneme Based Respiratory Analysis of Read Speech

Recent work shows that it is possible to use deep learning techniques to sense the speaker's respiratory parameters directly from a speech signal. This can be a beneficial option for future telehealth services. In this paper, we dive deeper and study how respiratory effort depends on the linguistic content of the speech utterance. This is obtained by analysis of respiratory belt sensor data and phoneme-aligned speech data. The results show, for example, that the respiratory effort was highest for fricatives, compared to other broad phonetic classes, and especially high for the glottal consonants. The insights may help to develop more efficient protocols for respiratory health monitoring in telehealth applications.
The Trade-Off between Hours of Paid Employment and Time Assistance to Elderly Parents at Midlife

Although the family has traditionally been the primary caregiver for the frail elderly, the rising labor force participation rates of married women may interfere with their historical caregiving responsibilities. This paper explores time transfers to elderly parents and their impact on labor supply for persons at midlife. We estimate simultaneous panel data models of annual hours of paid work and the provision of time assistance to parents for a sample of men and women ages 53 to 65 in the Health and Retirement Study. Our results indicate that time help to parents substantially reduces labor supply for both women and men.
Speech Mode Classification using the Fusion of CNNs and LSTM Networks

Speech Mode Classification using the Fusion of Machine Learning and Deep Learning based Classifiers Pratyusha Chowdary Vakkantula Speech mode classification is an area that has not been as widely explored in the field of sound classification as others such as environmental sounds, music genre, and speaker identification. But what is speech mode? While mode is defined as the way or the manner in which something occurs or is expressed or done, speech mode is defined as the style in which the speech is delivered by a person. There are some reports on speech mode classification using conventional methods, such as whispering and talking using a normal phonetic sound. However, to the best of our knowledge, deep learning-based methods have not been reported in the open literature for the aforementioned classification scenario. Specifically, in this work we assess the performance of image-based classification algorithms on this challenging speech mode classification problem, including the usage of pre-trained deep neural networks, namely AlexNet, ResNet18 and SqueezeNet. Thus, we compare the classification efficiency of a set of deep learning-based classifiers, while we also assess the impact of different 2D image representations (spectrograms, mel-spectrograms, and their image-based fusion) on classification accuracy. These representations are used as input to the networks after being generated from the original audio signals. Next, we compare the accuracy of the DL-based classifies to a set of machine learning (ML) ones that use as their inputs Mel-Frequency Cepstral Coefficients (MFCCs) features. Then, after determining the most efficient sampling rate for our classification problem (i.e. 32kHz), we study the performance of our proposed method of combining CNN with LSTM (Long Short-Term Memory) networks. For this purpose, we use the features extracted from the deep networks of the previous step. We conclude our study by evaluating the role of sampling rates on classification accuracy by generating two sets of 2D image representations – one with 32kHz and the other with 16kHz sampling. Experimental results show that after cross validation the accuracy of DL-based approaches is 15% higher than ML ones, with SqueezeNet yielding an accuracy of more than 91% at 32kHz, whether we use transfer learning, feature-level fusion or score-level fusion (92.5%). Our proposed method using LSTMs further increased that accuracy by more than 3%, resulting in an average accuracy of 95.7%.
High-throughput measurement of mass at the attogram scale for nanoparticles in solution using Suspended Nanochannel Resonators

In this Master’s Degree Thesis work I present the technological improvements of the Suspended Nanochannel Resonator (SNR) buoyant mass sensor technology developed in collaboration between the Manalis lab at the Koch Institute for Cancer Research – MIT and the CEA/Leti research center in Grenoble, France. ∼ 100nm thick piezoresistive gauges have been embedded in the hollow cantilevers of the SNR devices to allow a fully electrical piezoresistive readout mechanism of the beam’s resonance frequency. This achievement made it possible to design arrays of SNR sensors that are fluidically connected either in series or in parallel to perform different functions. Here, I compare the piezoresistive readout mechanism to the previously used optical lever scheme showing that they have equivalent performances: the optimized design of the piezoresistive gauge element has no negative impact on the SNR noise and a limit of detection in the attogram order of magnitude can be achieved. Then, I describe the parallel SNR array technology and the way I characterized its performances and compared them to the well-established single-resonator SNR. The results show an order of magnitude improvement in the measurement throughput of nanoparticles with buoyant mass down to 10ag, in aqueous solutions at concentrations as low as 108 particles/mL; polydisperse samples of particles with different sizes and mass can be measured using this technology and less than 20nm resolution in size difference can be achieved for gold nanoparticles. The simultaneous implementation of piezoresistive gauges for electrical readout and of parallel SNR arrays effectively lowers the measurement time for this technology making it competitive with other established techniques for mass sensing in solution: future industrial applications are envisioned.
Hardware Architecture and Software Stack for PIM Based on Commercial DRAM Technology : Industrial Product

Emerging applications such as deep neural network demand high off-chip memory bandwidth. However, under stringent physical constraints of chip packages and system boards, it becomes very expensive to further increase the bandwidth of off-chip memory. Besides, transferring data across the memory hierarchy constitutes a large fraction of total energy consumption of systems, and the fraction has steadily increased with the stagnant technology scaling and poor data reuse characteristics of such emerging applications. To cost-effectively increase the bandwidth and energy efficiency, researchers began to reconsider the past processing-in-memory (PIM) architectures and advance them further, especially exploiting recent integration technologies such as 2.5D/3D stacking. Albeit the recent advances, no major memory manufacturer has developed even a proof-of-concept silicon yet, not to mention a product. This is because the past PIM architectures often require changes in host processors and/or application code which memory manufacturers cannot easily govern. In this paper, elegantly tackling the aforementioned challenges, we propose an innovative yet practical PIM architecture. To demonstrate its practicality and effectiveness at the system level, we implement it with a 20nm DRAM technology, integrate it with an unmodified commercial processor, develop the necessary software stack, and run existing applications without changing their source code. Our evaluation at the system level shows that our PIM improves the performance of memory-bound neural network kernels and applications by 11.2× and 3.5×, respectively. Atop the performance improvement, PIM also reduces the energy per bit transfer by 3.5×, and the overall energy efficiency of the system running the applications by 3.2×.
NN-Baton: DNN Workload Orchestration and Chiplet Granularity Exploration for Multichip Accelerators

The revolution of machine learning poses an unprecedented demand for computation resources, urging more transistors on a single monolithic chip, which is not sustainable in the Post-Moore era. The multichip integration with small functional dies, called chiplets, can reduce the manufacturing cost, improve the fabrication yield, and achieve die-level reuse for different system scales. DNN workload mapping and hardware design space exploration on such multichip systems are critical, but missing in the current stage.This work provides a hierarchical and analytical framework to describe the DNN mapping on a multichip accelerator and analyze the communication overhead. Based on this framework, we propose an automatic tool called NN-Baton with a pre-design flow and a post-design flow. The pre-design flow aims to guide the chiplet granularity exploration with given area and performance budgets for the target workload. The post-design flow focuses on the workload orchestration on different computation levels -package, chiplet, and core - in the hierarchy. Compared to Simba, NN-Baton generates mapping strategies that save 22.5%∼44% energy under the same computation and memory configurations.The architecture exploration demonstrates that area is a decisive factor for the chiplet granularity. For a 2048-MAC system under a 2 mm2 chiplet area constraint, the 4-chiplet implementation with 4 cores and 16 lanes of 8-size vector-MAC is always the top-pick computation allocation across several benchmarks. In contrast, the optimal memory allocation policy in the hierarchy typically depends on the neural network models.