Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognizing human activity using deep learning with WiFi CSI and filtering

We are living in the era of the Internet of Things, where it is easy to find network access points (APs). APs could be useful for more than just connecting to the Internet. The presence of a human between two APs, as well as human behavior, causes a change in the waveform of a WiFi signal. In a previous research, we have explained how changes in waveforms affect the channel state information of the signal and how machine learning can utilize that information to recognize and predict human behavior. In this paper, we explain the limitation of the last paper and provide a solution for improving the limited performance, which is preprocessing. Kalman filtering improved the training accuracy by 2%. In conclusion, the overall Kalman filter is good for suppressing sudden signal errors such as those from hardware malfunctioning.
Evolution of PET Detectors and Event Positioning Algorithms Using Monolithic Scintillation Crystals

The use of monolithic scintillator-based photon detectors in positron emission tomography (PET) has emerged as an attractive alternative to traditional pixelated array designs. Monolithic-based detector designs employ the scintillation light distribution (LD) shape to provide a single 3-D photon interaction position per event, enabling high spatial resolution throughout the crystal volume. Since there are no intercrystal gaps, monolithic designs provide higher intrinsic detection efficiency compared to pixelated designs. However, in order to make the monolithic detector design practical for clinical PET systems, some major drawbacks need to be addressed, such as the time-consuming and complex calibration procedures to obtain precise spatial and timing information. This article gives a historical review of monolithic-based PET detectors, a description of their main advantages and challenges, describes the state-of-the-art, including their use in current commercial system, and ends with a future prospective.
Min-Max Backpropagation Neural Network to Forecast e-Journal Visitors

Electronic journal (e-journal) management comprises several aspects, specifically pageviews, sessions, visitors, and new visitors. Sessions or the number of unique visitors from a journal page is an essential indicator of a journal's outcome. Therefore, it is necessary to forecast the number of unique visitors to determine the strategy for developing a journal. Thus, it is expected to be able to accelerate the journal accreditation system in the future. In this study, this paper predicts the number of unique visitors to the journal by developing a time series forecasting model. Forecasting was done by applying the Backpropagation. The method has the advantage of being able to adapt to changes that occur in the input and output values. There are three time series data input models for this research, specifically three days, seven days and 14 days. The accuracy of forecasting results was measured using the MAPE evaluation of several forecasting models and BPNN architecture. The results show that the best forecasting is using forecasting model 1 and architecture 2-5-1 with an accuracy value of 69.9%. Thus, the performance of the Neural Network in this study is relatively good.
Machine Learning for Misuse-Based Network Intrusion Detection: Overview, Unified Evaluation and Feature Choice Comparison Framework

Network Intrusion detection systems are essential for the protection of advanced communication networks. Originally, these systems were hard-coded to identify specific signatures, patterns and rule violations; now artificial intelligence and machine learning algorithms provide promising alternatives. However, in the literature, various outdated datasets as well as a plethora of different evaluation metrics are used to prove algorithm efficacy. To enable a global comparison, this study compiles algorithms for different configurations to create common ground and proposes two new evaluation metrics. These metrics, the detection score and the identification score, together reliably present the performance of a network intrusion detection system to allow for practical comparison on a large scale. Additionally, we present a workflow to process raw packet flows into input features for machine learning. This framework quickly implements different algorithms for the various datasets and allows systematic performance comparison between those algorithms. Our experimental results, matching and surpassing the state-of-the-art, indicate the potential of this approach. As raw traffic input features are much easier and cheaper to extract when compared to traditional features, they show promise for application in real-time deep learning-based systems.
DeepLPC: A Deep Learning Approach to Augmented Kalman Filter-Based Single-Channel Speech Enhancement

Current deep learning approaches to linear prediction coefficient (LPC) estimation for the augmented Kalman filter (AKF) produce bias estimates, due to the use of a whitening filter. This severely degrades the perceived quality and intelligibility of enhanced speech produced by the AKF. In this paper, we propose a deep learning framework that produces clean speech and noise LPC estimates with significantly less bias than previous methods, by avoiding the use of a whitening filter. The proposed framework, called DeepLPC, jointly estimates the clean speech and noise LPC power spectra. The estimated clean speech and noise LPC power spectra are passed through the inverse Fourier transform to form autocorrelation matrices, which are then solved by the Levinson-Durbin recursion to form the LPCs and prediction error variances of the speech and noise for the AKF. The performance of DeepLPC is evaluated on the NOIZEUS and DEMAND Voice Bank datasets using subjective AB listening tests, as well as seven different objective measures (CSIG, CBAK, COVL, PESQ, STOI, SegSNR, and SI-SDR). DeepLPC is compared to six existing deep learning-based methods. Compared to other deep learning approaches to clean speech LPC estimation, DeepLPC produces a lower spectral distortion (SD) level than existing methods, confirming that it exhibits less bias. DeepLPC also produced higher objective scores than any of the competing methods (with an improvement of 0.11 for CSIG, 0.15 for CBAK, 0.14 for COVL, 0.13 for PESQ, 2.66% for STOI, 1.11 dB for SegSNR, and 1.05 dB for SI-SDR over the next best method). The enhanced speech produced by DeepLPC was also the most preferred by 10 listeners. By producing less biased clean speech and noise LPC estimates, DeepLPC enables the AKF to produce enhanced speech at a higher quality and intelligibility.
Production of Chlamydospores of Trichoderma longbrachiatum ACCC30150 and Trichoderma harzianum ACCC30371 by Liquid Fermentation

Culture medium,temperature,pH and content of oxygen can affect the production of chlamydospores of Trichoderma longbrachiatum ACCC30150 and T.harziamum ACCC30371.Our results indicated that the best single-factor conditions for the production of chlamydospores of T.longbrachiatum ACCC30150 were as follow: straw powder or Gorodkowa as medium,28℃,pH5.0,250 r/min and 75 ml liquid medium in the size of 250 ml bottle.In this case,number of chlamydospores of ACCC30150 was up to 4.07×108/ml after incubation for 10 d.The best single-factor conditions for the production of chlamydospores of T.harziamum ACCC30371 were as follow: straw powder as medium,28℃,pH6.0,250 r/min and 75 ml liquid medium in the size of 250 ml bottle.The number of chlamydospores of ACCC30371 was up to 5.13×108/ml after incubation for 10 d.
Effect of Vitamin D3 Supplementation on Symptomatic Uterine Leiomyoma in Women with Hypovitaminosis D

Objective: The objective of this study is to evaluate the effect of Vitamin D3 on symptoms, uterine and leiomyoma volume in women with symptomatic leiomyoma and hypovitaminosis D. Materials and Methods: In this pilot, interventional, prospective study, 30 premenopausal women with uterine leiomyoma and concomitant hypovitaminosis D (<30 ng/ml) received Vitamin D3 in doses of 60,000 IU weekly for 8 weeks followed by 60,000 IU every 2 weeks for another 8 weeks. Change in symptoms, uterine, and leiomyoma volume was evaluated at 8 weeks and 16 weeks. Serum Vitamin D3 levels were repeated at 16 weeks of therapy. Results: A significant negative correlation was observed between the baseline 25-hydroxy Vitamin D (25(OH) Vitamin D3) and leiomyoma volume (r = –0.434, P < 0.001). There was significant reduction of menstrual blood loss by 29.89% (P = 0.003) and severity of dysmenorrhea, pelvic pain, and backache by 44.12%, 35%, and 50% (P < 0.001, 0.019, and 0.002), respectively, at 16 weeks. At end of therapy, there was 6% reduction in mean uterine volume and 11% in leiomyoma volume which was not significant. Serum 25(OH) Vitamin D3 was significantly higher than baseline level (17.44 ± 5.82 vs. 39.38 ± 8.22, P < 0.001) at end of therapy. Conclusion: Vitamin D3 supplementation is effective in reducing leiomyoma-related symptoms and stabilizing uterine and leiomyoma volume.
The risk of coronavirus to healthcare providers during aerosol-generating procedures: A systematic review and meta-analysis

CONTEXT: Several medical procedures are thought to increase the risk of transmission of infectious agents to health-care providers (HCPs) through an aerosol-generating mechanism. AIMS: Given the significant influenza and coronavirus pandemics that have occurred in the 20th and 21st century, including the current severe acute respiratory syndrome coronavirus 2 global pandemic, the objective of this analysis is to assess the occurrence of disease transmission to HCPs from the performance aerosol-generating procedures (AGPs). SETTINGS AND DESIGN: This was a systematic review and meta-analysis SUBJECTS AND METHODS: We performed a systematic meta-analysis looking at the odds ratio (OR) of AGP, causing infection among HCPs. We searched the following databases: MEDLINE (PubMed), ProQuest, Cochrane databases, and the Gray literature (ClinicalTrials.gov and World Health Organization International Clinical Trials Registry Platform). In addition, we conducted nondatabase search activities. The search terms used were “MERS-CoV,” “COVID,” and “SARS” combined with “provider” or “healthcare provider. ” STATISTICAL ANALYSIS USED: RevMan meta-analysis was used for statistical analysis. RESULTS: Following the search, we reviewed 880 studies, of which six studies were eligible. The estimated odd ratio utilizing a control group of HCPs who were exposed to AGP but did not develop the infection was 1.85 (95% confidence interval [CI]: 1.33, 2.57). The OR remained the same when we added another control group who, despite not being exposed to AGP, had developed the infection. The OR remained 1.85 (95% CI: 1.33, 2.55). However, there is an increase in the OR to 1.89 (95% CI: 1.38, 2.59) when we added HCPs who did not use adequate personal protective equipment (PPE) during the procedures to the total estimates. CONCLUSIONS: The performance of AGP with inadequate PPE can result in an increased risk of disease transmission to HCWs.
An Efﬁcient Parallel Tree Mining Algorithm for Emerging Commodity Processors

With the advent of multi-core technology, it is becoming increasingly evident that an e(cid:11)ective parallel algorithm de-sign is central to realize performance that is commensurate with this advancement. Additionally, along with e(cid:11)ective parallelization strategies, algorithm designers must not only worry about memory access latencies but also memory band-width since technology constraints are likely to limit o(cid:11)-chip bandwidth to memory as one scales up the number of cores per chip. Asastep in this direction, from the perspective of data mining algorithm designs, in this article we consider the challenging problem of designing an e(cid:14)cient parallel algo-rithm for frequent tree mining. Our strawman solution (not a naive one) relies on a well known bijection between a labeled rooted tree and a sequence and leverages the properties of this bijection to realize an algorithm that is inherently array-based as opposed to tree-based that signi(cid:12)cantly improves the locality of said approach. We subsequently improve on this strawman by avoiding the maintenance of any state information during the mining process, and by employing a series of inter-linked pruning and computation chunking steps. We performed a detailed characterization of the employed optimizations on real data that demonstrated that our algorithm keeps a small working set, improves the memory locality, and alleviates the bandwidth pressure on the front side bus. For e(cid:11)ective parallelization we rely on an adaptive load balancing strategy that leverages a combination of coarse-grained and (cid:12)ne-grained task and data partitioning. An empirical evaluation of our algorithm on modern day shared memory systems (SMPs) showed that our algorithm achieves a near linear speedup (up to 13 times on 16 processors) and reduces the memory footprint by up to 366 times without sacri(cid:12)cing on the run time
Epidemiological characteristics of Salmonella spp. isolated from different stages of commercial swine farms

Epidemiological characteristics of a total of 48 swine herd with diarrhea or a history of diarrhea in Gyeongsang-do between 1999 and 2000 were performed to evaluate the prevalence of Salmonella spp. , their serotypes and antibiotic resistance patterns with respect to the different stages of swine production system. A total of 139 Salmonella spp. (21%) were isolated from 662 fecal samples and the overall herd prevalence of Salmonella spp. ranged from 12.5% to 88%. The average prevalence of Salmonella spp. from swine stages of suckling/nursery, grow/finisher and sow stage were 25.7%, 19.2% and 18.4%, respectively. Ten serotypes of Salmonella spp. were identified with a predominance of S. Typhimurium, S. Derby and S. Agona. Twenty-five isolates (18%) were found to be untypable. One hundred and two Salmonella isolates (73.4%) resistant to more than 1 antibiotic were characterized by 24 diverse resistance patterns, and their frequency of antibiotic resistance was highest in grow/finisher stage (83.3%). Resistance to tetracycline (TE; 67.6%), sulfamethoxazole (SU; 46.8%) and streptomycin (ST; 28%) was most common and the most common resistance patterns were TE SU (31.4%), TE (21.6%) and TE SU ST (20.6%) in order.
HEURISTIC TECHNIQUES FOR HANDWRITTEN SIGNATURE CLASSIFICATION

New theoretical and experimental techniques for offline classification of handwritten signatures are introduced in this paper. The proposed algorithms are mainly based on boundary tracing technique for extracting characteristic features. Outer and inner boundaries of the signature image are treated separately. The upper and lower parts of the boundaries are extracted to form two sequences of points. Three algorithms for calculating feature vectors are applied based on y coordinate, distances between consecutive points and from polar coordinates system. Experiments on classification of the resulted vectors were carried out by means of Dynamic Time Warping algorithm using window and slope constraints. A brief comparison between the authors' work and other known signature techniques is also discussed in the paper.
Insights in overcoming the non-adoption of voluntary agricultural ghg mitigation measures in Ireland

Agricultural Greenhouse Gas (GHG) emissions in Ireland are projected to increase up to 21 Mt CO2eq by 2030 mainly driven by increased dairy cow numbers and increased nitrogen fertiliser use. In response to the growing public awareness of the GHG emissions’ environmental impact, the Irish government published the Climate Action Plan in 2019, which identifies the agricultural sector’s leading role in reducing GHG emission and increasing carbon removals to achieve the national GHG emission targets by 2030. Marginal Abatement Cost Curves (MACCs) on Irish GHG emissions have projected the total technically feasible mitigation potential for the Irish agriculture, forestry and land use (AFOLU) sector to be sufficient enough to achieve the set targets by 2030. Although these mitigation measures are available and when implemented, would mostly lead to a win-win situation, the voluntary adaptation rate by farmers is low. This study addresses the most significant determinants of voluntary adoption of mitigation measures by systematically examining existing literature on how and to what extent non-price determinants affect the voluntary adoption rate of technically feasible mitigation measures in the Irish afolu sector. The main identified nonprice determining factors were the degree of farmers’ awareness regarding man-made GHG emissions, receiving agrienvironmental advice, implementation costs, profitability and size of farms, land quality and the type of farm enterprise. Integrating the gained results in the former macc analysis enabled us to adopt the implementation rates of the cost-efficient afolu mitigation measures accordingly. The non-price determinants impact the voluntary uptake rate of AFOLU mitigation measures to the extent that the adjusted total Irish AFOLU abatement potential is 47% lower than technically feasible. Considering that 51.6% of the total estimated AFOLU abatement potential in 2030 is offset through Irish forestry, which at current afforestation rate will turn into a net carbon source by 2035, a significant gap occurs to any potential Irish and EU GHG reduction targets. To substantially help bring the nexus between agricultural development and GHG emission targets in Ireland closer together, policy measures, that differentiate between the different type of AFOLU mitigation measures, need to be implemented to enhance the uptake rate of cost-beneficial and cost-effective measures. This would have the potential to reduce the level of agricultural GHG emissions by 2030 in a way that it would converge towards possible EU and Irish GHG emission reduction targets.
FDI flows into the OECD countries The impact of labor costs

This study explores the importance of labor costs as a determinant of foreign direct investments (FDI) inflows among the OECD countries between 2003 and 2012. This group of countries makes up for the largest portion of global FDI inflows and outflows and it is of high importance for international trade and investments. In addition, by the implementation of interaction effects, we investigate under which circumstances a lower cost of labor in the host country attracts FDI. For the purpose of this study, we use panel data on bilateral FDI inflows among 31 OECD countries and different measures of labor costs. Furthermore, the fixed effects method has been applied for the estimation of our model. The results of our study indicate that a lower level of host country’s labor costs draws more FDI into the economy while a higher level of home country’s labor costs increase the amount of FDI deriving from home country. Moreover, the outcome of the interaction effects gives strong evidence that labor costs have a greater impact on FDI inflows into emerging economies but also that, multinational corporations and international investors are attracted by a combination of highly educated employees and a relatively low cost of labor.
Agent based interaction of commodity price and freight market

Citation: Açık, A. , & Başer, S. Ö. , Agent based interaction of commodity price and freight market, bmij (2021) 9 (1):56-75, doi: https://doi.org/10.15295/bmij.v9i1. 1684 Abstract This study investigates the relationship between iron ore, coal and wheat prices, three major dry bulk cargoes, and Capesize, Panamax, and Handymax freight, which are the intensively used ships in transportation three essential cargoes. These major ship types are considered agents in the market. The main research questions are whether there are a volatility spillover and risk transmission between commodity prices and freight routes and whether there is a differentiation in relations according to the type of cargo and intensive carriage rate. Causality in variance analysis is used to test these research questions, which determines the flow of information between variables and the volatility spillover. The obtained results reveal that the interaction can differ according to both ship types and commodity types, and volatility spillovers and risk transfers are from commodity prices to freight rates.
Cancer metastasis detection using convolutional neural networks and transfer learning

Artificial Intelligence (AI) has been used extensively in the field of medicine. More recently, advanced machine learning algorithms have become a big part of oncology as they assist with detection and diagnosis of cancer. Convolutional Neural Networks (CNN) are common in image analysis and they offer great power for detection, diagnosis and staging of cancerous regions in radiology images. Convolutional Neural Networks get more accurate results, and more importantly, need less training data with transfer learning, which is the practice of using pre-trained models and fine-tuning them for specific problems. This paper proposes utilizing transfer learning along with CNNs for staging cancer diagnoses. Randomly initialized CNNs will be compared with CNNs that used transfer learning to determine the extent of improvement that transfer learning can offer with cancer staging and metastasis detection. Additionally, the model utilizing transfer learning will be trained with a smaller subset of the dataset to determine if using transfer learning reduced the need for a large dataset to get improved results.
Modeling the fate of pesticides in municipal wastewater treatment

The fate of two insecticides (lindane and diazinon) and three herbicides (2,4-D, 2,4,5-T, and atrazine) in municipal wastewater was investigated in pilot plant experiments. The pesticides were dosed into the wastewater to provide feed concentrations in the 2 to 10 μg/L range. The pilot plant was operated at both 4- and 10-day solids retention times (SRTs) in the first of two testing campaigns and at a 4-day SRT in the second campaign. Only lindane was found to be biodegradable to any extent. Loss of pesticides from the system due to sorption to mixed liquor solids was small. Estimated partition coefficients ranged from 0.01 to 2 L/g volatile suspended solids (VSS), whereas biodegradation rates ranged from 0.1 for lindane to 4 E-6 L/g VSS.h for 2,4,5-T. Because the pesticides examined are mostly refractory to activated sludge treatment under the conditions examined, it is desirable to limit their entry to wastewater collection and treatment systems to minimize their impact on receiving waters.
Antimicrobial and antioxidant potentials, total phenolic contents of some herbal waters

The aim of the actual study is to evaluate antimicrobial and antioxidant potentials, total phenolic contents of thyme (Thymus sp.), myrtle (Myrtus communis L.), eucalyptus (Eucalyptus globulus L.) and rosemary herbal waters (Rosmarinus officinalis L.). They were bought a retailer in Giresun. In the studies, it was determined that only thyme water exhibited antimicrobial activity in all herbal waters. Streptomycine, tetracycline and nystatin which were synthetic antimicrobials demonstrated higher activity than studied herbal waters. Moreover; total flavonoid contents of the tested waters ranges from 50.19±0.0038 μL CE/mL to 126.15±0.004 μL CE/mL. The highest and the lowest total phenolic contents were detected in the thyme water and the eucalyptus water as 688.18±0.009 μL GAE/mL and 24.54±0.0008 μL GAE/mL, respectively. DPPH and ABTS radical scavenging activities of the herbal waters exhibited a dose dependent manner and increased with increasing conentrations. As a result of this study, it was concluded that thyme water could be an alternative to synthetic antimicrobial agents and thyme water, myrtle water, eucalyptus water and rosemary waters might be an alternative to synthetic antioxidative agents. Hence, further and detailed investigations are needed to determine active constituents in the herbal waters.