In this paper, the error performance of coherent systems in presence of imperfect carrier phase estimation is investigated for signals propagating over the two-ray with diffuse power (TWDP) fading channels, in case when synchronization is performed using pilot carrier located out of the signal’s band-width. In that sense, closed-form approximate average binary error probability (ABEP) expressions are derived for binary and quadrature phase shift keying (BPSK and QPSK) modulated signals, with the carrier extracted using phase-locked loop (PLL) and phase noise approximated by Tikhonov probability density function (PDF). Derived expressions are calculated for various combinations of channel and phase loop parameters, enabling us to observe their effects on overall system performance. The accu-racy of derived expressions is verified through their comparison with the exact ABEPs obtained by numerical integration of the appropriate expressions.

Flow table lookup is a well-known bottleneck in software-defined network switches. Associative lookup is the fastest but most costly method. On the other hand, an approximate flow classification based on Bloom filters has an outstanding cost-benefit ratio but comes with a downside of false-positive results. Therefore, we propose a new flow table lookup scheme based on Bloom filters and RAM, which offers a good compromise between cost and performance. We solve the problem of false positives of primary Bloom filters by verifying the results and, if necessary, by linearly searching the contents of secondary RAM. Also, we provide a practical implementation in the FPGA-based SDN switch and experimentally show that the proposed solution can achieve better performance than the classic linear search at the low cost typical of Bloom filters.

In this paper approach for the experimental determination of the grounding system impulse impedance under the presence of the high-frequency electromagnetic interference is presented. The considered approach is based on the application of the discrete wavelet transform on the measured signals. Validation of the considered approach has been conducted in several experiments using a vertical grounding electrode. The experimental investigation has been performed using different impulse current peak values and different front rise times. On all measured current and voltage waveforms, high-frequency interferences were registered.

The detonation properties of nonideal explosives are highly dependent on charge diameter and existence and properties of confinement. In this study, the effect of different confinements on the detonation velocity of ANFO explosives was experimentally determined along with the results of the plate dent test. ANFO explosive was selected as one of the most commonly used nonideal explosives. Following the measurement results, we found that the detonation velocity increased with increasing wall thickness, and the velocity increase was different for different confinement materials. A strong correlation existed between the ratio of the mass of confiner and explosive (M/C) and the detonation velocity (R = 0.995), and between (M/C) and the depth of the dent (δ) (R = 0.975). The data presented in this paper represent preliminary findings in developing a confinement model required for reliable numerical modeling of nonideal explosives.

Ghrelin, a stomach-produced hormone, is well-recognized for its role in promoting feeding, controlling energy homeostasis, and glucoregulation. Ghrelin’s function to ensure survival extends beyond that: its release parallels that of corticosterone, and ghrelin administration and fasting have an anxiolytic and antidepressant effect. This clearly suggests a role in stress and anxiety. However, most studies of ghrelin’s effects on anxiety have been conducted exclusively on male rodents. Here, we hypothesize that female rats are wired for higher ghrelin sensitivity compared to males. To test this, we systematically compared components of the ghrelin axis between male and female Sprague Dawley rats. Next, we evaluated whether anxiety-like behavior and feeding response to endogenous or exogenous ghrelin are sex divergent. In line with our hypothesis, we show that female rats have higher serum levels of ghrelin and lower levels of the endogenous antagonist LEAP-2, compared to males. Furthermore, circulating ghrelin levels were partly dependent on estradiol; ovariectomy drastically reduced circulating ghrelin levels, which were partly restored by estradiol replacement. In contrast, orchiectomy did not affect circulating plasma ghrelin. Additionally, females expressed higher levels of the endogenous ghrelin receptor GHSR1A in brain areas involved in feeding and anxiety: the lateral hypothalamus, hippocampus, and amygdala. Moreover, overnight fasting increased GHSR1A expression in the amygdala of females, but not males. To evaluate the behavioral consequences of these molecular differences, male and female rats were tested in the elevated plus maze (EPM), open field (OF), and acoustic startle response (ASR) after three complementary ghrelin manipulations: increased endogenous ghrelin levels through overnight fasting, systemic administration of ghrelin, or blockade of fasting-induced ghrelin signaling with a GHSR1A antagonist. Here, females exhibited a stronger anxiolytic response to fasting and ghrelin in the ASR, in line with our findings of sex differences in the ghrelin axis. Most importantly, after GHSR1A antagonist treatment, females but not males displayed an anxiogenic response in the ASR, and a more pronounced anxiogenesis in the EPM and OF compared to males. Collectively, female rats are wired for higher sensitivity to fasting-induced anxiolytic ghrelin signaling. Further, the sex differences in the ghrelin axis are modulated, at least partly, by gonadal steroids, specifically estradiol. Overall, ghrelin plays a more prominent role in the regulation of anxiety-like behavior of female rats.

The combination of reinforcement learning and deep learning has shown some remarkable results in many scientific fields. Deep reinforcement learning algorithms are particularly good at understanding and modeling adaptive decision-making in dynamic environments. In recent years, this concept has been successfully applied to smart grids. In this paper, we provide a brief introduction to the concepts of reinforcement and deep reinforcement learning to the power system engineers and present research progress and prospects in the field. Additionally, we identify smart grid engineering domains that need extensive pattern-based modeling as being particularly suitable for deep reinforcement learning.

AIMS European guidelines set low-density lipoprotein cholesterol (LDL-C) treatment goals <1.4 mmol/L after acute coronary syndrome (ACS), and <1.0 mmol/L for patients with recurrent cardiovascular events ≤2 years. Many ACS patients do not achieve these goals on statin alone. We examined actual goal achievement with alirocumab and projected achievement with ezetimibe, either added to optimized statin therapy. METHODS AND RESULTS The ODYSSEY OUTCOMES trial (NCT01663402) compared alirocumab with placebo in 18,924 patients with recent ACS and hyperlipidaemia despite high-intensity or maximum-tolerated statin therapy. This subanalysis comprised 17,589 patients with LDL-C ≥1.4 mmol/L at baseline who did not receive ezetimibe treatment. High-intensity statin treatment was used in 88.8%. Median (interquartile range) baseline LDL-C was 2.3 (1.9-2.7) mmol/L. With alirocumab, 94.6% of patients achieved LDL-C <1.4 mmol/L at ≥1 post-baseline measurement vs. 17.3% with placebo. Among 2236 patients with a previous cardiovascular event within 2 years (before the qualifying ACS), 85.2% vs. 3.5%, respectively, achieved LDL-C <1.0 mmol/L. Among patients not treated with ezetimibe, we projected that its use would have achieved LDL-C <1.4 and <1.0 mmol/L in 10.6% and 0%, respectively at baseline (assuming 18 ± 3% reduction of LDL-C). CONCLUSION Among patients with recent ACS and LDL-C ≥1.4 mmol/L despite optimized statin therapy, addition of alirocumab allowed 94.6% to achieve the 2019 European guideline LDL-C goal <1.4 mmol/L, and 85.2% of those with recurrent cardiovascular events to achieve <1.0 mmol/L. In contrast, addition of ezetimibe to optimized statin therapy was projected to achieve LDL-C <1.4 mmol/L in only 10.6% of patients at baseline.

This paper addresses the use of deep learning techniques in 3D point cloud labeling of environment representations for the task of a semantic visual localization of mobile robots. In contrast to standard problems resolved with Convolutional Neural Networks (CNNs), the paper deals with applying CNNs to segment point clouds that are, unlike images, unordered and unstructured. The used point clouds contain laser measurements of 3D positions (x,y,z) as well as captured RGB camera images from the scanned scene to colorize the point cloud (RGB values). The main focus of the paper is on implementation and evaluation of a hand-crafted convolution layer and the ConvPoint CNN architecture that introduces continuous convolutions for point cloud processing. The solution was implemented in the Python programming language using the PyTorch deep learning framework.

Instantaneous frequency measurement is a critical component of power system control and automation. Recently, electric power distribution networks with a high proportion of renewable energy have been subjected to unprecedented complexity, necessitating more complicated automation solutions. The major reasons for frequency changes include the usage of dispersed generation, the connection of non-linear loads, and the occurrence of some unforeseen system problems. This paper presents two DFT-based power system frequency measuring algorithms. It considers frequency variations from the system’s fundamental frequency, as well as the noise generated by analog to digital converters (ADC). The IEEE Phasor Measurement Unit (PMU) latest Standard specification (IEC/IEEE 60255-118-1:2018) is used to examine these two methodologies. The methodologies are evaluated using test signals that are required to provide PMU quality evaluation and classification while accounting for process noise, ADC conversion noise, and dynamically changing input voltage and current signals. The tradeoff between DFT simplicity in implementation and needed complexity of power systems is put to the test by abrupt variations in frequency and amplitude of the fundamental component.

The paper analyzes the normative-formative framework that denotes the connection between memory and identity as a crucial origin of conflicts. In addition to concerns about memory politics, historical revisionism and ethnonational identity collectivism, the paper dissolves the connection between phenomena highlighting outcomes of the peace process, transitional justice, and its ethical/moral connotations. The study argues that Western Balkan’s sociopolitical stability depends on declining conflicting and contradictory memory order within radical sociopolitical processes. The revisionist contention memorializes conflicts and wars as the fundamental concept of ethnicity/religion/nation. It overlaps with the neoliberal and neoconservative reduction of all competitive relations, in which only the stronger have the right to existence. Discarding dominant ethnopolitical narratives is essential for conflict transformation and transitional justice for all ethnoreligious communities. The Balkan historical events and conflicting memory (WW2, Yugoslav wars) caused sociopolitical dominion shaping the collective behavior of ethnic groups. The damaging ethnic/religious practice of genocide denial and honoring war crimes within people’s social lives can become a matrix for future conflicts. Placing memory politics with radical populism is a critical condition of collective identity politics in the former Yugoslavia. Scientific rationality can provide a solid path through the anomalies in the form of political ideologies.

Background Pharmacogenomics (PGx) testing can reduce toxicities and improve efficacy of several drugs used to treat cancer and associated symptoms. PGx results can be determined from germline whole-exome sequencing (WES), but somatic mutations may cause discordance between tumor and germline DNA. Since clinical diagnostic sequencing in oncology frequently only includes tumor DNA, there would be clinical value in calling germline PGx genotypes from tumor DNA. Thus, the purpose of this study was to assess the feasibility of using somatic WES data to call germline PGx genotypes. Methods Germline and somatic WES data were obtained as part of the clinical workflow for 64 patients treated at the solid molecular tumor board clinic at Indiana University. Aldy v3.3 was implemented in LifeOmic’s Precision Health Cloud™ to call PGx genotypes from somatic WES. Somatic Aldy calls were compared with previously validated Aldy germline calls for 8 genes: CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, CYP4F2, DPYD, and TPMT. Somatic read depth was >100x, except for the intronic CYP3A4*22 variant, which was >30x. Results Somatic and germline Aldy calls were compared for a total of 512 genotypes and 56 (11%) calls were discordant. Discordant calls were most common for CYP2B6 (23.4%), followed by CYP2D6 (14.1%), CYP2C19 (10.9%), CYP2C8 (6.3%), and DPYD (6.3%). In contrast, all Aldy calls were concordant for CYP3A5 and TPMT. 38 out of 64 subjects (59%) had discordant calls for at least one gene. The most common first cancer diagnoses in our cohort were colorectal (9.3%), breast (7.8%), and pancreatic (7.8%), and the rates of discordant Aldy calls did not differ by cancer type (p>0.05 for all cancer types). Based on our analyses of discordant calls, we anticipate that adjusting Aldy’s thresholds for variant calling may allow Aldy to determine genotypes from somatic WES data. Conclusion In most cases, genotype calls of drug metabolism genes from tumor DNA reflected the germline genotypes; however, additional work needs to be done to determine if the remaining discordant calls can be corrected by modifying the informatics tools or if they are due to somatic mutations. Citation Format: Wilberforce A. Osei, Tyler Shugg, Reynold C. Ly, Steven M. Bray, Benjamin A. Salisbury, Ryan R. Ratcliff, Victoria M. Pratt, Ibrahim Numanagić, Todd Skaar. Pharmacogenomics genotyping from clinical somatic whole exome sequencing: Aldy, a computational tool [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1151.

The aim of this paper is to analyse the influence of foliar application of a biostimulative fertilizer on some of the elements of raspberry fruit quality of the Polka variety. The research was conducted in 2015, according to the system of controls and treatment. Slavol VVL, a foliar fertilizer with biostimulating effects was applied for treatment. A total of 12 quantitative and qualitative properties were analyzed depending on the influencing factor, namely: total sugar content, reducing sugars, invert sugars, sucrose, water content, dry matter, total acidity, vitamin C, total phenols, total flavonoids and antioxidant capacity, and fruit weight. After the completed analyzes, it can be concluded that raspberry plants treated with Slavol VVL were characterized by the highest values ​​of total acidity (2.07%), dry matter (14.86%), and vitamin C content (25.15 mg/100 g of fresh weight).

The purpose of a network intrusion detection (NID) is to detect intrusions in the network, which plays a critical role in ensuring the security of the Internet of Things (IoT). Recently, deep learning (DL) has achieved a great success in the field of intrusion detection. However, the limited computing capabilities and storage of IoT devices hinder the actual deployment of DL-based high-complexity models. In this article, we propose a novel NID method for IoT based on the lightweight deep neural network (LNN). In the data preprocessing stage, to avoid high-dimensional raw traffic features leading to high model complexity, we use the principal component analysis (PCA) algorithm to achieve feature dimensionality reduction. Besides, our classifier uses the expansion and compression structure, the inverse residual structure, and the channel shuffle operation to achieve effective feature extraction with low computational cost. For the multiclassification task, we adopt the NID loss that acts as a better loss function to replace the standard cross-entropy loss for dealing with the problem of uneven distribution of samples. The results of experiments on two real-world NID data sets demonstrate that our method has excellent classification performance with low model complexity and small model size, and it is suitable for classifying the IoT traffic of normal and attack scenarios.

The growing penetration of renewable resources such as wind and solar into the electric power grid through power electronic inverters is challenging grid protection. Due to the advanced inverter control algorithms, the inverter-based resources present fault responses different from conventional generators, which can fundamentally affect the way that the power grid is protected. This paper studied solar inverter dynamics focused on negative-sequence quantities during the restoration period following a grid disturbance by using a real-time digital simulator. It was found that solar inverters can act as negative-sequence sources to inject negative-sequence currents into the grid during the restoration period. The negative-sequence current can be affected by different operating conditions such as the number of inverters in service, grid strength, and grid fault types. Such negative-sequence responses can adversely impact the performance of protection schemes based on negative-sequence components and potentially cause relay maloperations during the grid restoration period, thus making system protection less secure and reliable.