Predefined Threshold Value Research Articles

Progesterone peak influences embryonic developmental morphokinetics on trigger day? A retrospective study

ObjectivePremature Progesterone Rise (PPR) is characterized by elevated serum progesterone concentrations either towards the end of the follicular phase or on the trigger day, surpassing a pre-defined threshold value. Aim of the study is to evaluate the impact of PPR exceeding 1.5 ng/ml at the time of hCG-trigger on embryo morphokinetic parameters and to identify predictive biomarkers of in IntraCytoplasmic Sperm Injection (ICSI) cycles outcomes.MethodsIt is a retrospective study including patients underwent ICSI cycles in the period 2020–2023. 58 patients were recruited in the study group showing P levels in the trigger day greater than or equal to 1.5 ng/ml. A matching control group of 58 patients with P levels below 1.5 ng/ml was after selected. The general characteristics of these patients, including age, Body Mass Index (BMI), antral follicle count (AFC), anti-Müllerian hormone (AMH) and follicle-stimulating hormone (FSH) levels, the type of infertility and smoking/non-smoking patients, were recorded on the day of their initial visit. Subsequently, data were collected regarding the number of eggs retrieved, mature eggs, successfully fertilized eggs, and embryos reaching the blastocyst stage. Additionally, the timing of embryonic development and the quality of obtained blastocysts, as assessed by the degree of expansion and the characteristics of the inner cell mass (ICM) and trophectoderm (TE), were evaluated using Time-Lapse technology.ResultsElevated P levels exceeding 1.5 ng/ml on the trigger day were directly associated with a significantly larger number of antral follicles, consequently leading to a higher count of retrieved eggs, mature eggs, successfully fertilized eggs and embryos reaching the blastocyst stage. Furthermore, the analysis of morphokinetic parameters indicated faster division times and a notably greater number of high-grade blastocysts in the study group compared to the control group.ConclusionsP levels ≥ 1.5 ng/ml on the trigger day did not negatively impact embryonic morphokinetic parameters, instead resulting in faster embryo development in the initial stages.

Wideband Transmissive Programmable Metasurface for Adaptive Millimeter‐Wave Beamforming

AbstractProgrammable metasurfaces provide a promising paradigm for the beamforming of electromagnetic waves in the fifth‐generation (5G) and sixth‐generation (6G) wireless communications with low cost and low complexity. However, most of the existing researches are focused on the sub‐6 GHz spectrum, which limited the gigabits per second (Gb s−1) high‐speed data transmission on future wireless network. Additionally, as a step forward in the development of intelligent metasurfaces, extra peripheral devices are often required to sense environmental variations for self‐adaptive modulations. Here, a wideband millimeter‐wave programmable metasurface is presented for peripheral‐free self‐adaptive signal enhancement in 5G/6G wireless communication. The proposed transmissive programmable metasurface consists of meta‐atoms, exhibiting efficient transmission (1 dB loss) and a stable 1‐bit phase difference from 23.7 to 32.1 GHz, effectively covering the desired 5G millimeter‐wave frequency bands: N257, N258, and N261. By integrating the self‐adaptive algorithm based on received signal intensity into the controller, the programmable metasurface can self‐adaptively establish an optimal or acceptable channel according to the predefined threshold values. To evaluate the performance of the prototype, an experiment on wavefront modulation was first conducted in a microwave chamber, verifying the wideband beamforming performance of the programmable metasurface. Moreover, self‐adaptive signal enhancement and millimeter‐wave wireless communication experiments were conducted in indoor environments, showing a maximum signal enhancement of 21.4 dB and a significant improvement in constellation. The proposed self‐adaptive wideband programmable metasurface demonstrates high potential for application in 5G/6G millimeter‐wave wireless networks.

Design and verification of q-axis perturbation based active islanding detection schemes for DG systems

The penetration of grid integrated distributed generation (DG) in the present decade, has benefited rural communities, the environment, and the power sector. These renewable power sources based DGs could eliminate the need of extensive transmission networks, especially in remote areas, reduce emissions and improve power supply reliability. A significant drawback of grid integrated DG systems is the islanding of DG units, which puts workers' safety at risk and raises the possibility of damaging electrical infrastructure. Therefore, islanding detection techniques are used to reduce the danger associated with islanded functioning of DG units. Fast detection, small non detection area and less power quality disturbance are the major requirements of any islanding detection method. To address this issue of islanding, researchers have proposed various islanding detection strategies. This paper compares various q-axis controller-based islanding identification approaches: sub-harmonic perturbation (SHP), complementary reactive power perturbation (CRPP), and even harmonic perturbation (EHP). In all three proposed methodologies, the perturbations introduced result in frequency deviations surpassing the predefined threshold values. But the time of islanding detection is least in the CRPP approach. CRPP can also drift the total harmonic distortion (THD) beyond the corresponding threshold in an appreciable way. The performance of these (Islanding detection methods) IDMs is evaluated through simulations using MATLAB-Simulink on a PV fed DG. The efficacy of the comparative analysis is ensured with necessary waveforms.

The utility of artificial intelligence in identifying radiological evidence of lung cancer and pulmonary tuberculosis in a high-burden tuberculosis setting.

Artificial intelligence (AI), using deep learning (DL) systems, can be utilised to detect radiological changes of various pulmonary diseases. Settings with a high burden of tuberculosis (TB) and people living with HIV can potentially benefit from the use of AI to augment resource-constrained healthcare systems. To assess the utility of qXR software (AI) in detecting radiological changes compatible with lung cancer or pulmonary TB (PTB). We performed an observational study in a tertiary institution that serves a population with a high burden of lung cancer and PTB. In total, 382 chest radiographs that had a confirmed diagnosis were assessed: 127 with lung cancer, 144 with PTB and 111 normal. These chest radiographs were de-identified and randomly uploaded by a blinded investigator into qXR software. The output was generated as probability scores from predefined threshold values. The overall sensitivity of the qXR in detecting lung cancer was 84% (95% confidence interval (CI) 80 - 87%), specificity 91% (95% CI 84 - 96%) and positive predictive value of 97% (95% CI 95 - 99%). For PTB, it had a sensitivity of 90% (95% CI 87 - 93%) and specificity of 79% (95% CI 73 - 84%) and negative predictive value of 85% (95% CI 79 - 91%). The qXR software was sensitive and specific in categorising chest radiographs as consistent with lung cancer or TB, and can potentially aid in the earlier detection and management of these diseases.

Gender Security in EU Member-States and Ukraine

Purpose: This article explores gender security within the economic frameworks of EU member-states and Ukraine. It aims to expand the definition of gender security beyond traditional concepts of conflict resolution and violence prevention, focusing on economic stability and equality. The article analyzes the impact of economic policies, labor market dynamics, healthcare systems, and social policies on gender security, contributing to a broader understanding of the concept. Methods: The study employs a quantitative methodology using a robust set of indicators across demographic, macroeconomic, and social security parameters. These indicators include predefined threshold values for evaluating economic and gender security. Countries are assigned numerical rankings based on their performance against these thresholds, with a focus on female-to-male ratios in key areas. Results and conlcusions: The research reveals significant disparities in gender security across EU member-states and Ukraine. Spain and Sweden demonstrate superior healthcare and socio-economic conditions, reflected in high female life expectancies and low infant mortality rates. In contrast, Bulgaria, Romania, and Ukraine exhibit lower life expectancies and higher infant mortality rates, highlighting challenges in healthcare and economic stability. The study also finds discrepancies in unemployment rates between genders, indicating varied economic conditions and societal norms. Implications: The findings can inform targeted policy interventions in healthcare, employment, and social welfare to address gender-specific challenges. By understanding disparities in gender security, policymakers can design more effective strategies to promote gender equality and economic stability. The article’s comprehensive approach provides a nuanced view of gender security, crucial for shaping policies that address both female and male economic securities. Originality / value: This article contributes to gender security research by providing a comprehensive, gender-centric analysis of economic security in the EU and Ukraine. It stands out from previous studies by integrating a wide range of economic indicators to understand the interplay between economic conditions and gender equality.

Progressive sarcopenia and myosteatosis predict prognosis of advanced HCC patients treated with immune checkpoint inhibitors.

Immunotherapy stands as a pivotal modality in the therapeutic landscape for the treatment of advanced hepatocellular carcinoma, yet responses vary among patients. This study delves into the potential impact of sarcopenia, myosteatosis and adiposity indicators, as well as their changes during immunotherapy, on treatment response and prognosis in patients with advanced hepatocellular carcinoma treated with immune checkpoint inhibitors. In this retrospective analysis, 116 patients with advanced hepatocellular carcinoma receiving immune checkpoint inhibitors were recruited. Skeletal muscle, intramuscular, subcutaneous, and visceral adipose tissue were assessed by computed tomography at the level of the third lumbar vertebrae before and after 3 months of treatment. Sarcopenia and myosteatosis were evaluated by skeletal muscle index and mean muscle density using predefined threshold values. Patients were stratified based on specific baseline values or median values, along with alterations observed during the treatment course. Overall survival (OS) and progression-free survival (PFS) were compared using the log-rank test and a multifactorial Cox proportional risk model. A total of 116 patients were recruited and divided into two cohorts, 81 patients for the training set and 35 patients for the validating set. In the overall cohort, progressive sarcopenia (P=0.021) and progressive myosteatosis (P=0.001) were associated with objective response rates, whereas progressive myosteatosis (P<0.001) was associated with disease control rates. In the training set, baseline sarcopenia, myosteatosis, and subcutaneous and visceral adipose tissue were not significantly associated with PFS and OS. In multivariate analysis adjusting for sex, age, and other factors, progressive sarcopenia(P=0.002) and myosteatosis (P=0.018) remained independent predictors of PFS. Progressive sarcopenia (P=0.005), performance status (P=0.006) and visceral adipose tissue index (P=0.001) were all independent predictors of OS. The predictive models developed in the training set also had good feasibility in the validating set. Progressive sarcopenia and myosteatosis are predictors of poor clinical outcomes in patients with advanced hepatocellular carcinoma receiving immune checkpoint inhibitors, and high baseline visceral adiposity is associated with a poorer survival.

Digital plaque monitoring: An evaluation of different intraoral scanners

ObjectivesIntraoral scanners (IOS) display disclosed plaque, and the scientific literature has reported that plaque levels can be monitored on intraoral scans using one IOS system (Dexis 3800; control IOS). This study aimed to investigate whether this is also possible with other IOS systems (i700, Primescan, Trios 5; test IOS). Materials and methodsTen participants (29.6 ± 5.5 years) were enrolled. After plaque accumulation and subsequent toothbrushing, intraoral scans were performed with the control IOS and the three test IOS. All scans were aligned and the vestibular/oral surfaces of the Ramfjord teeth (16, 21, 24, 36, 41, 44) were analysed with automated planimetry using a predefined threshold value. The proportion of pixels assigned to plaque-covered areas was expressed as a percentage of the total number of pixels (P%). We then assessed whether the planimetrically determined plaque-covered areas corresponded to those identified visually. This revealed that a threshold correction (P%corr) was required for approximately 20 % (i700 and Trios 5) to over 65 % (Primescan) of the images. ResultsBland-Altman analysis showed no significant systematic bias and limits of agreement ranging from approximately -20 to +20 P% units, with a tendency towards lower values at higher plaque coverage. Manual correction improved the agreement and halved the limits of agreement. All test IOS could detect a reduction in plaque after brushing, as well as the typical site-dependant plaque distribution patterns. ConclusionsAll test IOS appeared to be suitable for plaque monitoring. Planimetric methods must be adapted to the colour representation of the IOS. Clinical significancePlaque monitoring using IOS opens a new field of application in preventive dentistry.

Serviceability analysis of sea-crossing bridges under correlated wind and wave loads

With coupled wind and wave loads suffered by sea-crossing bridges, it is challenging to include uncertainties for assessing bridge serviceability. In this study, an efficient probabilistic peak response framework with surrogate models is developed for the serviceability analysis of sea-crossing bridges under coupled wind and wave loads. The joint probability distribution functions (JPDF) of the mean wind speed, significant wave height, and peak wave period are first derived based on long-term field measurement data and using a C-vine copula model. The JPDF serves as an input to the bridge to calculate the peak response of the bridge through finite-element analyses. To improve computational efficiency and make the problem manageable, the surrogate models are then generated by both the support vector machine and response surface method to predict the bridge peak responses. The exceeding probabilities for the serviceability assessment are finally obtained by the Monte-Carlo method using the pre-defined threshold values of bridge serviceability. A real long-span sea-crossing bridge is used as a case study to demonstrate the feasibility and effectiveness of the proposed framework. The results elucidate the effects of correlated wind and wave loads on bridge serviceability. Ignoring the correlation of wind and wave parameters will significantly overestimate the exceeding probability of bridge serviceability.

CONSIDERATIONS FOR THE PARAMETERISATION OF A CHATTER DETECTION ALGORITHM

Process instabilites due to the regenerative effect limit the material removal rate of cutting machine tools. A method developed at the Laboratory for Machine Tools and Production Engineering (WZL) at RWTH Aachen University reduces the experimental effort of determining stability limits for milling. The method bears on a time-domain chatter detection algorithm, which detects process instability based on predefined threshold values. Up to now, the definition of the threshold values has been derived from a selective number of sample tests. To investigate the transferability of the chatter detection algorithm to various boundary conditions cutting tests are carried out. In addition, a new stability criterion for high frequency chatter oscillations is introduced. The results are analysed with regard to factors that require an adjustment of the threshold values. Based on the results, a parameterisation of the chatter detection algorithm is presented.

Fault detection of high-speed train axle bearings based on a hybridized physical and data-driven temperature model

As a key component connecting axle and frame, axle bearings of the high-speed train are essential for the safe operation of modern high-speed trains. The current on-board fault detection system, which compares the axle box temperature measured from the sensor to a predefined threshold value, faces problems such as poor adaptability to varied service conditions, undesired high rate of false alarms and missing alarms, and lack of potential to be optimized. To solve these problems, in the present study, a novel hybrid model is proposed which predicts the temperature of a healthy bearing under the given service condition, and based on that detects the bearing fault. The hybrid model includes a physical model reflecting the thermal behavior of the axle box and two data-driven models adopting the same inputs but representing the relationship by means of back-propagation (BP) neural network and long-short term memory neural network (LSTM). After calibrating these models by real-life operation data collected from 2.25 million kilometers and using all results as inputs, a fault discriminant mechanism is established. The performance of this new mechanism is superior than the current on-board system and all stand-alone models in terms of indicators such as precision, recall and F1 score. This way, the study shows that similar hybrid models can be a viable solution for the future on-board fault detection system.

The K Critical Path Sets to Protect in Interdiction Networks Under Limited Defensive Resources

This paper considers a network under intelligent threats. The defender of the network attempts to identify which disjoint K-critical path sets to be fortified under limited resources in order to better resist to attacks. Strengthening paths consists on increasing the path survivability by increasing individual survival probabilities of some or all arcs within the path accounting for limited resources. These resources are expressed through a defensive budget. The model assumes different reinforcement scenarios using different levels of resources. The paper proposes two solution methods. The first method maximizes the smallest survival probability over the K breakthrough paths to select under a budget constraint. The second one minimizes the cost of the overall survivability improvement while maintaining the smallest survival probability over the K selected breakthrough paths to a predefined threshold value. The formulated integer programs are solved optimally in a short time. This is the case even for large networks. The sensitivity analysis indicates in particular that, beyond some levels of defensive resources consumption, the survivability improvement becomes very slow suggesting an efficient re-allocation of the budget.

Shot classification for human behavioural analysis in video surveillance applications

Human behavior analysis plays a vital role in ensuring security and safety of people in crowded public places against diverse contexts like theft detection, violence prevention, explosion anticipation etc. Analysing human behaviour by classifying of videos in to different shot types helps in extracting appropriate behavioural cues. Shots indicates the subject size within the frame and the basic camera shots include: the close-up, medium shot, and the long shot. If the video is categorised as Close-up shot type, investigating emotional displays helps in identifying criminal suspects by analysing the signs of aggressiveness and nervousness to prevent illegal acts. Mid shot can be used for analysing nonverbal communication like clothing, facial expressions, gestures and personal space. For long shot type, behavioural analysis is by extracting the cues from gait and atomic action displayed by the person. Here, the framework for shot scale analysis for video surveillance applications is by using Face pixel percentage and deep learning based method. Face Pixel ratio corresponds to the percentage of region occupied by the face region in a frame. The Face pixel Ratio is thresholded with predefined threshold values and grouped into Close-up shot, mid shot and long shot categories. Shot scale analysis based on transfer learning utilizes effective pre-trained models that includes AlexNet, VGG Net, GoogLeNet and ResNet. From experimentation, it is observed that, among the pre-trained models used for experimentation GoogLeNet tops with the accuracy of 94.61%.

Optimal ranking and directional signature classification using the integral strategy of multi-objective optimization-based association rule mining of multi-omics data.

Introduction: Association rule mining (ARM) is a powerful tool for exploring the informative relationships among multiple items (genes) in any dataset. The main problem of ARM is that it generates many rules containing different rule-informative values, which becomes a challenge for the user to choose the effective rules. In addition, few works have been performed on the integration of multiple biological datasets and variable cutoff values in ARM. Methods: To solve all these problems, in this article, we developed a novel framework MOOVARM (multi-objective optimized variable cutoff-based association rule mining) for multi-omics profiles. Results: In this regard, we identified the positive ideal solution (PIS), which maximized the profit and minimized the loss, and negative ideal solution (NIS), which minimized the profit and maximized the loss for all gene sets (item sets), belonging to each extracted rule. Thereafter, we computed the distance (d +) from PIS and distance (d -) from NIS for each gene set or product. These two distances played an important role in determining the optimized associations among various pairs of genes in the multi-omics dataset. We then globally estimated the relative closeness to PIS for ranking the gene sets. When the relative closeness score of the rule is greater than or equal to the pre-defined threshold value, the rule can be considered a final resultant rule. Moreover, MOOVARM evaluated the relative score of the rule based on the status of all genes instead of individual genes. Conclusions: MOOVARM produced the final rank of the extracted (multi-objective optimized) rules of correlated genes which had better disease classification than the state-of-the-art algorithms on gene signature identification.

Adaptive DFT-s-OFDM employed novel multi layered scheme for reduction of PAPR for mMTC node in 5G (NR)

In 5G New Radio, The DFT-s-OFDM is a promising multiplexing technique for enhancing the average power of subcarriers to reduce the overall peak-to-average power ratio (PAPR). The peak of subcarrier is too high then the impact of DFT-s-OFDM is quite inferior. An Adaptive DFT-s-OFDM employed multilayered novel mechanism has been proposed in this research article to reduce the PAPR. The extent of sub-band is varied in accordance with predefined threshold value to enhance average power, meanwhile clipping is applied to shape the peak value of subcarrier. The clipping technique reduced the subcarrier’s peak power. The proposed approach has the greater impact in order to enhance the efficiency of non-linear Power Amplifier (PA). The PAPR is suppressed by the multilayered strategy in two ways. The first is the numerator element, which is the reduction of peak power by clipping operation, and the second is the denominator element, which is the improvement of average power through K-point DFT sub-band. The superior time–frequency localization of DFT-s-OFDM across uplink transmissions reduces the requirement for difficult node-to-node synchronization, which is potentially used by the mMTC nodes. The proposed adaptive DFT-s-OFDM modulation scheme performs better than conventional OFDM modulation, which is approximately 24% more effective at 6 dB.

Localization of sensor nodes in the Internet of Things using fuzzy logic and learning automata

The Internet of Things (IoT) is a future-generation networking environment in which distributed smart objects can communicate directly and create a connection between different types of heterogeneous networks. Knowing the accurate localization of IoT-based devices is one of the most challenging issues in expanding the IoT network performance. This paper was done to propose a new fuzzy type2-based scheme to enhance the position accurateness of sensors deployed in the Internet of Things environments. Our proposed scheme is based on the weighted centralized localization strategy, in which the location of unknown nodes calculates using the fuzzy type-2 system. The flow measurement via the wireless channel to calculate the separation distance between the sensor/anchor nodes is employed as the fuzzy system input. Also, the fuzzy membership functions to better adaptivity of our scheme with lossy IoT environments via learning automata algorithm are tuned. Then, in the proposed method, the fuzzy type-2 calculations are restricted by comparing the received signal strength with a predefined threshold value to extend the network lifetime. The effectiveness of the proposed scheme has been proven through extensive simulation. Based on the simulation results, our scheme, on average, reduced the localization error by 35.9% and 9.5% decreased the energy consumption by 13% and 7.2%, and reduced the convergence rate by 33.1% and 12.37 % compared to the HSPPSO and IMRL methods, respectively.

A hybrid rough set shuffled frog leaping knowledge inference system for diagnosis of lung cancer disease

Abundant medical data are generated in the digital world every second. However, gathering helpful information from these data is difficult. Gathering useful information from the dataset is very advantageous and demanding. Besides, such data also contain many extraneous features that do not influence the foreboding accuracy while diagnosing a disease. The data must eliminate these extraneous features to get a better diagnosis. Ultimately, the minimized information system will lead to a better diagnosis. In this paper, we have introduced an incremental rough set shuffled frog leaping algorithm for knowledge inference. The proposed algorithm helps find minimum features from an information system while handling complex databases with uncertainty and incompleteness. The proposed rough set shuffled frog leaping knowledge inference model works in two phases. In the initial phase, the incremental rough set shuffled frog leaping algorithm is used to get the most relevant features. Identifying the relevant features is carried out using a fitness function, which uses the rough degree of dependency. The use of the fitness function identifies the much information with the minimum number of features. The purpose of feature selection is to identify a feature subset from an original set of features without reducing the predictive accuracy and to scale back the computation overhead in the data processing. In the second phase, a rough set is utilized for knowledge discovery in perception with rule generation. The selection of decision rules is carried out based on the accuracy of the decision rule and a predefined threshold value. An empirical analysis of the lung disease information system and a comparative study is conducted. Experimental outcomes exhibit that hybrid techniques express the feasibility of the proposed model while achieving better classification accuracy.

Handwritten Numeral Recognition Integrating Start–End Points Measure with Convolutional Neural Network

Convolutional neural network (CNN) based methods have succeeded for handwritten numeral recognition (HNR) applications. However, CNN seems to misclassify similarly shaped numerals (i.e., the silhouette of the numerals that look the same). This paper presents an enhanced HNR system to improve the classification accuracy of the similarly shaped handwritten numerals incorporating the terminals points with CNN’s recognition, which can be utilized in various emerging applications related to language translation. In handwritten numerals, the terminal points (i.e., the start and end positions) are considered additional properties to discriminate between similarly shaped numerals. Start–End Writing Measure (SEWM) and its integration with CNN is the main contribution of this research. Traditionally, the classification outcome of a CNN-based system is considered according to the highest probability exposed for a particular numeral category. In the proposed system, along with such classification, its probability value (i.e., CNN’s confidence level) is also used as a regulating element. Parallel to CNN’s classification operation, SEWM measures the start-end points of the numeral image, suggesting the numeral category for which measured start-end points are found close to reference start-end points of the numeral class. Finally, the output label or system’s classification of the given numeral image is provided by comparing the confidence level with a predefined threshold value. SEWM-CNN is a suitable HNR method for Bengali and Devanagari numerals compared with other existing methods.

A novel methodology to construct compartment models for a circulating fluidized bed riser

During the integrated modeling of a chemical process, there always exists a conflict between model accuracy and computational speed for non-ideal reactors. In view of this, a novel, easy to use, methodology to construct compartment or ideal reactor network models is proposed for a circulating fluidized bed (CFB) riser based on time-series. The pressure drop time-series is sampled during the Multi-Phase Particle-in-Cell (MP-PIC) simulation of the riser. Detailed procedures for mapping the time-series to a directed weighted complex network and the determination of model parameters are presented. Effects of the sampling frequency and division scheme for the time-series on compartment models are also investigated. Findings indicate that the MP-PIC simulation is reliable to record the time-series of pressure drop in the CFB riser. The non-ideal flow in the riser can be equivalently reproduced by a compartment model consisting of eight CSTRs with various sizes and backflow rates. The appropriate sampling frequency for the time-series should be greater than 200 Hz. The optimum division scheme for the time-series is with the predefined threshold value of K = [4025, 4150, 4275, 4400, 4525, 4650, 4775]. It is believed that the proposed methodology based on the time-series can be generalized to develop compartment models for non-ideal flow. It is also expected that compartment models can be readily integrated into a process simulator, e.g., Aspen Plus, to resolve the conflict between model accuracy and computational speed in process simulation, on-line analysis and real-time optimization.

A novel beamforming technique using mmWave antenna arrays for 5G wireless communication networks

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) and beamforming technologies are most likely the integral parts and the key enablers of 5G-and-Beyond (5G-B) wireless communication systems. From that perspective, this manuscript proposes an efficient digital beamforming technique for 5G applications. The suggested approach is formulated based on a reliable sampling technique that effectively samples the interference-plus-noise covariance matrix (IPNCM) to construct an efficient projection matrix (PM). The proposed technique is therefore named interference-plus-noise PM (IPNPM) beamformer. Next, a compact-size rectangular microstrip antenna operating at 49.3 GHz having 1.7 GHz potential bandwidth is designed using CST microwave studio. The designed antenna is subsequently used to model a 32-element uniform linear array (ULA) representing a typical 5G base station. The elements of the modeled ULA are then manually fed with the complex-weights generated from the proposed beamformer to produce high-power beams and deep nulls toward the desired and undesired locations, respectively. To further improve the beamformer performance in terms of minimizing radiating power within undesired regions, an iterative sidelobe level (SLL) suppression technique is developed and integrated with the proposed beamformer. The conducted theoretical analysis justifies that the IPNPM beamformer requires less arithmetic operations than the well-known beamforming approaches. To reveal the benefits of the proposed technique, the array radiation pattern based on the proposed beamformer is plotted and compared with those for the three widely used approaches. An intensive Monte Carlo simulation is also carried out in which the IPNPM beamformer performance is systemically compared with the popular approaches based on the output signal to interference-plus-noise ratio (SINR) criterion. The achieved results demonstrate that the proposed approach is superior and surpasses the rival algorithms in terms of generating deeper nulls, enhanced output SINR, and lower execution time. It is also demonstrated that, unlike the existing SLL reduction techniques, the newly developed SLL suppressor reduces the SLLs of the proposed beamformer to a predefined threshold value (i.e., −24 dB) after few iterations.

Efficient mining of top-k high utility itemsets through genetic algorithms

Mining high utility itemsets is an emerging and very active research area in data mining. The goal is to mine all itemsets with a utility value, in terms of importance to the user, no less than a predefined threshold value. Setting an appropriate threshold value is not trivial, requiring not only multiple trials but also the know-how in the application field. The advantage of algorithms for mining top-k high utility itemsets is they do not require such a utility threshold, but they suffer from very long runtimes and large memory requirements when large input data is considered. We propose a new genetic algorithm for mining top-k high utility itemsets, named TKHUIM-GA (Top-K High Utility Itemset Mining through Genetic Algorithms). It guides the search process by considering the utility of each item to produce initial solutions and to combine solutions accordingly, reducing the runtime and memory consumption as a result. A highly efficient data representation is utilized to reduce memory usage and runtime. A key advantage of TKHUIM-GA is that it works on positive, negative, integer and real unit utility values unlike existing approaches. Experiments on popular benchmark datasets demonstrate the high performance of the proposal regarding the state-of-the-art algorithms.