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In wireless communication system, use of multiple antennas for different requirements of system will increase the system complexity. However, reconfigurable antenna is maximizing the connectivity to cover different wireless services that operate different frequency range. Pattern reconfigurable antenna can improve security, avoid noise and save energy. Due to their compactness and better performance at different applications, reconfigurable antennas are very popular among the researchers. The purpose of this work, is to propose a novel design of S-shaped antenna with frequency and pattern diversity. The pattern and frequency reconfiguration are controlled via ON/OFF states of the PIN diode.

The geometrical structure of the proposed antenna dimension is 18 × 18 × 0.787 mm³ with εr = 2.2 dielectric constant. Three S-shaped patches are connected to a ring patch through PIN diodes. The approximate circumference of ring patch is 18.84 mm and length of patch is 5 mm, so approximate length of radiating patch is 14.42 mm and effective dielectric constant is 1.93. Conductor backed coplanar waveguide (CPW) is used for feeding. The proposed antenna is designed and simulated on CST microwave studio and fabricated using photolithography process. Measurements have been done in anechoic chamber.

Antenna shows the dual band operation at 2.1 and 3.4 GHz frequency. The first band remains constant at 2.1 GHz resonant frequency and 200–400 MHz impedance bandwidth. Second band is switched at seven different resonant frequencies as 3.14, 3.45, 3.46, 3.68, 3.69, 3.83 and 3.86 GHz with switching of the diodes. The −10 dB bandwidth is more than 1.4 GHz.

Pattern reconfigurability can be achieved using mechanical movement of antenna easily but it is not a reliable approach for planar antennas. Electronic switching method is used in proposed antenna. Antenna size is very small so fabrication is very crucial task. Measured results are deviated from simulation results due to fabrication error and effect of leads of diodes, connecting wires and battery.

The reconfiguration of the proposed antenna is controlled via ON/OFF states of the three PIN diodes. The lower band of 2.1 GHz is fixed, while second band is switched at five different resonant frequencies as 3.27, 3.41, 3.45, 3.55 and 3.88 GHz, with switching of the PIN diodes with all state of diodes and exhibit pattern reconfigurability at 2.1 GHz frequency. At second band center frequency is significantly changed with state of diodes and at 3.4 GHz pattern is also changed with state of diodes, hence antenna exhibits frequency and pattern reconfigurability.

A novel design of pattern and frequency reconfigurable antenna is proposed. Here, work is divided into two parts: first is frequency reconfiguration and second is radiation pattern reconfiguration. PIN diodes as switch are used to select the frequency band and reconfigure the radiation pattern. This proposed antenna design is novel dual band frequency and pattern reconfigurable antenna. It resonates at two distinct frequencies, i.e. 2.1 and 3.4 GHz, and has a pattern tilt from 0° to 355°. The conductor backed CPW feed technique is used for impedance matching.

This paper aims to present a low-cost, edge-fed, windmill-shaped, notch-band eliminator, circular monopole antenna which is practically loaded with a complementary split ring resonator (CSRR) in the middle of the radiating conductor and also uses a partial ground to obtain wide-band performance.

To compensate for the reduced value of gain and reflection coefficient because of the full (complete) ground plane at the bottom of the substrate, the antenna is further loaded with a partial ground and a CSRR. The reduction in the length of ground near the feed line improves the impedance bandwidth, and introduced CSRR results in improved gain with an additional resonance spike. This results in a peak gain 3.895dBi at the designed frequency 2.45 GHz. The extending of three arms in the circular patch not only led to an increase of peak gain by 4.044dBi but also eliminated the notch band and improved the fractional bandwidth 1.65–2.92 GHz.

The work reports a –10dB bandwidth from 1.63 GHz to 2.91 GHz, which covers traditional coverage applications and new specific uses applications such as narrow LTE bands for future internet of things (NB-IoT) machine-to-machine communications 1.8/1.9/2.1/2.3/2.5/2.6 GHz, industry, automation and business-critical cases (2.1/2.3/2.6 GHz), industrial, society and medical applications such as Wi-MAX (3.5 GHz), Wi-Fi3 (2.45 GHz), GSM (1.9 GHz), public safety band, Bluetooth (2.40–2.485 GHz), Zigbee (2.40–2.48Ghz), industrial scientific medical (ISM) band (2.4–2.5 GHz), WCDMA (1.9, 2.1 GHz), 3 G (2.1 GHz), 4 G LTE (2.1–2.5 GHz) and other personal communication services applications. The estimated RLC electrical equivalent circuit is also presented at the end.

Because of full coverage of Bluetooth, Zigbee, WiFi3 and ISM band, the proposed fabricated antenna is suitable for low power, low data rate and wireless/wired short-range IoT-enabled medical applications.

The antenna is fabricated on a piece (66.4 mm × 66.4 mm × 1.6 mm) of low-cost low profile FR-4 epoxy substrate (0.54 λ_g × 0.54 λ_g) with a dielectric constant of 4.4, a loss tangent of 0.02 and a thickness of 1.6 mm. The antenna reflection coefficient, impedance and VSWR are tested on the Keysight technology (N9917A) vector network analyzer, and the radiation pattern is measured in an anechoic chamber.

This paper aims to propose a two-element dual fed ultra-wideband (UWB) multiple input multiple output (MIMO) antenna system with no additional decoupling structures. The antenna operates from 3.1 to 10.6 GHz. The antenna finds its usage in on-body wearable device applications.

The antenna system measures 63.80 × 29.80 × 0.7 mm. The antenna radiating element is designed by using a modified dumbbell-shaped structure. Jean cloth material is used as substrate. The isolation improvement is achieved through spacing between two elements.

The proposed antenna has a very low mutual coupling of S₂₁ < −20 dB and impedance matching of S₁₁ < −10 dB. The radiation characteristics are stable in the antenna operating region. It provides as ECC < 0.01, diversity gain >9.9 dB. The antenna offers low average specific absorption rate (SAR) of 0.169 W/kg. The simulated and measured results are found to be in reasonable match.

The MIMO antenna is proposed for on-body communication, hence, a very thin jean cloth material is used as substrate. This negates the necessity of additional material usage in antenna design and the result range indicates good diversity performance and with a low SAR of 0.169 W/kg for on-body performance. This makes it a suitable candidate for textile antenna application.

The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

With the recognition that generating economic growth is not the same as sustaining it, the challenge to catch-up and growth literature is discerning between these processes. Recent research suggests that the decline in the frequency of “shrinking” episodes is more important for long-term development than higher growth rates. By using a framework centred around social capabilities, this study aims to investigate the effects of income inequality and poverty on economic shrinking frequency, as opposed to previous literature that has exclusively had a growth focus. The aim is to investigate how and why some societies might be more resilient to economic shrinking.

The research is a quantitative study, and the authors build a longitudinal data set including 23 developing countries throughout 42 years to test the paper’s purpose. This study uses country and period fixed-effects specifications as well as cross-sectional graphical representations to investigate the relationship between proxies of economic inclusivity and the frequency of shrinking episodes.

The authors demonstrate that while inclusive societies are more resilient to shrinking overall, it is changes in poverty levels, but not changes in income inequality, that appear to be correlated with economic shrinking frequency. Inequality, while still an important element to explain countries’ growth potential as an initial condition, does not seem to make the sample more resilient to shrinking. The authors conclude that the mechanisms in which poverty and inequality are correlated with the catch-up process must run through different channels. Ultimately, processes that explain growth may intersect but not always overlap with the ones that explain resilience to shrinking.

The need for inclusive growth in long-term development has been championed for decades, yet inclusion has seldom been explored from the shrinking perspective. Though poverty reduction is already an important mainstream political objective, this paper differentiates itself by providing an alternate viewpoint of why this is important. Income inequality could have more of an economic growth limiting effect, while poverty reduction could be required to build resilience to economic shrinking. Developing countries will need both growth and resilience to shrinking, to catch-up with higher-income economies, which policymakers might need to balance carefully.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

In this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.

Application of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.

Results achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.

The methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.

Pune is a prominent information technology (IT) hub in India, where snacking has become a customary practice among IT professionals. This study aims to determine the pattern and factors associated with snacking among IT professionals from various multinational corporations (MNCs) in Pune, Maharashtra, India.

This cross-sectional study considered 404 IT professionals aged 21 to 50 years. A convenient sampling method was adopted to administer a validated questionnaire. Information on snacking patterns and factors associated with snacking were recorded. Descriptive and inferential statistics were used to analyze the data with p = 0.05. The participation was voluntary, and confidentiality was ensured.

The mean age of the participants was 31 ± 7.9 years. Almost half (51.5%) of the participants engaged in daily snacking. The sociodemographic factors such as younger age (0.000), marital status (p = 0.001), salary package (p = 0.006), living situation (p = 0.05), designation (p = 0.042) and work experience (p = 0.001) significantly related with the unhealthy snacking pattern scores. Daily snacking was significantly associated with hunger (p = 0.001), stress (p = 0.001), weight (p = 0.000), peer influence (p = 0.041) and taste (p = 0.001). Hunger, stress, taste, peer influence, boredom and weight were significantly (p = 0.05) associated with unhealthy snacking patterns.

The mean age of the participants was 31 ± 7.9 years. Almost half (51.5%) of the participants engaged in daily snacking. The sociodemographic factors such as younger age (0.000), marital status (p = 0.001), salary package (p = 0.006), living situation (p = 0.05), designation (p = 0.042) and work experience (p = 0.001) significantly related with the unhealthy snacking pattern scores. Daily snacking was significantly associated with hunger (p = 0.001), stress (p = 0.001), weight (p = 0.000), peer influence (p = 0.041) and taste (p = 0.001). Overall, hunger, stress, taste, peer influence, boredom and weight were significantly (p = 0.05) associated with unhealthy snacking patterns such as snacking in between, prioritizing taste over nutrition, exclusion of fruits and vegetables in snacks, lack of control over snacking and snacking habit.

This study uniquely identifies the snacking pattern of IT professionals from Pune, India, which primarily includes unhealthy snacking. Various socio-demographic and other factors such as hunger, taste, stress, boredom, convenience, weight and peer influence, were associated with unhealthy snacking. Understanding the snacking pattern and its determinants can help create nutrition interventions to promote healthy snacking and decrease the risk of noncommunicable diseases in IT professionals.

This study explored how the use of different social media is related to subjective well-being among university students during the COVID-19 pandemic in Japan.

We surveyed 1,681 university students in the Kanto region of Japan in May 2021 to investigate how social media use relates to subjective well-being. We also examined the effects of self-consciousness and friendship, self-presentation desire, generalized trust, online communication skills, depression tendency and social support from others.

The responses revealed 15 possible patterns of social media usage on four widely used social media in Japan (LINE, Twitter, Instagram and Facebook). We selected users with the top five patterns for further statistical analyses: LINE/Twitter/Instagram/Facebook, LINE/Twitter/Instagram, LINE/Twitter, LINE/Instagram and LINE only. Overall, self-establishment as a factor of self-consciousness and friendship, and social support from others had positive effects on the improvement of subjective well-being, whereas depression tendency had negative effects on their subjective well-being regardless of their usage patterns, of which the results of social support from others and depression tendency were consistent with the results of previous studies. Regarding other factors, they had different effects on subjective well-being due to different patterns. Effects on subjective well-being from self-indeterminate and self-independency as factors of self-consciousness and friendship, praise acquisition, self-appeal and topic avoidance as factors of self-presentation desire were observed.

This is among the earliest studies on the relationship between young generations’ social media use and subjective well-being through social media usage patterns during the COVID-19 pandemic in Japan.

Compact and wideband antennas are the need of modern wireless systems that preferably work with compact, low-profile and easy-to-install devices that provide a wider coverage of operating frequencies. The purpose of this paper is to propose a novel compact and ultrawideband (UWB) microstrip patch antenna intended for high frequency wireless applications.

A square microstrip patch antenna was initially modeled on finite element method-based electromagnetic simulation tool high frequency structure simulator. It was then loaded with a rectangular slit and Koch snowflake-shaped fractal notches for bandwidth enhancement. The fabricated prototype was tested by using vector network analyzer from Agilent Technologies, N5247A, Santa Clara, California, United States (US).

The designed Koch fractal patch antenna is highly compact with dimensions of 10 × 10 mm only and possesses UWB characteristics with multiple resonances in the operating band. The −10 dB measured impedance bandwidth was observed to be approximately 13.65 GHz in the frequency range (23.20–36.85 GHz).

Owing to its simple and compact structure, positive and substantial gain values, high radiation efficiency and stable radiation patterns throughout the frequency band of interest, the proposed antenna is a suitable candidate for high frequency wireless applications in the K (18–27 GHz) and Ka (26.5–40 GHz) microwave bands.