Focusing on the years between 1895 and 1897, this article reconstructs what happened after the arrival of Young Turk revolutionaries into the cities of the Danubian hinterland, particularly centering on Rusçuk (Ruse in today’s Bulgaria). In tracing the footsteps of İbrahim Temo and Mustafa Ragıp, two self-exiled figures from İstanbul, this study captures a particular moment when the Danubian cities became the hotbed of transnational radicalism, as a number of assassination plots began to be hatched by Muslim revolutionaries. A well-connected port city serviced by regular steamship links, Rusçuk was where professional revolutionaries met with the local Muslims, much to the ire of Ottoman diplomats in the region. In capturing their encounters, the goal is to point to the significance of Young Turk activities in the Balkans before the turn of the century, a phase which remains understudied in the existing literature. By focusing on a secondary port city that became home to failed assassination plots, this article also seeks to contribute to ongoing discussion in global history that warns against narratives of unhindered globalization. In studying fin-de-siecle radicalization, I hope to contribute to these debates by reflecting upon the limits of globalization as a productive field of historical inquiry.

A novel wideband nonuniform metasurface antenna with stable gain is demonstrated. The nonuniform metasurface is composed of square patches and rings and is excited by a slot antenna. Based on characteristic mode analysis, two characteristic modes with same current direction are selected to achieve stable radiation performance in a wide frequency range. The wideband operation is achieved by assembling the resonant modes of the metasurface and slot antenna. The measured results show that the −10 dB impedance bandwidth of the proposed antenna is from 4.3 to 8.4 GHz (64.57%), and the 2 dB gain bandwidth is from 4.3 to 6.2 GHz (36.2%) with a peak gain value of 9.42 dBi. Moreover, broadside radiation performance is achieved.

Political campaigns increasingly conduct experiments to learn how to persuade voters. Little research has considered the implications of this trend for elections or democracy. To probe these implications, we analyze a unique archive of 146 advertising experiments conducted by US campaigns in 2018 and 2020 using the platform Swayable. This archive includes 617 advertisements produced by 51 campaigns and tested with over 500,000 respondents. Importantly, we analyze the complete archive, avoiding publication bias. We find small but meaningful variation in the persuasive effects of advertisements. In addition, we find that common theories about what makes advertising persuasive have limited and context-dependent power to predict persuasiveness. These findings indicate that experiments can compound money’s influence in elections: it is difficult to predict ex ante which ads persuade, experiments help campaigns do so, but the gains from these findings principally accrue to campaigns well-financed enough to deploy these ads at scale.

Maternal prenatal and postnatal psychological distress, including depression and anxiety, may affect children’s cognitive development. However, the findings have been inconsistent. We aimed to use the dataset from the Japan Environment and Children’s Study, a nationwide prospective birth cohort study, to examine this association. We evaluated the relationship between the maternal six-item version of the Kessler Psychological Distress Scale (K6) scores and cognitive development among children aged 4 years. K6 was administered twice during pregnancy (M-T1; first half of pregnancy, M-T2; second half of pregnancy) and 1 year postpartum (C-1y). Cognitive development was assessed by trained testers, using the Kyoto Scale of Psychological Development 2001. Multiple regression analysis was performed with the group with a K6 score ≤ 4 for both M-T1 and M-T2 and C-1y as a reference. Records from 1,630 boys and 1,657 girls were analyzed. In the group with K6 scores ≥ 5 in both M-T1 and M-T2 and C-1Y groups, boys had significantly lower developmental quotients (DQ) in the language-social developmental (L-S) area (partial regression coefficient: −4.09, 95% confidence interval: −6.88 – −1.31), while girls did not differ significantly in DQ for the L-S area. Among boys and girls, those with K6 scores ≤ 4 at any one or two periods during M-T1, M-T2, or C-1y did not have significantly lower DQ for the L-S area. Persistent maternal psychological distress from the first half of pregnancy to 1 year postpartum had a disadvantageous association with verbal cognitive development in boys, but not in girls aged 4 years.

The recognizing underwater targets is a crucial component of autonomous underwater vehicle patrols and detection efforts. In the process of visual image recognition in real underwater environment, the spatial and semantic features of the target often appear to different degrees of loss, and the scarcity of specific types of underwater samples leads to unbalanced data on categories. This kind of problem makes the target features appear weak and seriously affects the accuracy of underwater target recognition. Traditional deep learning methods based on data and feature enhancement cannot achieve ideal recognition effect. Based on the above difficulties, this paper proposes an improved feature enhancement network for weak feature target recognition. Firstly, a multi-scale spatial and semantic feature enhancement module is constructed to extract the feature information of the extraction target accurately. Secondly, this paper solves the influence of target feature distortion on classification through multi-scale feature comparison of positive and negative samples. Finally, the Rank & Sort Loss function was used to train the depth target detection to solve the problem of recognition accuracy under highly unbalanced sample data. Experimental results show that the recognition accuracy of the proposed method is 2.28% and 3.84% higher than that of the existing algorithms in the recognition of underwater fuzzy and distorted target images, which demonstrates the effectiveness and superiority of the proposed method.

Drosophila suzukii (Matsumura) is an exotic pest of economic importance that affects several soft-skinned fruits in Mexico. Previously, we found that yellow or yellow-green rectangular cards inside a transparent trap baited with attractants improved D. suzukii capture. In this study, we evaluated the influence of rectangular cards with different yellow shades inside a transparent multi-hole trap baited with apple cider vinegar (ACV) on D. suzukii capture in the field. Second, we tested whether ACV-baited traps with cards of other geometric shapes affected D. suzukii catches compared to traps with rectangular cards. Third, we evaluated the effects of commercial lures combined with a more efficient visual stimulus from previous experiments on trapping D. suzukii flies. We found that ACV-baited traps plus a yellow-shaded rectangle card with 67% reflectance at a 549.74 nm dominant wavelength captured more flies than ACV-baited traps with yellow rectangle cards with a higher reflectance. Overall, ACV-baited traps with rectangles and squares caught more flies than did ACV-baited traps without visual stimuli. The traps baited with SuzukiiLURE-Max, ACV and Z-Kinol plus yellow rectangles caught 57, 70 and 101% more flies, respectively, than the traps baited with the lure but without a visual stimulus.

Responsible leadership (RL) has become a buzz word in the current lexicon of business and politics, but there is still limited agreement on the components, scope, and characteristics. The confusion is rooted, in part, in the dominance of normative perspectives that take RL as a universal phenomenon. However, embedded in a specific culture, RL cannot be understood fully without understanding the moral traditions of that culture. In this article, we used a case study method to explore how RL is understood and practiced in China. Taking the role theory perspective, we conducted in-depth interviews with 9 highly regarded responsible executive leaders and 92 stakeholders in and outside of their companies who were well acquainted with the leaders. Our findings reveal that in China, the moral character of leaders guides them to define and take responsibility for themselves, their employees, companies, and external stakeholders. The five dimensions of RL we identified and the relationships among the dimensions include characteristics that reflect Chinese culture, such as strong sentiment for the nation, self-discipline, developing employees philosophically, and ‘jun zi wu ben’ (a gentleman should focus on fundamental matters). We conclude by discussing the implications of our study for RL research and practice.

This paper proposes a lightweight frequency selective surface polarization-insensitive wideband metamaterial absorber in C band and X band that employs only a few resistive elements. The proposed absorber is embodied with four quadrature slotted inner circular patch, which is horizontally and vertically bisected, and outer concentric copper rings of 0.035 mm thickness are attached with four lumped resistors placed at 90° apart. A slotted inner circular patch provides significant inductive and capacitive loading. The absorption bandwidth of 8.02 GHz with more than 90% absorption is observed from 5.69 to 13.71 GHz under normal incidence and maintains almost same absorptivity range under oblique incidence up to 45° in both transverse electric mode and transverse magnetic mode. The designed metamaterial absorber is fabricated and measured using free space measurement technique. The actual experiments and the simulated ones are in good agreement.

Circular antenna array (CAA) is one of the most widely used antenna array designs. This paper addresses the design challenges of the CAA with the non-uniform single ring, which is placed in an X-Y plane with the best sidelobe level (SLL) and improved first null beamwidth (FNBW). It has been solved using differential evolution, craziness-based particle swarm optimization (CRPSO), and novel particle swarm optimization (NPSO) techniques. An optimal combination of feeding current and inter-element spacing provides an array pattern with the best SLL and improved FNBW, as well as some other parameter calculations of the antenna array like maximum directivity, maximum effective aperture, total effective aperture, maximum beam area, total beam area, circumference, and radius of the CAAs using these techniques. There are six designs of CAAs with different antenna elements (i.e., 10-, 12-, 16-, 20-, 36-, and 64-elements) which have been taken into account. Simulations are done in MATLAB. Based on various simulation results, we can analyze the performance of SLL and FNBW with other parameters using NPSO and compare them with different techniques of CAAs, as shown in the numerical analysis and simulation result section.

Conjoint analysis is a popular experimental design used to measure multidimensional preferences. Many researchers focus on estimating the average marginal effects of each factor while averaging over the other factors. Although this allows for straightforward design-based estimation, the results critically depend on the ways in which factors interact with one another. An alternative model-based approach can compute various quantities of interest, but requires correct model specifications, a challenging task for conjoint analysis with many factors. We propose a new hypothesis testing approach based on the conditional randomization test (CRT) to answer the most fundamental question of conjoint analysis: Does a factor of interest matter in any way given the other factors? Although it only provides a formal test of these binary questions, the CRT is solely based on the randomization of factors, and hence requires no modeling assumption. This means that the CRT can provide a powerful and assumption-free statistical test by enabling the use of any test statistic, including those based on complex machine learning algorithms. We also show how to test commonly used regularity assumptions. Finally, we apply the proposed methodology to conjoint analysis of immigration preferences. An open-source software package is available for implementing the proposed methodology. The proposed methodology is implemented via an open-source software R package CRTConjoint, available through the Comprehensive R Archive Network https://cran.r-project.org/web/packages/CRTConjoint/index.html.

This paper considers semiparametric sieve estimation in high-dimensional single index models. The use of Hermite polynomials in approximating the unknown link function provides a convenient framework to conduct both estimation and variable selection. The estimation of the index parameter is formulated from solutions obtained by the routine penalized weighted linear regression procedure, where the weights are used in order to tackle the unbounded support of the regressors. The resulting index parameter estimator is shown to be consistent and sparse, and the asymptotic normality for the estimators of both the index parameter and the link function is established. To perform variable selection in the ultra-high dimension case, we further suggest a forward regression screening method, which is shown to enjoy the sure independence screening property. This screening procedure can be used before the penalized variable selection to reduce the burden of dimensionality. Numerical results show that both the variable selection procedures and the associated estimators perform well in finite samples.

The Muscle Analyzer System (MAS) project wants to create a standalone microwave device that can assess the muscle quality, called the MAS device. To achieve that an algorithm that can derive the properties of skin, fat and muscle from the measurements is needed. This paper presents a machine learning algorithm that aims to do precisely that. The algorithm relies on first predicting the skin using the data from the MAS device, then predicting the fat again using the data from the MAS but also the predicted skin value and lastly the muscle is predicted using the microwave data together with the skin and fat predictions. Data have been collected in phantom experiments, materials that mimick the dielectric properties of human tissues. The algorithm is trained to predict the properties of said phantoms. The results show that the prediction for skin thickness works well, the fat thickness prediction is okay but the muscle prediction struggles. This is partly due to the error from the skin and fat layers are propagated to the muscle layer and partly because the muscle layer is farthest away from the sensor, which makes getting information from that layer harder.

This paper will offer a new defense in response to the problem of natural evil, called the Perfect Will defense. The defense argues that in sustaining the universe, God conforms the system of physical laws to his intellect and will. Yet, God could not fully conform the system of laws (for our universe) to his intellect and will without simultaneously forcing people into a loving relation with God. Yet, since God would not force people to love him, God must thereby initially create people in a universe that has a system of laws that is only partially conformed to God’s intellect and will. However, while a universe with a system of laws that is only partially conformed to God’s intellect and will allows for people to exercise their freedom over their relation with God, it also results in the occurrence of natural evils. The paper will argue that once this defense is fully developed, it is able to account for why God allows for natural evils to occur within the universe. The paper will outline the defense, as well as respond to the defense’s major objections.