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The participation of females in economic activity remains a challenge, and received a lot of attention for a better labor policy discourse. The empirical research focused widely on the relationship between female labor force participation (FLFP) and economic development, called the feminization U-shape hypothesis. However, the linear/nonlinear relationship has been questioned due to empirical and methodological anomalies. Hence, this study aims to extend the previous work by reexamining this relationship in Pakistan.

The annual data from 1980 to 2021, the unit root tests augmented Dickey–Fuller and Phillips and Perron, the conventional autoregressive distributed lag bound test approach by including the quadratic-term of GDP per capita and the novel Sasabuchi–Lind–Mehlum (SLM) U test (2010) used for empirical estimation.

The findings revealed the prospects of a long-run nonlinear association between FLFP and economic development in Pakistan. However, an inverse U-shape exists between the female labor force participation rate (FLFPR) and GDP per capita, predicting that FLFP may decline in the future.

The traditional feminization U-shape hypothesis has little empirical support in the case of Pakistan. Therefore, the Government of Pakistan should enhance the enabling environment for females through the provision of better job opportunities, technical skills, on-the-job training and social security benefits during all phases of economic development.

The conventional approach of testing U-shape is insufficient. To the best of the authors’ knowledge, therefore, this study incorporated a wider data set in a time series that is less evident, an advanced methodology SLM U test (2010), to validate the feminization U-shape hypothesis in Pakistan for the first time.

This study aims to discuss the mathematical modelling of a compliance-assisted flapping mechanism and morphable structures for an UAV.

A compliance-assisted flapping wing was designed and modelled mathematically, and signals for the corresponding curves were calculated. The actual wing tip trace of a hummingbird was taken, and variables a, b, h and k were calculated from the image. This data was given to the mathematical model for plotting the graph, and the curve was compared with the input curve. The wing frame and mechanism for control surfaces using morphing is modelled along with single pivoted spine for centre of gravity augmentation and flight orientation control.

The model efficiently approximates the 2D path of the wing using line segments using the muscle and compliance mechanism.

Using a compliance-assisted flapping mechanism offers practical advantages. It allows us to synchronize the flapping frequency with the input signal frequency, ensuring efficient operation. Additionally, the authors can enhance the torque output by using multiple muscle strands, resulting in a substantial increase in the system’s torque-to-weight ratio. This approach proves to be more favourable when compared to conventional methods involving motors or servos, ultimately offering a more efficient and robust solution for practical application.

This model focuses on creating a flexible and tunable mechanism that can at least trace four types of wing traces from the same design, for shifting from one mode of flight to another.

Conventional ornithopter flapping mechanisms are gear or servo driven and cannot trace a wing tip, but some can trace complicated curves, but only one at a time. This model can trace multiple curves using the same hardware, allowing the user to program the curve based on their needs or bird. The authors may vary the shape of the wing tip trace to switch between forward flight, hovering, backward flying, etc., which is not conceivable with any traditional flapping mechanism.

Traditional robot arm trajectory planning methods have problems such as insufficient generalization performance and low adaptability. This paper aims to propose a method to plan the robot arm’s trajectory using the trajectory learning and generalization characteristics of dynamic motion primitives (DMPs).

This study aligns multiple demonstration motion primitives using dynamic time warping; use the Gaussian mixture model and Gaussian mixture regression methods to obtain the ideal primitive trajectory actions. By establishing a system model that improves DMPs, the parameters of the nonlinear function are learned based on the ideal primitive trajectory actions of the robotic arm, and the robotic arm motion trajectory is reproduced and generalized.

Experiments have proven that the robot arm motion trajectory learned by the method proposed in this article can not only learn to generalize and demonstrate the movement trend of the primitive trajectory, but also can better generate ideal motion trajectories and avoid obstacles when there are obstacles. The maximum Euclidean distance between the generated trajectory and the demonstration primitive trajectory is reduced by 29.9%, and the average Euclidean distance is reduced by 54.2%. This illustrates the feasibility of this method for robot arm trajectory planning.

It provides a new method for the trajectory planning of robotic arms in unstructured environments while improving the adaptability and generalization performance of robotic arms in trajectory planning.

The stress concentration factor (SCF) is commonly utilized to assess the fatigue life of a tubular T-joint in offshore structures. Parametric equations derived from experimental testing and finite element analysis (FEA) are utilized to estimate the SCF efficiently. The mathematical equations provide the SCF at the crown and saddle of tubular T-joints for various load scenarios. Offshore structures are subjected to a wide range of stresses from all directions, and the hotspot stress might occur anywhere along the brace. It is critical to incorporate stress distribution since using the single-point SCF equation can lead to inaccurate hotspot stress and fatigue life estimates. As far as we know, there are no equations available to determine the SCF around the axis of the brace.

A mathematical model based on the training weights and biases of artificial neural networks (ANNs) is presented to predict SCF. 625 FEA simulations were conducted to obtain SCF data to train the ANN.

Using real data, this ANN was used to create mathematical formulas for determining the SCF. The equations can calculate the SCF with a percentage error of less than 6%.

Engineers in practice can use the equations to compute the hotspot stress precisely and rapidly, thereby minimizing risks linked to fatigue failure of offshore structures and assuring their longevity and reliability. Our research contributes to enhancing the safety and reliability of offshore structures by facilitating more precise assessments of stress distribution.

Precisely determining the SCF for the fatigue life of offshore structures reduces the potential hazards associated with fatigue failure, thereby guaranteeing their longevity and reliability. The present study offers a systematic approach for using FEA and ANN to calculate the stress distribution along the weld toe and the SCF in T-joints since ANNs are better at approximating complex phenomena than standard data fitting techniques. Once a database of parametric equations is available, it can be used to rapidly approximate the SCF, unlike experimentation, which is costly and FEA, which is time consuming.

Composite materials are effective alternatives for rehabilitating critical members of offshore platforms, bridges, and other structures. The structural response of composite reinforcement greatly depends on the orientation of fibres in the composite material. Joints are the most critical part of tubular structures. Various existing studies have identified optimal reinforcement orientations for a single load component, but none has addressed the combined load case, even though most practical loads are multiplanar.

This study investigates the optimal orientation of composite reinforcement for reducing stress concentration factors (SCF) of tubular KT-joints. The joint reinforcement was modelled and simulated using ANSYS. A parametric study was carried out to determine the effect of the orientations of reinforcement in the interface region on SCF at every 15° offset along the weld toe using linear extrapolation of principal stresses. The impact of orientation for uniplanar and multiplanar loads was investigated, and a general result about optimum orientation was inferred.

It was found that the maximum decrease of SCF is achieved by orienting the fibres of composite reinforcement along the maximum SCF. Notably, the optimal direction for any load configuration was consistently orthogonal to the weld toe of the chord-brace interface. As such, unidirectional composites wrapped around the brace axis, covering both sides of the brace-chord interface, are most effective for SCF reduction.

The findings of this study are crucial for adequate reinforcement of tubular joints using composites, offering a broader and universally applicable optimum orientation that transcends specific joint and load configuration.

Currently, massive multiple-input multiple-output (m-MIMO) antennas are typically designed using complex trial-and-error methods. The purpose of this study is to determine an effective optimization method to achieve more efficient antenna design processes.

This paper presents the design stages of a m-MIMO antenna array compatible with 5G smartphones operating in long term evolution (LTE) bands 42, 43 and 46, based on a specific algorithm. Each antenna element in the designed 10-port m-MIMO antenna array is intended to perfectly cover the three specified LTE bands. The optimization methods used for this purpose include the Nelder–Mead simplex algorithm, covariance matrix adaptation evolution strategy, particle swarm optimization and trust region framework (TRF).

Among the primary optimization algorithms, the TRF algorithm met the defined objectives most effectively. The achieved antenna efficiency values exceeded 60.81% in the low band and 68.39% in the high band, along with perfect coverage of the desired bands, demonstrating the success of the design with the TRF algorithm. In addition, the potential electromagnetic field exposure caused by the designed m-MIMO antenna array is elaborated upon in detail using computational human models through specific absorption rate analysis.

The comparison of four different algorithms (two local and two global) for use in the design of a 10-element m-MIMO antenna array with a complex structural configuration and the success of the design implemented with the selected algorithm distinguish this study from others.

The purpose of this study is to examine how the childhood trauma experiences of CEOs influence firms’ internationalization.

The research used a difference-in-difference method with constructing a treatment group whose chief executive officer (CEO) experienced the great famine in China between the ages of 7 and 11, and a control group whose CEO was born within three years after 1961.

The study reveals a significant inverse correlation between CEOs’ childhood trauma experiences and firm internationalization. However, this correlation is weaker in the case of state-owned enterprises and firms led by CEOs with overseas work experience.

To the best of the authors’ knowledge, this study is the first to extend the theoretical framework to elucidate firms’ internationalization by introducing childhood trauma theory into the field of international business literature. Second, the authors link the literature on the effect of CEO explicit traits and psychological traits on firm internationalization by exploring how CEOs’ childhood trauma experience shapes their risk aversion, which, in turn, influences firm internationalization. Third, the authors address the call for examining the interplay of CEO life experiences by scrutinizing the moderating effect of CEO overseas work experience on the association between CEOs’ childhood trauma exposure and firm internationalization.

Knowledge management is a common practice in organizations, with empirical evidence suggesting that organizations value the breadth of their knowledge capabilities. This study investigated transformational leadership styles and their influence on knowledge management practices and organizational performance.

A quantitative survey was conducted, and data from 270 managers of Islamic banks in the United Arab Emirates were analyzed.

Transformational leadership (TL) considerably affects organizational performance and knowledge management capabilities (KMC).

This study offers critical insights into adopting knowledge management practices and discusses the theoretical and managerial implications of its findings. Furthermore, it elucidates the crucial impact of transformational leadership on organizational performance and KMC.

Due to the vital role of innovation for firms to respond to the change and achieve competitive advantage, the purpose of this study is to investigate the influence of knowledge-oriented leadership (KOL) on innovation performance via the mediating role of knowledge sharing (KS). This study also explores the moderating role of organizational justice in the relationship between KS and innovation performance.

Analysis of moment structures and structural equation modeling are applied to examine the relationship among the latent factors in the proposed research model using data collected from 335 participants in 121 manufacturing and service firms in Vietnam.

The findings revealed that KOL serves as a key precursor to foster innovation performance directly or indirectly through active and passive KS behaviors. In addition, the paper highlights the moderating role of organizational justice in strengthening the impact of KS activities on innovation performance.

By highlighting the important role of KOL for stimulating KS behaviors, this paper provides a valuable understanding and novel approach for firms to improve innovation performance. The research findings support the idea that building a climate of justice is crucial to enhance the effects of KS on innovation performance.

This study contributes to bridging the research gaps in the literature and advances the insights of how KOL directly and indirectly stimulates innovation performance via mediating roles of active and passive KS processes under the climate of justice.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.