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Unlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.

The paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.

Simulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.

Conventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.

This paper aims to answer questions pertinent to whether or not services provided by smart hotels are really what customers are looking for, as well as to ascertain what are some unintended experiences guests may encounter. In essence, to the best of the authors’ knowledge, this research is the first in the field to acknowledge the paradox of smart service.

This inquiry adopts a qualitative approach with data-driven from online customer reviews and semistructured interviews. Thematic analysis was undertaken to interpret review comments.

Results point to a new phenomenon, which is coined as the smartness paradox. In particular, customers on one hand enjoy an array of smart-infused experiences that jointly offer patrons a sense of a futuristic lifestyle. On the other hand, smart devices superimpose a number of hindrances that bring guests dismay and annoyance.

This investigation brings smart service failure to the fore to highlight several key failure themes that could jeopardize the entire operation with debased customers’ satisfaction and loyalty inclination.

The smartness-paradox framework used in the present inquiry entails both approach and avoidance consequences customers enact depending on their smart experiences.

This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

This chapter explores the nature of military law and IHL during the cold war period. It explores what treaties were completed, Additional Protocols I and II of the 1949 Geneva Conventions, the ad hoc international tribunals of the 1990s and 2000s, and examines the ICJ’s ruling of the legality of nuclear weapons.

The number of university students seeking mental health support is rapidly increasing. To provide additional psychological support to students accessing well-being services, this study aimed to pilot a mental health Web application (app) called Orpheus®.

Guided by student consultations, a multi-methods approach was adopted, including an examination of in-app data, chart reviews of routinely collected student information and interviews with mental health practitioners. Usage data were analysed descriptively. Changes in mental health outcomes were examined using means, standard deviations and reliable change indices for anxiety and depression scores. Inductive and deductive thematic analysis was conducted on qualitative data from staff interviews and student feedback.

A total of 26 students registered an account with 39 completed app visits. On 37 of the 39 (94.9%) occasions, students reported reductions in the intensity of unwanted negative emotions. Statistically significant reductions in the average pooled anxiety and depression scores were observed. Of the 15 students who completed pre- and post-routine outcome measures, between 20% and 60% showed reliable and meaningful symptom improvements. Students reported that the app was helpful and easy to set up and use, with no adverse events. Practitioners highlighted barriers and facilitators related to the technology features, situational contexts and individual differences.

Integrating Orpheus in real-world settings resulted in promising implementation processes, potential for future uptake and positive outcomes. However, ongoing research, staff training and app testing are needed to further improve the implementation processes for digital mental health interventions.

This paper aims to propose a stable and precise recognition and pose estimation method to deal with the difficulties that industrial parts often present, such as incomplete point cloud data due to surface reflections, lack of color texture features and limited availability of effective three-dimensional geometric information. These challenges lead to less-than-ideal performance of existing object recognition and pose estimation methods based on two-dimensional images or three-dimensional point cloud features.

In this paper, an image-guided depth map completion method is proposed to improve the algorithm's adaptability to noise and incomplete point cloud scenes. Furthermore, this paper also proposes a pose estimation method based on contour feature matching.

Through experimental testing on real-world and virtual scene dataset, it has been verified that the image-guided depth map completion method exhibits higher accuracy in estimating depth values for depth map hole pixels. The pose estimation method proposed in this paper was applied to conduct pose estimation experiments on various parts. The average recognition accuracy in real-world scenes was 88.17%, whereas in virtual scenes, the average recognition accuracy reached 95%.

The proposed recognition and pose estimation method can stably and precisely deal with the difficulties that industrial parts present and improve the algorithm's adaptability to noise and incomplete point cloud scenes.

This paper aims to investigate the influence of “BroncoCut-X” (copper core-ZnCu50 coating) electrode on the machining of Ti-3Al-2.5V in view of its extensive use in aerospace and medical applications. The machining parameters are selected as Spark-off Time (ST_off), Spark-on Time (ST_on), Wire-speed (S_w), Wire-Tension (WT) and Servo-Voltage (S_v) to explore the machining outcomes. The response characteristics are measured in terms of material removal rate (MRR), average kerf width (KW) and average-surface roughness (SA).

Taguchi’s approach is used to design the experiment. The “AC Progress V2 high precision CNC-WEDM” is used to conduct the experiments with ϕ 0.25 mm diameter wire electrode. The machining performance characteristics are examined using main effect plots and analysis of variance. The grey-relation analysis and fuzzy interference system techniques have been developed to combine (called grey-fuzzy reasoning grade) the experimental response while Rao-Algorithm is used to calculate the optimal performance.

The hybrid optimization result is obtained as ST_off = 50µs, ST_on = 105µs, S_w = 7 m/min, WT = 12N and S_v=20V. Additionally, the result is compared with the firefly algorithm and improved gray-wolf optimizer to check the efficacy of the intended approach. The confirmatory test has been further conducted to verify optimization results and recorded 8.14% overall machinability enhancement. Moreover, the scanning electron microscopy analysis further demonstrated effectiveness in the WEDMed surface with a maximum 4.32 µm recast layer.

The adopted methodology helped to attain the highest machinability level. To the best of the authors’ knowledge, this work is the first investigation within the considered parametric range and adopted optimization technique for Ti-3Al-2.5V using the wire-electro discharge machining.

Effective customer journey design (ECJD) is considered a key variable in customer experience management and an essential source of brand meaning and pro-brand behavior. Although previous research has confirmed its importance for driving brand attitudes and loyalty, the role of consumer-brand identification as a social identity-based influence in this relationship has not yet been discussed. Drawing on construal level and social identity theories, this paper aims to investigate whether effective journeys and the resulting overall journey experience are equally powerful in driving brand loyalty among customers with different levels of consumer-brand identification.

The present article develops and tests a research model using data from the European and US service sectors (N = 1,454) to investigate how and when ECJD affects service brand loyalty.

Across two cultural contexts, four service industries and 33 service brands, the results reveal that ECJD is a crucial driver of service brand loyalty for customers with low consumer-brand identification. Moreover, the findings show that different aspects of journey effectiveness positively impact the valence of customers’ experience related to those journeys – a process that is ultimately decisive for their brand loyalty.

This study is unique because it generates theoretical and practical knowledge by combining the literature streams of customer journey design, customer experience and branding. Furthermore, this work demonstrates that consumer-brand identification is a critical boundary condition to be considered in the relationship between ECJD and brand loyalty in services.

This chapter examines how the nature of World War I catalyzed significant changes in the laws of war, the Treaty of Versailles, the failed Leipzig Trials, and the multiple treaties enacted in the 1920s, with particular focus on the Geneva Convention of 1929.