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Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

The Mongol invasion caused widespread destruction in many cities; this research studies the destruction course of cities after the Mongol invasion and their reconstruction during the reform period, the change that it brought to the cityscapes of Iranian cities and the difference between the urbanscape of the cities that flourished or were re-established after these destructions with the cities prior to them.

The method of research used is historical interpretation/analysis. The historical texts of pre-Mongolian Persia and texts from the Ilkhanid era are studied, references to the shapes and appearances of Iranian cityscapes are classified, and with the help of contemporary interpretations and existing physical evidence, the urbanscape of these two periods are redrawn and compared to each other.

The selection of scenic meadows to build the city, the presence of many gardens in the urban patterns and the construction of satellite towns around large cities have been the effects of the Mongol tradition of (Yurt) tent-dwelling on Iranian cities during the reforms. The declining population and the massive migration of artists together with the rethinking of the rulers made the existence of dense cities with multi-storey houses less likely. The tradition of pre-designing the city and buildings and designing open and right-angled pathways continued after the Mongol invasion.

The prevailing belief is that during the Mongol era, only the destruction of cities took place and the Mongols did not create any cities and had no influence on urban development. This research aims to challenge that.

As host to over one million Syrian refugees, Lebanon continues to experience challenges addressing the needs of refugee families. This research examined the experiences of Syrian families with the refugee support system in Lebanon. The purpose of this study was to better understand the strengths and gaps in existing mechanisms of support for these Syrian families, including informal support from family, neighbors and community and more formalized support provided through entities such as nongovernmental organizations and United Nations agencies.

Data were collected from 46 families displaced by the war and living in Lebanon (N = 351 individuals within 46 families). Collaborative family interviews were conducted with parents, children and often extended family.

The data identified both strengths and gaps in the refugee support system in Lebanon. Gaps in the refugee support system included inadequate housing, a lack of financial and economic support, challenges with a lack of psychosocial support for pregnant women and support for disabled youth. Despite these challenges, families and community workers reported informal community support as a strong mediator of the challenges in Lebanon. Furthermore, the data find that organizations working with Syrian families are utilizing informal community support through capacity building, to create more effective and sustainable support services.

This study provides an overview of strengths and gaps in supports identified by refugees themselves. The research will inform the development and improvement of better support systems in Lebanon and in other refugee–hosting contexts.

This study aims to propose a more precise method for robust design optimization of mechanical structures with black-box problems, while also considering the efficiency of uncertainty analysis.

The method first introduces a dual adaptive chaotic flower pollination algorithm (DACFPA) to overcome the shortcomings of the original flower pollination algorithm (FPA), such as its susceptibility to poor accuracy and convergence efficiency when dealing with complex optimization problems. Furthermore, a DACFPA-Kriging model is developed by optimizing the relevant parameter of Kriging model via DACFPA. Finally, the dual Kriging model is constructed to improve the efficiency of uncertainty analysis, and a robust design optimization method based on DACFPA-Dual-Kriging is proposed.

The DACFPA outperforms the FPA, particle swarm optimization and gray wolf optimization algorithms in terms of solution accuracy, convergence speed and capacity to avoid local optimal solutions. Additionally, the DACFPA-Kriging model exhibits superior prediction accuracy and robustness contrasted with the original Kriging and FPA-Kriging. The proposed method for robust design optimization based on DACFPA-Dual-Kriging is applied to the motor hanger of the electric multiple units as an engineering case study, and the results confirm a significant reduction in the fluctuation of the maximum equivalent stress.

This study represents the initial attempt to enhance the prediction accuracy of the Kriging model using the improved FPA and to combine the dual Kriging model for uncertainty analysis, providing an idea for the robust optimization design of mechanical structure with black-box problem.

The most important need after natural disasters is the sheltering. However, most of the existing temporary shelters do not meet all requirements for long-term use and not provide adequate flexibility within the space. This paper aims to develop a transitional postdisaster shelter transforming from a closed shape to an expanded form in response to changing functional and spatial needs of disaster victims. The study also proposes alternative unit combinations for various functions, and settlement layouts to create a comfortable living environment for occupants.

The research methodology is based on theoretical and design frameworks which requires inductive and deductive approaches. Forming the background of the study, the theoretical framework consists of four parts which are literature review on temporary shelters presenting state-of-the-art; determination of design guidelines and strategies based on shelter standards; identification of technical requirements; and analysis of existing temporary shelters. Having three parts, the design framework includes design of transformable transitional shelter based on three-dimensional modeling, creation of different unit combinations to be used for various purposes and development of settlement layouts as case studies.

The analysis conducted in this study demonstrates that most of the existing temporary shelters have limited geometric configurations and major problems in terms of their performance, transportation and storage. On the other hand, the transformable shelter proposed by the authors can provide form and spatial flexibilities thanks to its expansion properties, occupy less space for transportation, easily be transported to any desired location in its compact state and be customized according to user needs. Several units can be combined either to serve larger families or to be used for different functions.

This paper contributes to the literature as presenting not only a theoretical framework on temporary shelters but also a design framework on transformable shelter design for the ones who are willing to develop similar transformable shelters based on the determined guidelines, strategies and requirements.

As the consumer-centric approach is evolving in the 21st century, especially in the post-COVID-19 era, people seek unique experiences. Adopting co-creation in promoting a destination implies involving tourists, stakeholders and organisers in creating value for a product or service. The innovative strategy of co-creating experiences encourages tourists' engagement, leading to destination promotion. Some notable examples of co-creation in tourism are gastronomic tours, virtual tours and travel guides. This chapter aims at the significance of co-creating experiences at events that lead to destination promotion. Co-creation of experiences at events brings the spotlight from the stage to the audience and is considered the future of the experience economy. The study presents a case study of Jal Mahotsav in Madhya Pradesh, India. The study highlights the multi-stakeholder approach adopted by the authorities to co-create the event experience.

This paper aims to tell something of the story of the “Hawkspur Experiment” (1936–1941), a therapeutic camp organised early in the modern history of therapeutic community as an intervention into the lives of young men who were viewed to be at risk of delinquency (Wills, 1967). Although it was to have a remarkable influence on group and therapeutic community practice and theory, the authors argue that its influence is not as well-remembered nor incorporated into contemporary therapeutic understanding and discussion as it should be.

This paper is a historical reflection based on systematic examination of the clinical and administrative records of Hawkspur Camp for men, and supporting documents held in the Planned Environment Therapy Archive. In addition, the authors use published primary and secondary sources.

Hawkspur Camp was a cross-disciplinary enterprise which brought together psychoanalytic thinking, social work, an interest in groups, political activism, a concern with the dynamics and working of democracy and the application of emergent social science methods. It was overtly an intervention into the criminal justice system but was also an intentional exploration of the therapeutic benefits of community living and of a “pioneering” lifestyle; a rigorous experiment in how psychoanalytic ideas might be used in group residential settings; and a politically grounded exploration of participative democracy as a fundamental therapeutic principle.

To the best of the authors’ knowledge, this paper presents the first findings from a systematic study of the records of Hawkspur Camp.

Aspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use graph-based mechanisms, which reduce prediction accuracy and introduce large amounts of noise. The other problem with graph-based mechanisms is that for some context words, the feelings change depending on the aspect, and therefore it is impossible to draw conclusions on their own. ASA is challenging because a given sentence can reveal complicated feelings about multiple aspects.

This research proposed an optimized attention-based DL model known as optimized aspect and self-attention aware long short-term memory for target-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect extraction and classification. Aspect extraction is done using a double-layered convolutional neural network (DL-CNN). The optimized aspect and self-attention embedded LSTM (OAS-LSTM) is used to classify aspect sentiment into three classes: positive, neutral and negative.

To detect and classify sentiment polarity of the aspect using the optimized aspect and self-attention embedded LSTM (OAS-LSTM) model. The results of the proposed method revealed that it achieves a high accuracy of 95.3 per cent for the restaurant dataset and 96.7 per cent for the laptop dataset.

The novelty of the research work is the addition of two effective attention layers in the network model, loss function reduction and accuracy enhancement, using a recent efficient optimization algorithm. The loss function in OAS-LSTM is minimized using the adaptive pelican optimization algorithm, thus increasing the accuracy rate. The performance of the proposed method is validated on four real-time datasets, Rest14, Lap14, Rest15 and Rest16, for various performance metrics.