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Although there has been considerable growth in the higher education systems of Turkey and China in about the last two decades, there is still a room for development in enabling equity in all regions, increasing opportunities and resources regardless of socio-economic status or gender differences. Students coming from disadvantaged backgrounds do not have enough tools to change their fate for the better due to the accumulated barriers they face. Given this background, the chapter discusses how the barriers to equitable HE admissions relate to each other and which one has a greater negative impact over the Accumulated Conversion Barriers Modal we propose defined by personal, discriminatory, institutional, and geographical barriers. The perspectives of Turkish and Chinese HE stakeholders were examined through 21 in-depth interviews that were subjected to content analysis and interpreted in a comparative style using the lens of Capabilities Approach of Sen. We also offer policy suggestions to increase students’ conversion capacities for better outcomes to serve both the national and the international educational contexts owing to the adaptable nature of our modal to other countries experiencing similar issues in their higher education systems.

The purpose of this paper is to develop new types of anchor bolt materials by adding corrosion-resistant elements for alloying and microstructure regulation.

Three new anchor bolt materials were designed around the 1Ni system. The stress corrosion cracking resistance of the new materials was characterized by microstructure observation, electrochemical testing and slow strain rate tensile testing.

The strength of the new anchor bolt materials has been improved, and the stress corrosion sensitivity has been reduced. The addition of Nb makes the material exhibit excellent stress corrosion resistance under –1,200 mV conditions, but the expected results were not achieved when Nb and Sb were coupled.

The new anchor bolt materials designed around 1Ni have excellent stress corrosion resistance, which is the development direction of future materials. Nb allows the material to retain its ability to extend in hydrogen-evolution environments.

The metallic alloys and their components fabricated via laser powder bed fusion (LPBF) suffer from the microvoids formed inevitably due to the extreme solidification rate during fabrication, which are impossible to be removed by heat treatment. This paper aims to remove those microvoids in as-built AlSi10Mg alloys by hot forging and enhance their mechanical properties.

AlSi10Mg samples were built using prealloyed powder with a set of optimized LPBF parameters, viz. 350 W of laser power, 1,170 mm/s of scan speed, 50 µm of layer thickness and 0.24 mm of hatch spacing. As-built samples were preheated to 430°C followed by immediate pressing with two different thickness reductions of 10% and 35%. The effect of hot forging on the microstructure was analyzed by means of X-ray diffraction, scanning electron microscopy, electron backscattered diffraction and transmission electron microscopy. Tensile tests were performed to reveal the effect of hot forging on the mechanical properties.

By using hot forging, the large number of microvoids in both as-built and post heat-treated samples were mostly healed. Moreover, the Si particles were finer in forged condition (∼150 nm) compared with those in heat-treated condition (∼300 nm). Tensile tests showed that compared with heat treatment, the hot forging process could noticeably increase tensile strength at no expense of ductility. Consequently, the toughness (integration of tensile stress and strain) of forged alloy increased by ∼86% and ∼24% compared with as-built and heat-treated alloys, respectively.

Hot forging can effectively remove the inevitable microvoids in metals fabricated via LPBF, which is beneficial to the mechanical properties. These findings are inspiring for the evolution of the LPBF technique to eliminate the microvoids and boost the mechanical properties of metals fabricated via LPBF.

While providing users with important innovation resources, an enterprise's open innovation community also encounters numerous challenges. The purpose of this study is to develop and implement a set of evaluation systems that can identify the ecological characteristics of open innovation communities. The ultimate goal is to provide guidance for the construction and establishment of these communities.

Firstly, from the perspective of information ecology, according to the connotation requirements of the four information elements of “people, information, technology and environment” in the information ecosystem, this paper determines the evaluation indicators of enterprise's open innovation community from the aspects of integrity and dynamics. Then, based on the improved analytic hierarchy process, the weight of each index is calculated, and a complete evaluation system of open innovation community is established. Finally, the fuzzy comprehensive evaluation method is used to conduct empirical research on Haier Open Partnership (HOPE) platform, analyze its ecological situation and existing problems and put forward the corresponding construction recommendations.

The application of HOPE platform shows that the evaluation index system built in this study has good applicability, and the whole research process and research method have certain reference value for future studies. At the same time, the study found that the HOPE platform has good ecological characteristics, but there are also some problems. This study can directly promote the construction of the platform.

This paper, for the first time, combines the information ecology theory with the open innovation community. The results demonstrate that the open innovation community is an information ecosystem, which can be evaluated ecologically. Furthermore, to ensure the calculation's scientific accuracy, the index weight is determined by constructing a judgment matrix based on expert questionnaires.

The purpose of this paper is to establish a dynamic analysis model of composite cylindrical roller bearings, investigate the effects of different working conditions on the kinematic characteristics of composite bearings and compare the differences between them and solid roller bearings.

This paper establishes a dynamic analysis model for composite cylindrical roller bearings and proves the correctness of the established model by establishing dynamic vibration experiments and contact theory for composite roller bearings. Comparative analysis was conducted on the effects of coupling changes in rotational speed, load, number of rollers and filling ratio on parameters such as bearing static stiffness, contact stress and vibration acceleration.

The composite roller can enhance the bearing’s operational stability and minimize contact stress, but that a higher filling ratio is going to increase the bearing’s stiffness. The acceleration degree of bearing vibration, the load on the outer raceway nodes and the bearing stability all decrease as inner ring speed rises.

A dynamic calculation model of composite cylindrical roller bearings is established, and the influence of multiparameter coupling changes on bearing vibration and contact is studied, which lays a foundation for the structural improvement of the bearings.

This study aims to investigate the relationships between affective commitment (AC), innovative work behavior (IWB) and organizational socialization strategies (training, coworker support, understanding and future prospects) to ensure the viability and prosperity of businesses in Iraq.

The methodology includes demographic analysis, confirmatory factor analysis and structural equation modeling.

An analysis of survey data based on a random sample of participating employees shows that training, understanding and future prospects all significantly and positively affect employee AC. Coworker support does not significantly affect AC. Employees’ AC to their companies significantly positively affects their IWB. Employees’ AC to their companies significantly mediates the relationships between training, understanding, future prospects and IWB. Company practices regarding training, understanding, coworker support and future prospects do not affect employees’ IWB.

The authors conducted the study in Sulaymaniyah. The results may not apply to Iraq and other nations. Researchers from various industries and countries can evaluate the model. The research ignores company age, size and fit between individuals and organizations.

The study closes a research gap in organizational behavior by exploring the association between managerial socialization strategies, AC and creative work behavior in Iraq.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

In pursuit of affordable and nutrient-rich food alternatives, the symbiotic culture of bacteria and yeast (SCOBY) emerged as a selected food ink for 3D printing. The purpose of this paper is to harness SCOBY’s potential to create cost-effective and nourishing food options using the innovative technique of 3D printing.

This work presents a comparative analysis of the printability of SCOBY with blends of wheat flour, with a focus on the optimization of process variables such as printing composition, nozzle height, nozzle diameter, printing speed, extrusion motor speed and extrusion rate. Extensive research was carried out to explore the diverse physical, mechanical and rheological properties of food ink.

Among the ratios tested, SCOBY, with SCOBY:wheat flour ratio at 1:0.33 exhibited the highest precision and layer definition when 3D printed at 50 and 60 mm/s printing speeds, 180 rpm motor speed and 0.8 mm nozzle with a 0.005 cm³/s extrusion rate, with minimum alteration in colour.

Food layered manufacturing (FLM) is a novel concept that uses a specialized printer to fabricate edible objects by layering edible materials, such as chocolate, confectionaries and pureed fruits and vegetables. FLM is a disruptive technology that enables the creation of personalized and texture-tailored foods, incorporating desired nutritional values and food quality, using a variety of ingredients and additions. This research highlights the potential of SCOBY as a viable material for 3D food printing applications.

The most common reaction to an acute physical illness is anxiety, which may be followed by depression. In patients with chronic diseases, the prevalence of anxiety disorders and depression is almost twice as high as in other diseases. This study aims to extract prominent components in the design of treatment spaces on reducing hospitalized patients’ depression from both experts and patients/users’ point of views. A final model is also presented based on the findings.

This research used an exploratory mixed method. The effective components were extracted through the administration of two Likert-scale researcher-made questionnaires in two phases. Q factor analysis was conducted to reach the components. A total of 205 patients were admitted to Namazi Hospital in Shiraz, and 20 architecture and psychology experts participated in the survey. Final modeling of the data was done through path analysis.

Six factors were found to be effective by experts in reducing depression in therapeutic spaces: nature-oriented space, targeted social space, diverse space, visual comfort, logical process and safe space. On the part of patients, seven components were deemed to be effective: visual perception, naturalism, functionalism, physical security, logical process, psychological safety and diversity. Also, four main cycles were extracted from the final model with the direct effect of diversity and the other five cycles were mediated by naturalism.

A total of 15 interviews with architects and psychologists, who were available at the time of the study, were conducted in January 2018. The only general question during interviews was “In your opinion, what factors are effective in reducing the level of depression of patients in the design of treatment spaces?” This may have limited the range of factors that could be surveyed in the study. After collecting the effective factors from the aforementioned expert’s points of view, the questionnaire of experts was designed (Appendix). The expert questionnaires were distributed and edited in two stages in January 2019 among 20 architect experts who were available at the time of the study. The one-year interval between designing and administering the questionnaires occurred because of the limitations posed by the COVID-19 pandemic situation. However, the interval did not pose methodological obstacles for the study.

Evidence-based investigation of the effectiveness of proper design components of therapeutic spaces in reducing the symptoms of patients with chronic secondary depression has received little attention in the literature. Using a “conceptual model,” the present study brought the issue into its focus so as to find effective components in the design of treatment spaces that can alleviate depression symptoms in chronically hospitalized patients.