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The aim of this paper is to synthesize graphene-modified titanium dioxide (GR-TiO₂) nanorod arrays nanocomposite films, so that these can enhance the photocatalytic properties of titanium dioxide and overcome the problem of difficult separation and recovery of photocatalysts.

The GR-TiO₂ nanocomposite films were synthesized via hydrothermal method and spin-coating. The obtained samples were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM), ultraviolet–visible (UV-Vis) diffuse reflectance spectrum and Raman spectrum. The photocatalytic performance of the GR-TiO₂ nanocomposite films for degrading Rhodamin B under ultraviolet (UV) was studied by a UV-Vis spectrophotometer. The photocatalytic enhancement mechanism of graphene was studied by photoelectrochemical analysis.

The introduction of graphene expanded the range of the optical response of TiO₂ nanorod arrays, improving the separation efficiency of the photogenerated electron-hole pairs, and thus dramatically increasing its photocatalytic performance.

A simple and novel way for synthesizing GR-TiO₂ nanocomposite films has enhanced the photocatalytic performance of TiO₂.

The photocatalyst synthesized is easy to separate and recycle in the process of photocatalytic reaction, so it is possible to achieve industrialization.

The purpose of this study is to analyse the role of the clean development mechanism (CDM) established by the Kyoto Protocol in channelling foreign technology to China. Appraising the experience of CDM remains of key importance when drawing lessons for the post-2012 climate regime.

Descriptive analysis of the sources and the determinants of foreign technology transfer based on the examination of 1,355 registered projects. Econometric analysis of the probability of having a foreign supplier of technology in any project.

The prominence of German firms as technology providers and the absence of a strong relationship between technology suppliers and credit buyers. The econometric analysis finds that project size and cost, project location, credit buyers’ and consultants’ characteristics, as well as technology diffusion are all relevant factors in determining the probability of having a foreign supplier of technology.

China is a particularly interesting case for analysing technology transfer in CDM projects since, after a slow start, the country has become the largest and most dynamic CDM recipient worldwide. Furthermore, the analysis of CDM projects may offer some insights into the complex web of technological links between Chinese and foreign firms.

The transfer of emission-saving technologies to developing countries is expected to play a major role in addressing environmental problems worldwide.

This study analyses the sources and determinants of international technology transfer in CDM projects in China, and offers some insights into how the characteristics of the major players and the links between them affect this phenomenon.

The aim of the research is to achieve a robot skin which is easy to use, and can detect both position and force interacted between robot and environments.

The new type of robot skin proposed in this paper includes two functional modules – contact position sensor and contact force sensor. The contact position sensor module is based on the resistor divider principle, which consists of two perpendicular conductive fiber layers and insulated dot spacer between them. The contact force sensor module is based on capacitance change theory, which consists of two soft conductive plates and a viscoelastic layer between them. By combining the two modules, the soft robot skin was designed.

Simulation and experiment results demonstrate that the proposed robot skin design is feasible and effective enough to sense contact position and contact force simultaneously.

This robot skin is low-cost and easy to make and use, which provides safety solutions for most of the robot.

For the first time, an integrated robot skin which can get contact position and force information simultaneously is designed. Unlike general tactile sensor matrices, this robot skin has only six leads. Furthermore, the number of leads does not increase with the enlarging of sensor area. Soft and simple structure of the robot skin makes it possible to cover any region of the robot body.

During the operation of nickel-based alloys as blades and discs in turbines, the sliding activity between metallic surfaces is subjected to structural and compositional changes. In as much as friction and wear are influenced by interacting surfaces, it is necessary to investigate these effects. This study aims to understand better the mechanical and tribological characteristics of Ni-17Cr-10X (X = Mo, W, Ta) ternary alloy systems developed via spark plasma sintering (SPS) technique.

Nickel-based ternary alloys were fabricated via SPS technique at 50 MPa, 1100 °C, 100 °C/min and a dwell time of 10 mins. Scanning electron microscopy, X-Ray diffraction, energy dispersive X-ray spectroscopy, nanoindentation techniques and tribometer were used to assess the microstructure, phase composition, elemental dispersion, mechanical and tribological characteristics of the sintered nickel-based alloys.

The outcome of the investigation showed that the Ni-17Cr10Mo alloy exhibited the highest indentation hardness value of 8045 MPa, elastic modulus value of 386 GPa and wear resistance. At the same time, Ni-17Cr10W possessed the least mechanical and wear properties.

It can be shown that the SPS technique is efficient in the development of nickel-based alloys with good elemental distribution and without defects such as segregation of alloying elements, non-metallic inclusions. This is evident from the scanning electron microscopy micrographs.

This paper aims to prepare an oil-impregnated porous polytetrafluoroethylene (PTFE) composite with advanced tribological properties using citric acid as a novel pore-forming agent.

Citric acid (CA) was used to form pores in PTFE, and then oil-impregnated PTFE composites were prepared. The pore-forming efficiency of CA was evaluated. The possible mechanism of lubrication was proposed according to the tribological properties.

The results show CA is an efficient pore-forming agent and completely removed, and the porosity of the PTFE increases with the increase of the CA content. The oil-impregnated porous PTFE exhibits an excellent tribological performance, an increased wear resistance of 77.29% was realized in comparison with neat PTFE.

This study enhances understanding of the lubrication mechanism of oil-impregnated porous polymers and guides for their tribological applications.

The purpose of this paper is to propose a new method based on nonlinear least squares (NLS) for solving the parameters of nonlinear grey Bernoulli model (NGBM(1,1)) and to verify the proposed model using the case of employee demand prediction of high-tech enterprises in China.

First of all, minimising the square sum of fitting error of grey differential equation of NGBM(1,1) is taken as the optimisation target and the parameters of classic grey model (GM(1,1)) are set as the initial value of parameter vector. Afterwards, the structural parameters and power exponents are solved by using the Gauss-Newton iteration algorithm so as to calculate the parameters of NGBM(1,1) under given rules for ceasing the algorithm. Finally, by taking the employee demand of high-tech enterprises in the state-level high-tech industrial development zone in China as examples, the validity of the new method is verified.

The results show that the parameter estimation algorithm based on the NLS method can effectively identify the power exponents of NGBM(1,1) and therefore can favourably adapt to the nonlinear fluctuations of sequences. In addition, the algorithm is superior to the GM(1,1) model, grey Verhulst model, and Quadratic-Exponential smoothing algorithm in terms of the simulation and prediction accuracy.

Under the framework of solving parameters based on NLS, various aspects of NGBM(1,1) remain to be further investigated including background value, initial condition and variable structural modelling methods.

The parameter estimation algorithm based on NLS can effectively identify the power exponent of NGBM(1,1) and therefore it can favourably adapt to the nonlinear fluctuation of sequences.

According to the basic principle of NLS, a new method for solving the parameters of NGBM(1,1) is proposed by using the Gauss-Newton iteration algorithm. Moreover, by conducting the modelling case about employees demand in high-tech enterprises in China, the effectiveness and superiority of the new method are verified.

This paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.

The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.

The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.

This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.

India’s rapid economic growth has triggered a significant transformation in its logistics sector, fueled by comprehensive reforms and digital initiatives outlined in the National Logistics Policy. Smart warehouses, equipped with cutting-edge technologies such as IoT, AI and automation, have taken center stage in this evolution. They play a pivotal role in India’s digital journey, revolutionizing supply chains, reducing costs and boosting productivity. This AI-driven transformation, in alignment with the “Digital India” campaign, positions India as a global logistics leader poised for success in the industry 4.0 era. In this context, this study highlights the significance of smart warehouses and their enablers in the broader context of supply chain and logistics.

This paper utilized the ISM technique to suggest a multi-tiered model for smart warehouse ecosystem enablers in India. Enablers are also graphically categorized by their influence and dependence via MICMAC analysis.

The study not only identifies the 17 key enablers fostering a viable ecosystem for smart warehouses in India but also categorizes them as linkage, autonomous, dependent and independent enablers.

This research provides valuable insights for practitioners aiming to enhance technological infrastructure, reduce costs, minimize wastage and enhance productivity. Moreover, it addresses critical academic and research gaps contributing to the advancement of knowledge in this domain, thus paving the way forward for more research and learning in the field of smart warehouses.

The qualitative modeling is done by collecting experts' opinions using the ISM technique solicits substantial value to this research.

To meet customers' expectations on shorter lead times, high product availability, flexibility, and variation in delivery and return options, retailers have turned their attention to warehousing and are making big investments in technology. Currently, technology providers are pushing for smart warehousing, a new and under-researched phenomenon. This study aims to conceptualize the term and examine pathways toward implementing smart warehousing.

An exploratory survey was administered to 50 leading Swedish retailers in varying segments. A two-tailed t-test for equality of means was used to detect significant differences between current and future states.

The study found that future smart warehouses will be automated, autonomous, digital, and connected, but that retailers will follow different paths along this journey, driven by contextual trends, e.g. sales growth, wider product assortment, shorter lead-time offerings, and integration of brick-and-mortar and online stores. Interestingly, the study revealed that many of the retailers that aim to create smart warehouses in five years are not the retailers with the most developed technology today.

The paper operationalizes smart warehousing in two dimensions: degree of automation and degree of digitalization and connectivity of information platforms. Based on the findings, 16 theoretical propositions are put forth that, based on contextual factors, explain different pathways for retailers to implement smart warehousing.

The empirical insights and theoretical discussions provide practically useful guidance, including outlined trends, for selecting and benchmarking automation and complementary technologies in warehouse operations.

This paper conceptualizes and operationalizes smart warehousing – an original approach. It is also one of the first to investigate the technological transformation in retail warehousing empirically, explaining how and why retailers choose different pathways toward smart warehousing.

This study aims to explore whether, how and when leaders' artificial intelligence (AI) symbolization (i.e. the demonstration of leaders' acceptance of and support for AI by engaging in AI-related behaviors and/or displaying objects that reflect their affinity for AI) affects employee job crafting behaviors.

The authors conducted two studies (i.e. an experiment and a multi-wave field survey) with samples from different contexts (i.e. United States and China) to test our theoretical model. The authors used ordinary least squares (OLS) and hierarchical linear modeling (HLM) to test the hypotheses.

Leaders' AI symbolization is positively related to employee change readiness and, in turn, promotes employee job crafting. Moreover, employee-attributed impression management motives moderate the positive indirect effect of leaders' AI symbolization on employee job crafting via change readiness, such that this indirect effect is stronger when employee-attributed impression management motives are low (vs high).

Leaders should engage in AI symbolization to promote employee job crafting and avoid behaviors that may lead employees to attribute their AI symbolization to impression management.

By introducing the concept of leaders' AI symbolization, this study breaks new ground by illustrating how leaders' AI symbolization positively influences employees' change readiness, as well as job crafting in the workplace. Further, integrating AI as a novel and timely context for evaluating job crafting contributes to the literature where empirical research is relatively scant, particularly regarding the factors that prompt employees to engage in job crafting.