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The purpose of this paper is to review the state of the art of laser powder deposition (LPD), a solid freeform fabrication technique capable of fabricating fully dense functional items from a wide range of common engineering materials, such as aluminum alloys, steels, titanium alloys, nickel superalloys and refractory materials.

The main R&D efforts and the major issues related to LPD are revisited.

During recent years, a worldwide series of R&D efforts have been undertaken to develop and explore the capabilities of LPD and to tap into the possible cost and time savings and many potential applications that this technology offers.

These R&D efforts have produced a wealth of knowledge, the main points of which are highlighted herein.

The purpose of this paper is to purify the wastewater in the garment industry.

The preparation of the calcium alginate (CA)/activated carbon (AC) composite membrane was achieved by vacuum freeze-drying and the cross-linking reaction between sodium alginate and CaCl₂. Effective parameters in the methylene blue (MB) adsorption such as temperature, dose, contact time and pH were discussed. The adsorption properties of the composite membrane were investigated by isotherm, kinetics and thermodynamic analysis. The adsorption equilibrium data were described by the adsorption isotherm Langmuir model and the Freundlich model. The pseudo-first-order, pseudo-second-order and intra-particle diffusion equations were selected to evaluate the kinetics. The thermodynamic study described that the adsorption reaction was spontaneous and exothermic.

The AC/CA membrane is an efficient and powerful adsorbent to remove MB in printing and dyeing wastewater, and provides a new idea for the selection of adsorption materials for industrial printing and dyeing wastewater.

The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials.

The paper contributes to its wider and convenientapplication in wastewater treatment.

Studies on the combination of CA and AC into adsorption membranes and for the removal of dyes from printing and dyeing wastewater have not been reported. A novel composite material is provided for treatment dyeing wastewater in garment production. The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials and contribute to its wider and convenient application in wastewater treatment.

This work provides an overview of academic articles on the application of artificial intelligence (AI) in healthcare. It delves into the innovation process, encompassing a two-stage trajectory of exploration and development followed by dissemination and adoption. To illuminate the transition from the first to the second stage, we use prospect theory (PT) to offer insights into the effects of risk and uncertainty on individual decision-making, which potentially lead to partially irrational choices. The primary objective is to discern whether clinical decision support systems (CDSSs) can serve as effective means of “cognitive debiasing”, thus countering the perceived risks.

This study presents a comprehensive systematic literature review (SLR) of the adoption of clinical decision support systems (CDSSs) in healthcare. We selected English articles dated 2013–2023 from Scopus, Web of Science and PubMed, found using keywords such as “Artificial Intelligence,” “Healthcare” and “CDSS.” A bibliometric analysis was conducted to evaluate literature productivity and its impact on this topic.

Of 322 articles, 113 met the eligibility criteria. These pointed to a widespread reluctance among physicians to adopt AI systems, primarily due to trust-related issues. Although our systematic literature review underscores the positive effects of AI in healthcare, it barely addresses the associated risks.

This study has certain limitations, including potential concerns regarding generalizability, biases in the literature review and reliance on theoretical frameworks that lack empirical evidence.

The uniqueness of this study lies in its examination of healthcare professionals’ perceptions of the risks associated with implementing AI systems. Moreover, it addresses liability issues involving a range of stakeholders, including algorithm developers, Internet of Things (IoT) manufacturers, communication systems and cybersecurity providers.