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Background: Dry eye disease (DED) is a common, yet largely underdiagnosed, disorder of the eye encountered in healthcare facilities. It disrupts the tear film, causing ocular discomfort symptoms such as itching, tearing and irritation. Aim: This study aimed to determine the clinical profile of DED patients at a tertiary eye hospital to inform a public sector intervention strategy. Setting: The study was conducted at McCord Provincial Eye Hospital (MPEH). Methods: A quantitative, descriptive study design was undertaken and included 602 patients. The standardised patient evaluation of eye dryness (SPEED) questionnaire was administered, after which the tear break-up time (TBUT), Schirmer 2, blink rate, tear meniscus height (TMH) and meibography clinical tests were done. Data were managed using Statistical Package for Social Sciences (SPSS) version 28.0. Results: The prevalence of DED was 83.2%, with the majority being in the age group 41 years – 55 years (mean = 48.54 ± 18.76) and female (84.1%). The disease increased significantly with age (p = 0.02) and the prevalence of aqueous deficient, evaporative and mixed DED was 3.2%, 62.7% and 34.1%, respectively. Irrespective of the associated risk factors, the majority had either moderate (45.3%) or severe (30.9%) DED, with those with a history of glaucoma, hypertension and post-cataract surgery mostly having severe DED (p < 0.001). Conclusion: Noting the chronic discomfort and other complications of DED, the high prevalence found among public sector hospital patients warrants a strategic intervention. Interventions could include a DED management protocol with clinical guidelines for interventions from primary to tertiary levels of care. Further, as most older patients presented with systemic diseases such as diabetes and hypertension, DED clinical guidelines should be extended to multidisciplinary teams managing systemic diseases. Contribution: In addition to contributing to the scholarship of DED, the study provides empirical data to assist the provincial hospital and its eleven catchment district facilities in developing a comprehensive DED management strategy. Although conducted in the eThekwini District, the guideline has generic features which may be applied to health districts throughout the province of KwaZulu-Natal.

Dry eye disease (DED) has become common on a global scale in recent years. There is a wide prevalence of DED in different countries based on various ethnicities and environment. DED is a multifactorial ocular disorder. In addition to advanced age and gender, such factors as living at high altitude, smoking, pterygium, prolonged use of consumer electronics or overingesting of caffeine or multivitamins are considered to be the major risk factors of DED. We report the DED epidemiology in Taiwan firstly in this article. According to the pathophysiological factors and changes inthe composition of the tear film in DED, it can be categorized into several subtypes, including lipid anomaly dry eye, aqueous tear deficiency, allergic and toxic dry eye among others. Each subtype has its own cause and disease management; therefore, it is important for ophthalmologists to identify the type through literature review and investigation. The management of DED, relies not only on traditional medications such as artificial tears, gels and ointments, but also newer treatment options such as acupuncture, SYL1001, and nanomedicine therapy. We also conducted a comprehensive literature review including common subtypes and treatment of DED. Clearly, more clinical trials are needed to assess the efficacy and safety of the various treatments and common subtypes of DED.

The prevalence of myopia and dry eye disease (DED) has increased significantly in schoolchildren over the past few decades, particularly in East Asia, leading to intensive physical and emotional burdens on schoolchildren’s growth. Current studies reported associations between myopia and DED, where myopic individuals tend to have a higher prevalence of DED compared to non-myopic individuals among adults and children. The current review aims to summarize existing literature to investigate the relationship between DED and refractive error in schoolchildren. Possible mechanisms have been discussed, including mechanical and behavioral factors (ocular surface exposure, High-Order Aberrations, ethnicity, behavioral habits), parasympathetic dysregulation and neurovascular coupling.

Additive manufacturing (AM) is one of the pillars of Industry 4.0, where automation to create smart factories is the main target. The hybridization of processes is one of the leading strategies to implement a more flexible, efficient, and interconnected manufacturing environment. Nowadays, different researches are focused on the hybridization of metal AM and subtractive manufacturing (SM). Based on the working principles of AM and SM, it can be established that they are complementary processes. Hence, a synergy between them allows conceiving a unique process. As a result, the advantages are magnified, and the limitations of each one are minimized or eliminated. This review presents the latest developments, challenges, limitations, and future perspectives for the integration between directed energy deposition (DED) and SM. DED is a versatile AM process for metal parts fabrication, where the geometrical complexity is its main advantage. Nevertheless, the low surface quality and the difficult dimensional control of the parts create the need for post-processing. Traditional post-processing involves a higher production time, and the barriers cannot be completely overcome. Then, a hybrid process constitutes a powerful concept to combine both technologies efficiently, to produce complex parts with less waste of material and energy.

Developing novel alloys for 3D printing of metals is a time‐ and resource‐intensive challenge. High‐throughput 3D printing and material characterization protocols are used in this work to rapidly screen a wide range of chemical compositions and processing conditions. In situ, alloying of high‐strength steel with pure Al in the targeted range of 0–10 wt.% and flexible adjustment of the volumetric energy input is performed to derive 20 individual alloy combinations. These conditions are characterized using large‐area crystallographic analysis combined with chemistry and nanoindentation protocols. The significant influence of Al content and processing conditions on the constitutive material behavior of the metastable base alloy allowed for efficient exploration of the underlying process‐structure‐properties (PSP) relationships. The extracted PSP relations are discussed based on the dominant physical mechanisms observed in the samples. Furthermore, the microstructure‐property relationship based on limited experimental data is supported by an explainable machine‐learning approach. This work shows combining high‐throughput experiments of metal 3D printing with data science‐driven prediction leads to faster alloy development. Researchers develop steel alloy specimens specifically for 3D printing by adjusting aluminum content (0–10 wt.%) and energy input during printing. Through crystallographic, chemical, and mechanical analysis, key relationships between processing conditions, microstructure, and properties, enabling machine learning models are identified to predict alloy behavior efficiently.

Additive manufacturing (AM) technologies have gained considerable attention in recent years as an innovative method to produce high entropy alloy (HEA) components. The unique and excellent mechanical and environmental properties of HEAs can be used in various demanding applications, such as the aerospace and automotive industries. This review paper aims to inspect the status and prospects of research and development related to the production of HEAs by AM technologies. Several AM processes can be used to fabricate HEA components, mainly powder bed fusion (PBF), direct energy deposition (DED), material extrusion (ME), and binder jetting (BJ). PBF technologies, such as selective laser melting (SLM) and electron beam melting (EBM), have been widely used to produce HEA components with good dimensional accuracy and surface finish. DED techniques, such as blown powder deposition (BPD) and wire arc AM (WAAM), that have high deposition rates can be used to produce large, custom-made parts with relatively reduced surface finish quality. BJ and ME techniques can be used to produce green bodies that require subsequent sintering to obtain adequate density. The use of AM to produce HEA components provides the ability to make complex shapes and create composite materials with reinforced particles. However, the microstructure and mechanical properties of AM-produced HEAs can be significantly affected by the processing parameters and post-processing heat treatment, but overall, AM technology appears to be a promising approach for producing advanced HEA components with unique properties. This paper reviews the various technologies and associated aspects of AM for HEAs. The concluding remarks highlight the critical effect of the printing parameters in relation to the complex synthesis mechanism of HEA elements that is required to obtain adequate properties. In addition, the importance of using feedstock material in the form of mix elemental powder or wires rather than pre-alloyed substance is also emphasized in order that HEA components can be produced by AM processes at an affordable cost.

This study explores the potential of Artificial Intelligence (AI) in early screening and prognosis of Dry Eye Disease (DED), aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners. Despite the promising opportunities, challenges such as diverse diagnostic evidence, complex etiology, and interdisciplinary knowledge integration impede the interpretability, reliability, and applicability of AI-based DED detection methods. The research conducts a comprehensive review of datasets, diagnostic evidence, and standards, as well as advanced algorithms in AI-based DED detection over the past five years. The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques: (1) those with ground truth and/or comparable standards, (2) potential AI-based methods with significant advantages, and (3) supplementary methods for AI-based DED detection. The study proposes suggested DED detection standards, the combination of multiple diagnostic evidence, and future research directions to guide further investigations. Ultimately, the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations, advanced methods, challenges, and potential future perspectives, emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.

Defects are a leading issue for the rejection of parts manufactured through the Directed Energy Deposition (DED) Additive Manufacturing (AM) process. In an attempt to illuminate and advance in situ quality monitoring and control of workpieces, we present an innovative data-driven method that synchronously collects sensing data and AM process parameters with a low sampling rate during the DED process. The proposed data-driven technique determines the important influences that individual printing parameters and sensing features have on prediction at the inter-layer qualification to perform feature selection. Three Machine Learning (ML) algorithms including Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) are used. During post-production, a threshold is applied to detect low-density occurrences such as porosity sizes and quantities from CT scans that render individual layers acceptable or unacceptable. This information is fed to the ML models for training. Training/testing are completed offline on samples deemed “high-quality” and “low-quality”, utilizing only features recorded from the build process. CNN results show that the classification of acceptable/unacceptable layers can reach between 90% accuracy while training/testing on a “high-quality” sample and dip to 65% accuracy when trained/tested on “low-quality”/“high-quality” (respectively), indicating over-fitting but showing CNN as a promising inter-layer classifier.

Laser-directed energy deposition (DED) using wire or powder feedstock is a promising way to fabricate prototypes in rapid time, including complex metal parts for advanced engineering applications. In this work, AISI 316L stainless steel—a well-known, weldable alloy model—was used to perform a foundational comparative study of wire-fed (LW-DED) and powder-fed (LP-DED) processes, establishing a baseline before progressing to high-temperature alloys. Hollow cylindrical specimens were fabricated and characterized microstructurally and mechanically. LP-DED produced a refined cellular–dendritic structure with primary dendrite arm spacing of 3.29 ± 0.49 µm and slightly higher average hardness (226 ± 8 HV0.2), accompanied by fine, spherical porosity inherent to the powder feedstock. LW-DED generated coarser epitaxial columnar dendrites (5.15 ± 0.69 µm) and slightly lower hardness (206 ± 10 HV0.2) but achieved nearly full density and high material catching efficiency. The results indicate that both methods yield comparable deposits when parameters are controlled, with LP-DED offering enhanced microstructural refinement and LW-DED providing faster deposition and higher build volume. These findings provide practical guidance for the additive manufacturing of high-performance parts and establish a baseline for the application of DED processes to advanced alloys.

This paper provides a comprehensive analysis of recent advancements in additive manufacturing, a transformative approach to industrial production that allows for the layer-by-layer construction of complex parts directly from digital models. Focusing specifically on Directed Energy Deposition, it begins by clarifying the fundamental principles of metal additive manufacturing as defined by International Organization of Standardization and American Society for Testing and Materials standards, with an emphasis on laser- and powder-based methods that are pivotal to Directed Energy Deposition. It explores the critical process mechanisms that can lead to defect formation in the manufactured parts, offering in-depth insights into the factors that influence these outcomes. Additionally, the unique mechanisms of defect formation inherent to Directed Energy Deposition are examined in detail. The review also covers the current landscape of process evaluation and non-destructive testing methods essential for quality assurance, including both traditional and contemporary in situ monitoring techniques, with a particular focus given to advanced machine-vision-based methods for geometric analysis. Furthermore, the integration of process monitoring, multiphysics simulation models, and data analytics is discussed, charting a forward-looking roadmap for the development of Digital Twins in Laser–Powder-based Directed Energy Deposition. Finally, this review highlights critical research gaps and proposes directions for future research to enhance the accuracy and efficiency of Directed Energy Deposition systems.