A fiber ring laser sensor setup utilizing FBGs (Fiber Bragg Gratings) for simultaneous measurement of ambient temperature and relative humidity (RH) is presented. Two FBGs are incorporated as tunable filters for a dual-wavelength laser emission, where one FBG was coated with Polyimide (PI) in order to achieve sensitivity to RH changes, while the other bare FBG was used for temperature sensing. An increase in RH would induce a strain on the grating, which results in a variation in the resonance wavelength of the PI-coated FBG. This causes a shift in the laser emission wavelength. Being insensitive to RH changes, the bare FBG was employed to measure temperature. The dual-wavelength fiber ring laser sensor created thus allows to determine simultaneous measurement of RH and temperature. The RH sensitivities observed by the PI coated FBG to RH and temperature are 3.6 pm/%RH and 12.15 pm/°C respectively. The temperature sensitivity of the bare FBG was observed to be 9.6 pm/°C. The main advantage of the proposed setup is an optical signal to noise ratio (OSNR) higher than 55 dB and a 3 dB-bandwidth less than 0.02 nm, which points out efficient capabilities for both precise sensing and remote detection applications.

This paper presents a linear dependence of temperature sensitivity in Rayleigh scattering-based optical frequency domain reflectometry (OFDR) on the concentration of germanium in the fiber core. Eight different commercially available single-mode fibers have been investigated and compared with the standard SMF-28 Corning fiber. We have experimentally analyzed the influence of germanium concentration in the fiber core, the protective coating, the degree of reduction of the fiber cladding's diameter, and the amount of humidity on Rayleigh backscatter in response to temperatures ranging from 10-90°C. Maximum sensitivity was 1.343 GHz/°C for 20.78 mol % of the germanium-doped bare fiber and it does not depend on changes in humidity. The experimental results were compared and qualitatively agreed with the numerical simulations.

Liu Kang (1911–2004) was renowned Singapore artist trained in Shanghai and Paris, and known for his contributions to the Nanyang style—an art movement practised by migrant Chinese painters in Singapore between the late 1940s to the 1960s. The style depicts aspects of the tropical way of life, synthetising the artistic traditions of the School of Paris and Chinese ink painting with remarkable stylistic innovations. The aim of this study was to characterise Liu Kang’s painting materials and technique by way of ten paintings from a significant period in his oeuvre, 1950–1958, during which his Nanyang style emerged. The selected artworks are from the National Gallery Singapore. A broad range of analytical techniques was employed to study the painting supports and paint layers. The results indicate the prevailing use of commercially prepared linen canvases with double-layered oil-based ground. Single- and triple-layered structures of the ground, as well as semi-absorbent ground, were used sporadically. The identified group of pigments partially overlaps with those already known from Liu Kang’s earlier practice and also incorporates some noteworthy peculiarities like manganese, cerulean and phthalocyanine blues, phthalocyanine green, zinc yellow, and naphthol red AS-D. Some of these newly identified pigments made a distinctive appearance in the individual artworks, but ultimately Liu Kang was not convinced about increasing their role in his painting practice of the 1950s as presented in this research. This study highlights the significance of drawing and photography as integral elements of his artistic process. It also delves into the artist’s different painting approaches and discusses their evolution, which culminated in the stylistic innovation that became Liu Kang’s signature for decades to come. The obtained data may assist art historians and conservators in authenticity and attribution studies, evaluating the condition of artworks and designing conservation strategies. Moreover, this study contributes to the growing body of knowledge about twentieth-century artists’ materials, which are characterised by the complex mixtures of inorganic and organic compounds. It also provides information about the availability of art materials in Singapore in the 1950s.

This paper presents, for the first time to the best of our knowledge, simultaneous temperature and relative humidity (RH) measurement using a machine learning (ML) model in Rayleigh-based Optical Frequency Domain Reflectometry (OFDR). The sensor unit consists of two segments: bare and polyimide-coated fibers, each with different sensitivities to temperature. The polyimide-coated fiber is RH-sensitive, unlike the bare fiber. We propose the ML approach to avoid manual post-processing data and maintain relatively high accuracy of the sensor. The root mean square error (RMSE) values for the 3 cm length of the sensor unit were 0.36 °C and 1.73% RH for temperature and RH, respectively. Furthermore, we investigated the impact of sensor unit lengths and number of data points on RMSE values. This approach eliminates the need for manual data processing, reduces analysis time, and enables accurate, simultaneous measurement of temperature and RH in Rayleigh-based OFDR.

The comprehensive technical investigation of female nude paintings by the Singapore pioneer artist Liu Kang (1911–2004) provided the evidence for a discussion of the evolution of his palette of colours and his working process for expression in this genre, particularly the execution of female bodies. As the artist’s free expression in classical nude paintings was limited by the censorship imposed by the Singapore government, the investigated artworks span two periods, 1927–1954 (early career) and 1992–1999 (the “golden years”, during which censorship policies were relaxed). Hence, eight paintings from the Liu family and National Gallery Singapore were selected for non- and micro-invasive analyses of the paint layers. The obtained results were supplemented with archival sources to elucidate certain aspects of Liu Kang’s working practice. The investigation revealed the importance of drawing and sketching studies in the development of artistic ideas. The analytical techniques, such as polarised light microscopy (PLM), field emission scanning electron microscope with energy dispersive spectroscopy (FE-SEM-EDS) and attenuated total reflectance–Fourier transform infrared spectroscopy (ATR-FTIR), enabled us to observe a transition from the yellow iron-based tonal ranges of skin colours to complex pigment mixtures composed of additions of cobalt blue, ultramarine, Prussian blue, Cr-containing yellow(s) and green(s), cadmium yellow, orange and/or red and organic reds, revealing the artist’s more liberal use of colours and his experimentation with their contrasting and complementary juxtaposes. In terms of painting technique, the artist’s comparatively laborious paint application using small brushes quickly gave way to a more effortless manipulation of the paint using bigger brushes and the incorporation of palette knives. Moreover, visible light (VIS), near-infrared (NIR) and X-ray radiography (XRR) imaging techniques led to the discovery of a hidden composition in one investigated artwork, which bears resemblance to the nude painting known only from an archival photograph. Additionally, for the first time, the archival search provided photographic evidence that Liu Kang used oil paint tubes from Royal Talens and Rowney in the 1990s. Overall, this in-depth investigation contributes to the understanding of Liu Kang’s approach to the female nude painting and may assist conservators and art historians in studies of twentieth-century commercial paints.

Accurate pigment classification is essential for the analysis and conservation of historical paintings. This study presents a non-invasive approach based on supervised machine learning for classifying pigments using image data acquired under three distinct spectral illumination conditions: visible-light reflectography (VIS), ultraviolet false-color imaging (UVFC), and infrared false-color imaging (IRFC). A dataset was constructed by extracting 32 × 32 pixel patches from pigment samples, resulting in 200 classes representing 40 unique pigments with five preparation variants each. A total of 600 initial raw images were acquired, from which 4000 image patches were extracted for feature engineering. Feature vectors were obtained from visible reflectography, ultraviolet false-color imaging (UVFC), and infrared false-color imaging (IRFC) using statistical descriptors derived from RGB channels. This study demonstrates that accurate pigment classification can be achieved with a minimal set of three illumination types, offering a practical and cost-effective alternative to hyperspectral imaging in real-world conservation practice. Among the evaluated classifiers, the random forest model achieved the highest accuracy (99.30 $\pm 0.15$%). The trained model was subsequently validated on annotated regions of historical paintings, demonstrating its robustness and applicability. The proposed framework offers a lightweight, interpretable, and scalable solution for non-invasive pigment analysis in cultural heritage research that can be implemented with accessible imaging hardware and minimal post-processing.