Abstract

Abstract
Optical microbubble resonators (OMBRs)-understood as localized thin wall bulges induced in silica microcapillaries-are gaining an ever-growing interest in microfluidic sensing applications due to their capability to sustain whispering gallery modes (WGMs) and confine the fluidic sample within their own hollow-core microcavity. Currently, most applications use an external tapered optical fiber for coupling light to the resonator. This arrangement is known to be fragile and prone to vibrations. In this work, an alternative approach, based on coupling OMBR with a femtosecond (fs) laser-written optical waveguides, integrated at the surface of fused silica substrate, is proposed. In this configuration, a stable and robust final structure is accomplished by gluing the two ends of the microcapillary, on which the OMBR is made, to the substrate. The OMBR quality factors, measured at the excitation wavelength of 1540 nm, show values close to 10(4) in the case of a water-filled cavity, with a maximum coupling efficiency of up to 6.5%. Finally, the operation of the integrated optical devices as refractometers is demonstrated by delivering different solutions with successively increasing concentrations of NaCl inside the OMBR. An average sensitivity of 45 nm/RIU is obtained, yielding a resolution of 4.4x10(-5) RIU, creating the potential for this platform to be applied in chemical/biochemical sensing.

Abstract
This article presents a methodology to estimate the evolution of QoS indices, based on investments and maintenance costs carried out in the DN. The indices were estimated at various disaggregated levels, including the global index, 3 different QoS zones (urban, semi-urban and rural) and 278 municipalities, thereby facilitating the mitigation of QoS asymmetries by allocating investments and maintenance actions to specific regions. To achieve this objective, an optimization problem was formulated to allocate investments and maintenance costs to municipalities with higher improvement benefit-cost ratios, potentially exhibiting lower levels of QoS. This methodology was adopted by the Portuguese DSO to establish the future investments plan from 2023 to 2027. The results demonstrate estimations of good performance, considering the stochastic nature of the phenomena affecting QoS (e.g. atmospheric conditions), which are included in this study, thus developing confidence levels for the global indices.

Abstract
Given the high prevalence of lung cancer, an accurate diagnosis is crucial. In the diagnosis process, radiologists play an important role by examining numerous radiology exams to identify different types of nodules. To aid the clinicians' analytical efforts, computer-aided diagnosis can streamline the process of identifying pulmonary nodules. For this purpose, medical reports can serve as valuable sources for automatically retrieving image annotations. Our study focused on converting medical reports into nodule annotations, matching textual information with manually annotated data from the Lung Nodule Database (LNDb)-a comprehensive repository of lung scans and nodule annotations. As a result of this study, we have released a tabular data file containing information from 292 medical reports in the LNDb, along with files detailing nodule characteristics and corresponding matches to the manually annotated data. The objective is to enable further research studies in lung cancer by bridging the gap between existing reports and additional manual annotations that may be collected, thereby fostering discussions about the advantages and disadvantages between these two data types.

Abstract
Hardware specialization is seen as a promising venue for improving computing efficiency, with reconfigurable devices as excellent deployment platforms for application-specific architectures. One approach to hardware specialization is via the popular RISC-V, where Instruction Set Architecture (ISA) extensions for domains such as Edge Artifical Intelligence (AI) are already appearing. However, to use the custom instructions while maintaining a high (e.g., C/C++) abstraction level, the assembler and compiler must be modified. Alternatively, inline assembly can be manually introduced by a software developer with expert knowledge of the hardware modifications in the RISC-V core. In this paper, we consider a RISC-V core with a vectorization and streaming engine to support the Unlimited Vector Extension (UVE), and propose an approach to automatically transform annotated C loops into UVE compatible code, via automatic insertion of inline assembly. We rely on a source-to-source transformation tool, Clava, to perform sophisticated code analysis and transformations via scripts. We use pragmas to identify code sections amenable for vectorization and/or streaming, and use Clava to automatically insert inline UVE instructions, avoiding extensive modifications of existing compiler projects. We produce UVE binaries which are functionally correct, when compared to handwritten versions with inline assembly, and achieve equal and sometimes improved number of executed instructions, for a set of six benchmarks from the Polybench suite. These initial results are evidence towards that this kind of translation is feasible, and we consider that it is possible in future work to target more complex transformations or other ISA extensions, accelerating the adoption of hardware/software co-design flows for generic application cases.

Abstract
Spectral point-of-care technology is reagentless with minimal sampling (<10 mu L) and can be performed in real-time. White blood cells are non-dominant in blood and in spectral information, suffering significant interferences from dominant constituents such as red blood cells, hemoglobin and billirubin. White blood cells of a bigger size can account for 0.5% to 22.5% of blood spectra information. Knowledge expansion was performed using data augmentation through the hybridization of 94 real-world blood samples into 300 synthetic data samples. Synthetic data samples are representative of real-world data, expanding the detailed spectral information through sample hybridization, allowing us to unscramble the spectral white blood cell information from spectra, with correlations of 0.7975 to 0.8397 and a mean absolute error of 32.25% to 34.13%; furthermore, we achieved a diagnostic efficiency between 83% and 100% inside the reference interval (5.5 to 19.5 x 10(9) cell/L), and 85.11% for cases with extreme high white blood cell counts. At the covariance mode level, white blood cells are quantified using orthogonal information on red blood cells, maximizing sensitivity and specificity towards white blood cells, and avoiding the use of non-specific natural correlations present in the dataset; thus, the specifity of white blood cells spectral information is increased. The presented research is a step towards high-specificity, reagentless, miniaturized spectral point-of-care hematology technology for Veterinary Medicine.