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Assessment of the anterior chamber angle (ACA) is an essential part of the ophthalmological examination. It is intrinsically related to the diagnosis and treatment of glaucoma and has a role in its prevention. Although slit-lamp gonioscopy is considered the gold-standard technique for ACA evaluation, its poor reproducibility and the long learning curve are well-known shortcomings. Several new imaging techniques for angle evaluation have been developed in the recent years. However, whether these instruments may replace or not gonioscopy in everyday clinical practice remains unclear. This review summarizes the last findings in ACA evaluation, focusing on new instruments and their application to the clinical practice. Special attention will be given to the comparison between these new techniques and traditional slit-lamp gonioscopy. Whereas ultrasound biomicroscopy and anterior segment optical coherence tomography provide quantitative measurements of the anterior segment's structures, new gonio-photographic systems allow for a qualitative assessment of angle findings, similarly to gonioscopy. Recently developed deep learning algorithms provide an automated classification of angle images, aiding physicians in taking faster and more efficient decisions. Despite new imaging techniques made analysis of the ACA more objective and practical, the ideal method for ACA evaluation has still to be determined.

The COVID-19 pandemic has had an unprecedented impact on various aspects of our lives, including education, work dynamics, and social interactions. Dealing with the provision of building utilities in such circumstances has become a formidable challenge. During lockdowns, it becomes crucial to allocate resources strategically, giving priority to residential areas over commercial and financial districts based on population density. Identifying residential areas is of utmost importance not only for effective emergency response during natural disasters but also for ensuring fair distribution of electricity and gas when resources are scarce. However, accurately delineating residential zones is challenging due to the intricate nature of urban landscapes. This paper aims to discuss a comprehensive indicator that utilizes open-source intelligence and incorporates a multi-criteria decision-making framework to assess the likelihood of an area being residential. This indicator will greatly assist in optimizing resource allocation for power, gas, and water distribution. To demonstrate the effectiveness of the proposed approach, a case study conducted in Nicosia, Cyprus, is presented. [ABSTRACT FROM AUTHOR]

There is an increasing demand for measurement of organic carbon in solutions both in industry and in environmental research for the purpose of continuous water-quality monitoring. Practically all the methods used are based on a catalytic system in which metal oxides play a major role. The development of a new TOC/DOC measurement system, in our work, uses a mixed anatase–rutile form of TiO2 as catalyst of the mineralisation process, and direct measurement of the CO2 produced, by a gaseous diffusion electrode. The entire research is based on heterogeneous catalysis using an immobilised catalyst, which can offer considerable advantages over other methods of catalysis. Four different catalytic systems were analysed. Three involved thermal immobilisation of TiO2 on glass supports (glass spheres and glassy particulates) or on an Al metal grid. One further system consisted of direct oxidation of a Ti grid at high temperatures. The system was illuminated using a 350 nm UV source (350 mW cm-2) contained in a home-made measurement cell. Five molecules—malic acid, pentachlorophenol, sodium dodecylsulfate, hydroquinone, and citric acid—which were deemed to be representative and had been investigated in several previous studies, were used as photodegradation targets. However, it was not possible to obtain a direct expression of "catalytic efficiency" simply by reading the data obtained, nor any indirect molecular "recalcitrancy" scale. Chemometric analysis, by principal components analysis, allows the five used catalytic systems to be easily compared each other and a single PC component is able to perform classification. [ABSTRACT FROM AUTHOR]

A method, PIAMOD (Prevalence, Incidence, Analysis MODel), which allows the estimation and projection of cancer prevalence patterns by using cancer registry incidence and survival data is presented. As a first step the method involves the fit of incidence data by an age, period and cohort model to derive incidence projections. Prevalence is then estimated from modelled incidence and survival estimates. Cancer mortality is derived as a third step from modelled incidence, prevalence and survival. An application to female breast cancer is given for the Connecticut State by using data from the Connecticut Tumor Registry (CTR), 1973-1993. The age, period and cohort model fitted incidence quite well and allowed us to derive long-term projections up to 2030. Patients' survival was also projected to future years according to a scenario approach based on two extreme hypotheses: steady, that is, no more improvements after 1993 (conservative), and continuously improving at the same rate as during the observation period. Age-standardized estimated incidence shows a changing trend around the year 2005, when it starts decreasing. Age-standardized prevalence is expected to increase and change trend at a later date. Breast cancer mortality is projected as decreasing, as the combined result of no further increase in incidence and improving cancer patients' survival. An easy-to-use PIAMOD software package, on which work is in progress, will be made available to individual cancer registries and/or health planning institutions or authorities once it is developed. The use of the PIAMOD method for cancer registries will allow them to provide results of paramount importance for the whole community involved in the assessment of future disease burden scenarios in an evolving society., (Copyright 2002 John Wiley & Sons, Ltd.)