Failure modes and downtime of radiotherapy LINACs and multileaf collimators in Indonesia.

BACKGROUND AND PURPOSE: The lack of equitable access to radiotherapy (RA) linear accelerators (LINACs) is a substantial barrier to cancer care in low- and middle-income countries (LMICs). These nations are expected to bear up to 75% of cancer-related deaths globally by 2030. State-of-the-art LINACs in LMICs experience major issues in terms of robustness, with mechanical and electrical breakdowns resulting in downtimes ranging from days to months. While existing research has identified the higher failure frequency and downtimes between LMICs (Nigeria, Botswana) compared to high-income countries (HICs, the UK), there has been a need for additional data and study particularly relating to multileaf collimators (MLCs). MATERIALS AND METHODS: This study presents for the first time the analysis of data gathered through a dedicated survey and workshop including participants from 14 Indonesian hospitals, representing a total of 19 LINACs. We show the pathways to failure of radiotherapy LINACs and frequency of breakdowns with a focus on the MLC subsystem. RESULTS: This dataset shows that LINACs throughout Indonesia are out of operation for seven times longer than HICs, and the mean time between failures of a LINAC in Indonesia is 341.58 h or about 14 days. Furthermore, of the LINACs with an MLC fitted, 59 . 02 - 1.61 + 1.98 $59.02_{ - 1.61}^{ + 1.98}$ % of all mechanical faults are due to the MLC, and 57 . 14 - 1.27 + 0.78 $57.14_{ - 1.27}^{ + 0.78}$ % of cases requiring a replacement component are related to the MLC. CONCLUSION: These results highlight the pressing need to improve robustness of RT technology for use in LMICs, highlighting the MLC as a particularly problematic component. This work motivates a reassessment of the current generation of RT LINACs and demonstrates the need for dedicated efforts toward a future where cancer treatment technology is robust for use in all environments where it is needed.

DQC: A Python program package for differentiable quantum chemistry.

Automatic differentiation represents a paradigm shift in scientific programming, where evaluating both functions and their derivatives is required for most applications. By removing the need to explicitly derive expressions for gradients, development times can be shortened and calculations can be simplified. For these reasons, automatic differentiation has fueled the rapid growth of a variety of sophisticated machine learning techniques over the past decade, but is now also increasingly showing its value to support ab initio simulations of quantum systems and enhance computational quantum chemistry. Here, we present an open-source differentiable quantum chemistry simulation code and explore applications facilitated by automatic differentiation: (1) calculating molecular perturbation properties, (2) reoptimizing a basis set for hydrocarbons, (3) checking the stability of self-consistent field wave functions, and (4) predicting molecular properties via alchemical perturbations.

Investigating mechanisms of state localization in highly ionized dense plasmas.

We present experimental observations of K_{ß} emission from highly charged Mg ions at solid density, driven by intense x rays from a free electron laser. The presence of K_{ß} emission indicates the n=3 atomic shell is relocalized for high charge states, providing an upper constraint on the depression of the ionization potential. We explore the process of state relocalization in dense plasmas from first principles using finite-temperature density functional theory alongside a wave-function localization metric, and find excellent agreement with experimental results.

Risk mapping and estimation of COVID-19 transmission in South Sulawesi, Indonesia by a self-identification survey.

The rapid transmission rate of coronavirus disease 2019 (COVID-19) is multi-factorial but primarily due to population mobility and aggregation. This research aimed at estimating the rate based on risk mapping and investigation of geospatial distribution. It was divided into different phases that included data collection through a self-identification form available online; data validation of the data collected; application of spatial statistics; comparison with official numbers of positive COVID-19; and mapping of the results. The results show that self-identification based on procurement of independent personal data online had an accuracy of 89%.

Superspreading in early transmissions of COVID-19 in Indonesia.

This paper presents a study of early epidemiological assessment of COVID-19 transmission dynamics in Indonesia. The aim is to quantify heterogeneity in the numbers of secondary infections. To this end, we estimate the basic reproduction number [Formula: see text] and the overdispersion parameter [Formula: see text] at two regions in Indonesia: Jakarta-Depok and Batam. The method to estimate [Formula: see text] is based on a sequential Bayesian method, while the parameter [Formula: see text] is estimated by fitting the secondary case data with a negative binomial distribution. Based on the first 1288 confirmed cases collected from both regions, we find a high degree of individual-level variation in the transmission. The basic reproduction number [Formula: see text] is estimated at 6.79 and 2.47, while the overdispersion parameter [Formula: see text] of a negative-binomial distribution is estimated at 0.06 and 0.2 for Jakarta-Depok and Batam, respectively. This suggests that superspreading events played a key role in the early stage of the outbreak, i.e., a small number of infected individuals are responsible for large numbers of COVID-19 transmission. This finding can be used to determine effective public measures, such as rapid isolation and identification, which are critical since delay of diagnosis is the most common cause of superspreading events.

Machine learning applied to proton radiography of high-energy-density plasmas.

Proton radiography is a technique extensively used to resolve magnetic field structures in high-energy-density plasmas, revealing a whole variety of interesting phenomena such as magnetic reconnection and collisionless shocks found in astrophysical systems. Existing methods of analyzing proton radiographs give mostly qualitative results or specific quantitative parameters, such as magnetic field strength, and recent work showed that the line-integrated transverse magnetic field can be reconstructed in specific regimes where many simplifying assumptions were needed. Using artificial neural networks, we demonstrate for the first time 3D reconstruction of magnetic fields in the nonlinear regime, an improvement over existing methods, which reconstruct only in 2D and in the linear regime. A proof of concept is presented here, with mean reconstruction errors of less than 5% even after introducing noise. We demonstrate that over the long term, this approach is more computationally efficient compared to other techniques. We also highlight the need for proton tomography because (i) certain field structures cannot be reconstructed from a single radiograph and (ii) errors can be further reduced when reconstruction is performed on radiographs generated by proton beams fired in different directions.

Quantitative shadowgraphy and proton radiography for large intensity modulations.

Shadowgraphy is a technique widely used to diagnose objects or systems in various fields in physics and engineering. In shadowgraphy, an optical beam is deflected by the object and then the intensity modulation is captured on a screen placed some distance away. However, retrieving quantitative information from the shadowgrams themselves is a challenging task because of the nonlinear nature of the process. Here, we present a method to retrieve quantitative information from shadowgrams, based on computational geometry. This process can also be applied to proton radiography for electric and magnetic field diagnosis in high-energy-density plasmas and has been benchmarked using a toroidal magnetic field as the object, among others. It is shown that the method can accurately retrieve quantitative parameters with error bars less than 10%, even when caustics are present. The method is also shown to be robust enough to process real experimental results with simple pre- and postprocessing techniques. This adds a powerful tool for research in various fields in engineering and physics for both techniques.

Optimization of plasma amplifiers.

Plasma amplifiers offer a route to side-step limitations on chirped pulse amplification and generate laser pulses at the power frontier. They compress long pulses by transferring energy to a shorter pulse via the Raman or Brillouin instabilities. We present an extensive kinetic numerical study of the three-dimensional parameter space for the Raman case. Further particle-in-cell simulations find the optimal seed pulse parameters for experimentally relevant constraints. The high-efficiency self-similar behavior is observed only for seeds shorter than the linear Raman growth time. A test case similar to an upcoming experiment at the Laboratory for Laser Energetics is found to maintain good transverse coherence and high-energy efficiency. Effective compression of a 10kJ, nanosecond-long driver pulse is also demonstrated in a 15-cm-long amplifier.

Compression of X-ray Free Electron Laser Pulses to Attosecond Duration.

State of the art X-ray Free Electron Laser facilities currently provide the brightest X-ray pulses available, typically with mJ energy and several hundred femtosecond duration. Here we present one- and two-dimensional Particle-in-Cell simulations, utilising the process of stimulated Raman amplification, showing that these pulses are compressed to a temporally coherent, sub-femtosecond pulse at 8% efficiency. Pulses of this type may pave the way for routine time resolution of electrons in nm size potentials. Furthermore, evidence is presented that significant Landau damping and wave-breaking may be beneficial in distorting the rear of the interaction and further reducing the final pulse duration.

Disability in people affected by leprosy: the role of impairment, activity, social participation, stigma and discrimination.

BACKGROUND: Leprosy-related disability is a challenge to public health, and social and rehabilitation services in endemic countries. Disability is more than a mere physical dysfunction, and includes activity limitations, stigma, discrimination, and social participation restrictions. We assessed the extent of disability and its determinants among persons with leprosy-related disabilities after release from multi drug treatment. METHODS: We conducted a survey on disability among persons affected by leprosy in Indonesia, using a Rapid Disability Appraisal toolkit based on the International Classification of Functioning, Disability and Health. The toolkit included the Screening of Activity Limitation and Safety Awareness (SALSA) scale, Participation Scale, Jacoby Stigma Scale (anticipated stigma), Explanatory Model Interview Catalogue (EMIC) stigma scale and Discrimination assessment. Community members were interviewed using a community version of the stigma scale. Multivariate linear regression was done to identify factors associated with social participation. RESULTS: Overall 1,358 persons with leprosy-related disability (PLD) and 931 community members were included. Seventy-seven percent of PLD had physical impairments. Impairment status deteriorated significantly after release from treatment (from 59% to 77%). Around 60% of people reported activity limitations and participation restrictions and 36% anticipated stigma. As for participation restrictions and stigma, shame, problems related to marriage and difficulties in employment were the most frequently reported problems. Major determinants of participation were severity of impairment and level of education, activity and stigma. Reported severity of community stigma correlated with severity of participation restrictions in the same districts. DISCUSSION: The majority of respondents reported problems in all components of disability. The reported physical impairment after release from treatment justifies ongoing monitoring to facilitate early prevention. Stigma was a major determinant of social participation, and therefore disability. Stigma reduction activities and socio-economic rehabilitation are urgently needed in addition to strategies to reduce the development of further physical impairment after release from treatment.