Window Analysis Research Articles

Optimizing launch window opportunities for ESA's comet Interceptor mission using primer vector theory

Comet Interceptor (Comet-I) was selected in June 2019 as the first ESA F-Class mission and aims to explore a pristine comet, which will visit the inner Solar System for the first time. Comet-I will hitch a ride to a Sun-Earth L2 quasi-halo orbit, as a co-passenger in ESA's M4 ARIEL's launch. At the time of writing, this is scheduled for 2029. It will then remain there awaiting the right departure conditions to leave definitively and intercept a newly discovered comet. Comet-I will be the first mission to be designed and launched without an already identified target. Given the latter, the Comet-I Target Identification Working Group is tasked with the continuous follow-up of newly discovered comets, to analyse their characteristics as virtual targets for Comet-I. The goal of the paper is to inform the decision-making process that will eventually be necessary to commit Comet-I spacecraft to its final target.Within this context, this paper presents a trajectory optimization process based on Primer Vector theory that considers intercept trajectories with multiple revolutions and multiple deep space manoeuvres (DSM). The process is applied to 21 long period comets, which were discovered since July 1, 2020 and whose characteristics are of interest for the Comet-I mission. In fact, one of the latest of these recently discovered comets, C/2023H2, is the most accessible virtual target discovered at the time of writing, with delta-V intercept costs well within Comet-I capabilities. A complete launch window analysis is thus presented for C/2023H2, which accounts not only for the most optimal delta-V intercept transfer, but also considers all launch options that satisfy all Comet-I's current boundary conditions and flyby constraints. A systematic launch window analysis is made possible by a grid search approach on which departure and fly-by dates are fixed and the remaining design variables, including the times for manoeuvres and their Δv impulse vector, are optimized via Primer Vector theory. This allows to understand the latest possible departure date, but also the sensitivity to departure and arrival dates of key encounter characteristics such as relative fly-by speed, the solar aspect angle and the Earth distance.

Comparative Analysis of Predictive Models Using Synthetic Data for Financial Fraud Detection

This paper conducts a comparative analysis of predictive models in financial fraud detection using synthetic data. Financial fraud is an adversarial problem that is becoming increasingly common with technological advancement; fraudsters use techniques such as social engineering to lure victims into releasing confidential information for nefarious purposes. One of the types of fraud this research aims to tackle is detecting fraudulent transactions processed on customers' accounts as customers are usually responsible for bearing the costs associated with fraud. The study examines machine learning techniques applied to mitigate fraud in financial transactions under controlled five scenarios which include unusual transaction type, account behaviour profiling, and temporal correlation of transactions, time based velocity checks and 1 hour window analysis. A synthetic dataset comprising 2,549,085 transactions categorized into fraud and non fraud classes (849,695 fraudulent transactions and 1,699,390 non fraudulent transactions) was utilized for analysis. Transaction level features were employed to evaluate the performance of various machine learning classifiers and Deep Learning Algorithms. The classifiers examined include SVM, Decision Trees, Logistic Regression, Gradient Boosting, LSTM, Deep Neural Network, CNN, and Random Forest. Among these, the Random Forest Classifier demonstrates superior performance, achieving an accuracy rate of 99.18%. The evaluation primarily focuses on comparing the performance of classifiers using the confusion matrix as the primary evaluation metric. The findings indicate that the Random Forest Classifier, Decision Trees and Gradient Boosting are suitable classifiers for detecting financial fraud.

Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984-2022.

This study reviews scholarly publications on data envelopment analysis (DEA) studies on acute care hospital (ACH) efficiency published between 1984 and 2022 in scholarly peer-reviewed journals.We employ systematic literature review (SLR) method to identify and analyze pertinent past research using predetermined steps. The SLR offers a comprehensive resource that meticulously analyzes DEA methodology for practitioners and researchers focusing on ACH efficiency measurement. The articles reviewed in the SLR are analyzed and synthesized based on the nature of the DEA modelling process and the key findings from the DEA models. The key findings from the DEA models are presented under the following sections: effects of different ownership structures; impacts of specific healthcare reforms or other policy interventions; international and multi-state comparisons; effects of changes in competitive environment; impacts of new technology implementations; effects of hospital location; impacts of quality management interventions; impact of COVID-19 on hospital performance; impact of teaching status, and impact of merger. Furthermore, the nature of DEA modelling process focuses on use of sensitivity analysis; choice of inputs and outputs; comparison with Stochastic Frontier Analysis; use of congestion analysis; use of bootstrapping; imposition of weight restrictions; use of DEA window analysis; and exogenous factors. The findings demonstrate that, despite several innovative DEA extensions and hospital applications, over half of the research used the conventional DEA models. The findings also show that the most often used inputs in the DEA models were labor-oriented inputs and hospital beds, whereas the most frequently used outputs were outpatient visits, followed by surgeries, admissions, and inpatient days. Further research on the impact of healthcare reforms and health information technology (HIT) on hospital performance is required, given the number of reforms being implemented in many countries and the role HIT plays in enhancing care quality and lowering costs. We conclude by offering several new research directions for future studies.

The Influence of Self Instruction Techniques on Students’ Learning Burnout at State Madrasah Aliyah

This research was conducted to determine the influence provided by group counseling services using Self Instruction to minimize the level of learning boredom of XI MIPA 3 students at MAN 3 Medan. This research method uses an experiment with a one group pretest-posttest design. Where there were 32 students as the population and a sample of 7 students who had high learning boredom scores was taken using the purposive sampling technique. The data analysis techniques used are the Normality Test and the T Test (paired sample test). With the help of the SPSS Version 22.0 For Windows analysis tool. Based on the research results before and after the treatment, the Sig value was obtained. (2-tailed) is 0.000, then < 0.05 H0 is rejected and Ha is accepted. The results of this study showed that there was a decrease in the level of student learning boredom after being given group counseling services using the Self Instruction technique as treatment for students at Madrasah Aliyah Negeri 3 Medan.

Exploring landscape and geologic controls on spatial patterning of streambank groundwater discharge in a mixed land use watershed

AbstractPreferential groundwater discharge features along stream corridors are ecologically important at local and stream network scales, yet we lack quantification of the multiscale controls on the spatial patterning of groundwater discharge. Here we identify physical attributes that best explain variation in the presence and lateral extent of preferential groundwater discharges along two 5th order streams, the Housatonic and Farmington Rivers, and 32 1st to 4th order reaches across the Farmington River network. We mapped locations of preferential groundwater discharge exposed along streambanks using handheld thermal infrared cameras paired with high‐resolution topographic and land use land cover datasets, surficial soil characteristic maps, and depth‐to‐bedrock geophysical measurements. The unconfined Housatonic River, MA, USA (12 km) had fewer discharge locations and less lateral extent (41 discharge locations with 38 m of active discharge/km of river) compared to the partially confined Farmington River, CT, USA (26 km; 169 discharge locations with 129 m of active discharge/km of river). Using a moving window analysis, we found along both rivers that discharge was more likely to occur where bank slopes were steeper, floodplain extent was narrower, and degree of confinement was higher. Along the Farmington River, groundwater discharge was more likely to occur where saturated hydraulic conductivity was higher and depth‐to‐bedrock was shallower. Among the 32 stream reaches surveyed (33.2 km of total stream length) within the Farmington River watershed, preferential discharge was observed in all but two stream reaches, varied from 0 to 25% of lateral extent along stream banks (mean = 6%), and was more likely to occur where stream reach slopes were steep, saturated hydraulic conductivity was high, and watershed urbanization was low. Our results show that, though both surface (e.g., topographic, land use land cover) and subsurface (e.g., soil characteristics, bedrock depth) factors control the prevalence of streambank preferential groundwater discharge, the dominant controls vary across valley settings and stream sizes.

Short-time acoustic indices for monitoring urban-natural environments using artificial neural networks

Urban-natural environments, proximal to rapidly urbanizing cities, provide essential ecosystem functions that benefit both city residents and ecological communities. With escalating urbanization, the resilience of these ecosystems is being progressively challenged, highlighting the need for robust monitoring mechanisms. Acoustic monitoring has emerged as an unobtrusive method to evaluate the status of these environments, capitalizing on indicators that reflect both landscape features and specific acoustic events. Despite potentially offering significant insights, this approach generates a large volume of acoustic data, introducing complexities in subsequent analyses. To mitigate this, we propose integrating artificial neural networks with acoustic indices to enhance data analysis. Our approach emphasizes the usefulness of short-time acoustic indices, computed over finite-duration analysis windows, to enhance polyphonic sound event detection accuracy. Empirical results support the performance of our approach, registering both an F1-Score and an error rate of 0.614. Overall, this study delineates a novel paradigm geared towards enhancing or preserving the biological diversity of urban-natural environments in areas with population growth and urban development.

Efficiency and Stability of Zakat Institutions in Malaysia and Indonesia: DEA Window Analysis

The purpose of this study is to measure the efficiency and stability of zakat institutions in Indonesia and Malaysia. There are 14 zakat institutions that will be analyzed using data envelopment analysis (DEA) window analysis during the period of 2016 to 2020. The result shows that the average efficiency of zakat institution is 78% during the observation period. The inefficiency of zakat institutions from both countries is caused by the less-than-optimal distribution of zakat funds. The best performing zakat institutions are Selangor, BSMU, BRI Foundation, and Baznas because they have the highest efficiency and stability. This study has filled the gap in previous studies that measure the efficiency of zakat institutions with the standard DEA approach. Zakat institution managers can identify potential improvements in input and output variables to achieve maximum level of efficiency.

Condensation risk-based applicability analysis and design of a dynamic thermal insulation window with ventilated airflow in different climates

The exhaust air insulation glazing system (EAIG) integrates exhaust air heat recovery and dynamic insulation to minimize heat loss/gain through the window by allowing the exhaust air to be ventilated through the air gap between two glass panels. However, condensation may form on the internal glazing surface of EAIG during winter. How to prevent the occurrence of condensation and improve technical feasibility of EAIG for various climatic conditions has not been addressed. A numerical model was developed to determine the hourly glazing surface temperatures of EAIG and identify whether condensation will form or not during winter. The condensation probability of EAIG was quantitatively estimated considering various window orientations and climatic conditions. Furthermore, the effectiveness of some potential measures for preventing condensation in EAIG was also investigated. The results indicate that outdoor weather conditions significantly influence the condensation of EAIG. For a climate with a relatively mild winter, slight condensation occurs within the EAIG, while in severe cold climate, long-term condensation with a probability of 45.4 % occurs, which detrimentally affects the practical application of EAIG. Moreover, reducing indoor humidity and adding a low-e coating can effectively reduce condensation probability and expand technical feasibility of EAIG for various climatic conditions. In severe cold climates, the condensation probability decreases from 45.5 % to 6.5 % when employing an additional low-e coating. The findings in this study are beneficial to providing a design guideline for engineers and architectural designers, and enhancing the applicability of EAIG in various climatic conditions.

Adult body mass is heritable, positively genetically correlated and under selection of differing shapes between the sexes in a bird with little apparent sexual dimorphism.

In most animals, body mass varies with ecological conditions and is expected to reflect how much energy can be allocated to reproduction and survival. Because the sexes often differ in their resource acquisition and allocation strategies, variations in adult body mass and their consequences on fitness can differ between the sexes. Assessing the relative contributions of environmental and genetic effects (i.e. heritability)-and whether these effects and their fitness consequences are sex-specific-is essential to gain insights into the evolution of sexual dimorphism and sexual conflicts. We used 20+ years of data to study the sources of variation in adult body mass and associated fitness consequences in a bird with biparental care, the Alpine swift (Tachymarptis melba). Swifts appear monomorphic to human observers, though subtle dimorphisms are present. We first investigated the effects of weather conditions on adult body mass using a sliding window analysis approach. We report a positive effect of temperature and a negative effect of rainfall on adult body mass, as expected for an aerial insectivorous bird. We then quantified the additive genetic variance and heritability of body mass in both sexes and assessed the importance of genetic constraints on mass evolution by estimating the cross-sex genetic correlation. Heritability was different from zero in both sexes at ~0.30. The positive cross-sex genetic correlation and comparable additive genetic variance between the sexes suggest the possibility for evolutionary constraints when it comes to body mass. Finally, we assessed the sex-specific selection on adjusted body mass using multiple fitness components. We report directional positive and negative selection trending towards stabilizing and diversifying selection on females and males respectively in relation to the weighted proportion of surviving fledglings. Overall, these results suggest that while body mass may be able to respond to environmental conditions and evolve, genetic constraints would result in similar changes in both sexes or an overall absence of response to selection. It remains unclear whether the weak (1%) dimorphism in Alpine swift body mass we report is simply a result of the similar fitness peaks between the sexes or of genetic constraints.

Examination and analysis of a stored stucco window in the conservation lab of Bab Al-azab area, citadel of Salah Al-din Al-Ayyubi, Cairo Egypt

Historic areas are at the forefront of global attention because of their historical and social traits that reflect human civilization, giving them an economic weight that helps preserve the city's historical identity. For that, the aim of this paper is based on examining and analyzing the components of a deteriorated stucco window kept in the Restoration Lab of Bab Al-azab Area of the Citadel of Salah al-din Al-ayyubi, which was carried out via different techniques. The stucco window used in this study was inspected using a digital microscope and scanning electron microscope to determine the degradation of the stucco and wooden frame's outside surface. Besides, the polarized microscope involves studying minerals by studying the optical properties of stucco. Also, the light microscope was used to identify the wood. Besides, energy dispersive X-ray (EDX) showed the presence of constituent elements for both the stucco and glass. In addition, X-ray diffraction estimated cellulose crystallinity to decrease. A technique called Fourier transform infrared (FTIR) was also used to find changes in lignin, gypsum, anhydrite, and calcite, as well as cellulose and hemicellulose. A microbiological investigation indicated the presence of a fungal infestation on a wooden frame. This study showed that Salah El-Din Castle's semi-separate historical areas can be considered one of its architectural, planning, and historical models, which have their fabric and characteristics.

Phase‐Accurate Internal Tides in a Global Ocean Forecast Model: Potential Applications for Nadir and Wide‐Swath Altimetry

AbstractInternal tides (ITs) play a critical role in ocean mixing, and have strong signatures in ocean observations. Here, global IT sea surface height (SSH) in nadir altimetry is compared with an ocean forecast model that assimilates de‐tided SSH from nadir altimetry. The forecast model removes IT SSH variance from nadir altimetry at skill levels comparable to those achieved with empirical analysis of nadir altimetry. Accurate removal of IT SSH is needed to fully reveal lower‐frequency mesoscale eddies and currents in altimeter data. Analysis windows of order 30–120 days, made possible by the frequent (hourly) outputs of the forecast model, remove more IT SSH variance than longer windows. Forecast models offer a promising new approach for global internal tide mapping and altimetry correction. Because they provide information on the full water column, forecast models can also help to improve understanding of the underlying dynamics of ITs.

Identifying topology of distribution substation in power Internet of Things using dynamic voltage load fluctuation flow analysis.

At present, the reconfiguration, maintenance, and review of power lines play a pivotal role in maintaining the stability of electrical grid operations and ensuring the accuracy of electrical energy measurements. These essential tasks not only guarantee the uninterrupted functioning of the power system, thereby improving the reliability of the electricity supply but also facilitate precise electricity consumption measurement. In view of these considerations, this article endeavors to address the challenges posed by power line restructuring, maintenance, and inspections on the stability of power grid operations and the accuracy of energy metering. To accomplish this goal, this article introduces an enhanced methodology based on the hidden Markov model (HMM) for identifying the topology of distribution substations. This approach involves a thorough analysis of the characteristic topology structures found in low-voltage distribution network (LVDN) substations. A topology identification model is also developed for LVDN substations by leveraging time series data of electricity consumption measurements and adhering to the principles of energy conservation. The HMM is employed to streamline the dimensionality of the electricity consumption data matrix, thereby transforming the topology identification challenge of LVDN substations into a solvable convex optimization problem. Experimental results substantiate the effectiveness of the proposed model, with convergence to minimal error achieved after a mere 50 iterations for long time series data. Notably, the method attains an impressive discriminative accuracy of 0.9 while incurring only a modest increase in computational time, requiring a mere 35.1 milliseconds. By comparison, the full-day data analysis method exhibits the shortest computational time at 16.1 milliseconds but falls short of achieving the desired accuracy level of 0.9. Meanwhile, the sliding time window analysis method achieves the highest accuracy of 0.95 but at the cost of a 50-fold increase in computational time compared to the proposed method. Furthermore, the algorithm reported here excels in terms of energy efficiency (0.89) and load balancing (0.85). In summary, the proposed methodology outperforms alternative approaches across a spectrum of performance metrics. This article delivers valuable insights to the industry by fortifying the stability of power grid operations and elevating the precision of energy metering. The proposed approach serves as an effective solution to the challenges entailed by power line restructuring, maintenance, and inspections.

Navigating Choppy Waters: Interplay between Financial Stress and Commodity Market Indices

Financial stress can have significant implications for individuals, businesses, asset prices and the economy as a whole. This study examines the nonlinear structure and dynamic changes in the multifractal behavior of cross-correlation between the financial stress index (FSI) and four well-known commodity indices, namely Commodity Research Bureau Index (CRBI), Baltic Dry Index (BDI), London Metal Index (LME) and Brent Oil prices (BROIL), using multifractal detrended cross correlation analysis (MFDCCA). For analysis, we utilized daily values of FSI and commodity index prices from 16 June 2016 to 9 July 2023. The following are the most important empirical findings: (I) All of the chosen commodity market indices show cross correlations with the FSI and have notable multifractal characteristics. (II) The presence of power law cross-correlation implies that a noteworthy shift in FSI is likely to coincide with a considerable shift in the commodity indices. (III) The multifractal cross-correlation is highest between FSI and Brent Oil (BROIL) and lowest with LME. (IV) The rolling windows analysis reveals a varying degree of persistency between FSI and commodity markets. The findings of this study have a number of important implications for commodity market investors and policymakers.

Enhancing Agricultural Ecological Efficiency in China: An Evolution and Pathways under the Carbon Neutrality Vision

Under China’s carbon neutrality vision, the integration of resource consumption and ecological protection in agricultural production requires increased attention to agroecology. This study comprehensively integrates the carbon impact within agricultural ecological efficiency (AEE). Based on the window analysis of super-SBM-undesirable and geographically and temporally weighted regression (GTWR) models, AEE and its influencing factors in China’s 31 provinces from 2000 to 2021 are measured, and then types categorized, mechanisms investigated, and optimization pathways provided. The results revealed that from 2000 to 2021, the AEE showed a growing trend, with the spatial agglomeration degree increased. Its gravity center presented a migration path of “northeast–southwest”. Driven by internal and external mechanisms, both agricultural production conditions and regional socio-economic conditions had a stronger positive effect and wider range of promotion on China’s AEE, and the positive contribution of physical environmental conditions decreased, while the inhibitory effect increased. Furthermore, this study divided three zones and emphasized that the agri-ecological power zone plays a demonstrative role in carbon sequestration and efficiency improvement, the eco-pollution balance zone needs to strike a balance between economic development and agricultural ecological development, and the agri-pollution power zone ought to reduce agricultural carbon emissions and mitigate its environmental impact.

Application of broken windows theory to identify flow disruptions in neurosurgery procedure.

Addressing flow disruptions (FDs) in neurosurgery requires a multifaceted approach. Strategies like improved communication protocols, minimizing interruptions, improving coordination among team, optimizing operating room layout, and promoting user-centered design can help mitigate the challenges and enhance the overall flow and safety of neurosurgical procedures. Thirty neurosurgery cases were observed at two tertiary care facilities. The data collected were from wheels into the operating room to wheels out from the operating room. Data points were categorized using a human factors taxonomy known as RIPCHORD-TWA (Realizing Improved Patient Care Through Human-Centered Operating Room Design for Threat Window Analysis). Of the 541 total disruptions observed, coordination issues were the most prevalent (26.25%), followed by layout issues (26.06%), issues related to interruption (22.55%), communication (22.37%), equipment issues (2.40%) and usability issues (0.37%) comprised the remainder of the observations. This translated into one disruption every 2.7 min. Instead of focusing exclusively on errors and adverse events, we propose conceptualizing the accumulation of disruptions as "threat windows" to analyze potential threats to the integrity of the care system. This perspective allows for the improved identification of system weaknesses or threats, affording us the ability to address these inefficiencies and intervene before errors and adverse events may occur.

DETERMINATION OF EFFICIENCIES OF INFORMATION TECHNOLOGY SECTOR COMPANIES TRADED ON BORSA ISTANBUL IN TERMS OF FINANCIAL PERFORMANCES UNDER THE EFFECT OF COVID-19 PANDEMIC BY USING DEA WINDOW ANALYSIS

Window analysis is a time-dependent variant of Data Envelopment Analysis (DEA) and it is based on the moving average method. It is used for showing the trend of Decision Making Units’ (DMUs') performance over various times. The efficiency changes of 13 companies operating in Borsa Istanbul (BIST) Information Technology (IT) sector for quarterly periods between 2019:03 and 2021:06 are evaluated by DEA Window Analysis. The input-oriented CCR model and the input-oriented BCC Windows analysis models are applied on data set. When the CCR model and BCC model window analysis results are evaluated together, one of the first two periods with the highest average efficiency for both of them is 2021:06, and the period with the lowest average efficiency for both of them is 2019:09. DESPC company has the highest average efficiency for both models.

How does managerial ability affect R&D efficiency of emerging-economy high-tech enterprises

ABSTRACT Numerous publications have discussed the nexus between managerial ability (MA) and innovation capability; however, regarding the hi-tech enterprises in transition economies, the nexus between MA and R&D efficiency will be discussed in depth. Considering the data from high-tech listed companies A-shares in Mainland China in 2012–2017, the data envelopment analysis (DEA) window, Tobit panel model, and panel threshold model is utilised to analyse the direct, moderating effects and threshold effect of MA on R&D efficiency of China’s high-tech enterprises. The findings illustrated that MA could significantly facilitate R&D efficiency, and this enhancement effect is interestingly more evident in state-owned enterprises. Enterprise ownership exerts a significant positive moderation effect between MA and R&D efficiency of China’s hi-tech enterprises. Both equity incentive and salary incentive exerts a significant negative moderation effect between MA and R&D efficiency of China’s hi-tech enterprises. In addition, the panel threshold model proved a nonlinear nexus between MA and its R&D efficiency. Based on the abovementioned findings, corresponding countermeasures are proposed.

Dynamic vagal-mediated connectivity of cortical and subcortical central autonomic hubs predicts chronotropic response to submaximal exercise in healthy adults

BackgroundDespite accumulation of a substantial body of literature supporting the role of exercise on frontal lobe functioning, relatively less is understood of the interconnectivity of ventromedial prefrontal cortical (vmPFC) regions that underpin cardio-autonomic regulation predict cardiac chronotropic competence (CC) in response to sub-maximal exercise. MethodsEligibility of 161 adults (mean age = 48.6, SD = 18.3, 68% female) was based upon completion of resting state brain scan and sub-maximal bike test. Sliding window analysis of the resting state signal was conducted over 45-s windows, with 50% overlap, to assess how changes in photoplethysmography-derived HRV relate to vmPFC functional connectivity with the whole brain. CC was assessed based upon heart rate (HR) changes during submaximal exercise (HR change /HRmax (206–0.88 × age) – HRrest). ResultsDuring states of elevated HRV the vmPFC showed greater rsFC with an 83-voxel region of the hypothalamus (p < 0.001, uncorrected). Beta estimates of vmPFC connectivity extracted from a 6-mm sphere around this region emerged as the strongest predictor of CC (b = 0.283, p <.001) than age, BMI, and resting HRV F(8,144) = 6.30, p <.001. ConclusionExtensive glutamatergic innervation of the hypothalamus by the vmPFC allows for top-down control of the hypothalamus and its various autonomic efferents which facilitate chronotropic response during sub-maximal exercise.

1975 Does attendance at a geriatrician led, oncogeriatric clinic, improve the symptoms for older adults with cancer?

Abstract Introduction In the cancer setting, Comprehensive Geriatric Assessment (CGA) reduces chemotherapy toxicity, improves QOL and increases advance directive completion (ASCO 2020: The Geriatric Assessment Comes of Age; Soto-Perez-de-Celis et al; The Oncologist). We wanted to look at whether CGA improved symptomatology, as patients attending our oncogeriatric clinic complained of a range of symptoms, related to their cancer, as well as other co-morbidities and frailty. Methods We retrospectively analysed follow up clinic letters of patients who had attended the oncogeriatric clinic, between June 2022 and June 2023. We used a Lirkert scale, to see whether symptoms they had complained of had 1 – got worse, 2 – stayed the same, 3 – improved or 4 – resolved. Results 32 patients with a wide range of malignancies were included. 59 patients were excluded because they: died before the 2nd appointment, did not require a second appointment, had their second appointment outside the analysis window, DNA or in 1 case the follow up letter could not be found. On average each patient complained of 3 symptoms. 30 different symptoms were noted (2 excluded as there was no mention of them in the 2nd visit.) The top presentations were pain, constipation, low mood, breathlessness, reduced mobility, falls and dizziness. 68% of the symptoms complained of showed improvement – including all the top presentations. The average score on the Lirkert scale was 2.76. 78% of patients had shown improvement or resolution in at least some of their symptoms. Conclusions Our retrospective review shows that older, cancer patients, have a high burden of varied symptomatology, because of their cancer, co-morbidities and frailty. Attendance at an oncogeriatric clinic results in improvement in the symptom burden for the majority of older adults, and an improvement in some symptoms, whether they are related to cancer, or other frailty syndromes.

Evaluation of strategy portfolios

People usually create a portfolio in order to diversify the risk coming from individual investments. To get a high yield with a good level of diversification, investors usually seek professional advice from portfolio managers. However, the true performance of an optimized portfolio usually depends on the correctness of the estimates of the distribution of future returns, which is often a matter of luck rather than skill. Thus, the optimization models may not be better than randomly selected portfolios. Our aim is to find how the so-called strategy portfolios, i.e., portfolios obtained by some decision optimized for a long-run horizon, perform compared to a benchmark, namely, a random investment, under specific market conditions. For this purpose, we evaluate several portfolio strategies over two periods of crisis: the subprime mortgage crisis and the Covid-19 pandemic, as well as run a moving window analysis over a longer horizon. In each case, the results are compared with the performance of random-weight portfolios. We find that if the strategy is minimization, the portfolios perform well; however, for the maximization of the objectives, the results are rather mixed.