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The purpose of this paper is to explore risk factors contained in the existing UK domestic abuse (DA) risk assessment tool: domestic abuse, stalking and harassment and honour-based violence (DASH) for individual predictive validity of DA recidivism using data from Devon and Cornwall Constabulary.

In total, 1,441 DA perpetrators were monitored over a 12-month period, and 270 (18.7 per cent) went on to commit a further DA offence. The individual risk factors which were associated and predictive of increased risk of recidivism were identified.

Only four of the individual risk factors were significantly associated with an increased risk of DA recidivism: “criminal history”, “problems with alcohol”, “separation” and “frightened”. Therefore, 21 of the risk factor items analysed could not discriminate between non-recidivist and recidivist perpetrators. Only two risk factors were able to significantly predict the recidivist group when compared to the non-recidivist group. These were identified as “criminal history” and “separated”. Of those who did commit a further DA offence in the following 12 months, 133 were violent and 137 were non-violent. The risk factors associated with these types of recidivism are identified.

The implications for UK police practice and the DASH risk assessment tool are discussed. By identifying key individual factors that can prioritise those individuals likely to recidivate and the severity of that recidivism, this could assist police decision making regarding the response and further prevention of DA incidents. The validation of association between individual factors and DA recidivism should improve the accuracy of risk levels.

This is the first large-scale validation of the individual risk factors contained within the UK’s DA risk assessment tool. It should be noted that the validity of the DASH tool itself was not examined within the current study.

The purpose of this paper is to examine neural document clustering techniques, e.g. self‐organising map (SOM) or growing neural gas (GNG), usually assume that textual information is stationary on the quantity.

The authors propose a novel dynamic adaptive self‐organising hybrid (DASH) model, which adapts to time‐event news collections not only to the neural topological structure but also to its main parameters in a non‐stationary environment. Based on features of a time‐event news collection in a non‐stationary environment, they review the main current neural clustering models. The main deficiency is a need of pre‐definition of the thresholds of unit‐growing and unit‐pruning. Thus, the dynamic adaptive self‐organising hybrid (DASH) model is designed for a non‐stationary environment.

The paper compares DASH with SOM and GNG based on an artificial jumping corner data set and a real world Reuters news collection. According to the experimental results, the DASH model is more effective than SOM and GNG for time‐event document clustering.

A real world environment is dynamic. This paper provides an approach to present news clustering in a non‐stationary environment.

Text clustering in a non‐stationary environment is a novel concept. The paper demonstrates DASH, which can deal with a real world data set in a non‐stationary environment.

Established data revealed a relationship between obesity and increasing the risk of mortality and morbidity of chronic diseases. There are conflicting data regarding the association between adherence of dietary approach to stop hypertension (DASH) and obesity. Therefore, this study aims to investigate this relationship among a large sample of Iranian adults.

This cross-sectional study was performed by 10,693 individuals; 6750 individuals related to Yazd Health Study living in the urban area and 3943 individuals related to Shahedieh cohort study living in the suburb area. Dietary intake was evaluated by using a validated food frequency questionnaire. In all participants, anthropometric indices including body mass index were measured. The DASH score was considered using gender-specific quintiles of DASH items. To evaluate the relationship of DASH diet and obesity, multivariate logistic regression analysis was used.

By adjusting confounders, participants in highest quintiles of DASH diet were compared to the lowest have lower odds of obesity in suburb area (odds ratio [OR]: 0.78; 95% confidence interval [CI]: 0.63, 0.96), in urban (OR: 0.71; 95% CI: 0.52, 0.99) and in whole population of both studies (OR: 0.75; 95% CI: 0.63, 0.90). Besides, more compliance of women to this diet in urban (OR: 0.64; 95% CI: 0.48, 0.85) and population of both studies (OR: 0.77; 95% CI: 0.62, 0.96) were associated with reduced odds of central obesity.

Considering this study limitations, the following can be mentioned: in this cross-sectional study, the causal relationship between DASH diet and obesity could not be assessed. Consequently, further prospective studies are required in this area. Second, although a valid food frequency questionnaire was used, but there was a measurement error and an error in the classification of people participating in the study. Moreover, we cannot reject the possibility of residual confounding bias because unknown or unmeasured confounders may exist that affected our results. Finally, our participants with odds of obesity might have been advised to reduce their fat intake, which led them to alter their dietary habits. However, such possibility cannot be resolved in a cross-sectional study.

DASH dietary pattern could decrease odds of obesity in both urban and suburb area and central obesity in urban area only. Further prospective studies are needed for causal conclusion.

This paper determined sodium provisions from a seven-day cycle menu and commissary at a rural Southwest County jail and compared it to Dietary Reference Intakes (DRI) and Dietary Approaches to Stop Hypertension (DASH) recommendations for sodium.

A seven-day cycle menu and commissary items were used to determine sodium content for each meal and commissary pack. Estimates for the menu and commissary packs paired with the menu (commissary scenarios) were converted to a daily average of sodium and compared to DRI and DASH recommendations.

Menu provisions provided 167% of daily DRI sodium recommendations and 256% of daily DASH sodium recommendations. The sodium content for individual commissary scenarios averaged 218% of DRI and 334% of DASH recommendations. Commissary items are notably high in sodium and if eaten can significantly exceed dietary recommendations.

Small changes to one meal within the cycle menu and the inclusion of fresh or frozen produce could reduce sodium content to align with DRI and DASH recommendations.

From the blowing snows of the Canadian Arctic and the blasting sands of the Saudi Arabian desert to the high of the Colorado Rockies and the wind swept plains of Spain, the de Havilland of Canada's Dash 7 has come out a winner.

This research applies a static hedging portfolio method derived from Derman, Ergener, and Kani (1995) (henceforth Derman's SHP method) and a new SHP method with European cash-or-nothing binary options developed by Chung, Shih, and Tsai (2013) to price European continuous double barrier (ECDB) options and the rebates of the ECDB options. Our numerical results indicate that the new SHP method outperforms Derman's SHP method in terms of efficiency and effectiveness under all circumstances.

This study aims to identify the dietary patterns of two groups of subjects (with and without COVID-19), and to assess the relationship of findings with the prognosis of COVID-19 and metabolic risk parameters.

This study included 100 individuals in the age range of 19–65 years. The medical history, and data on biochemical, hematological and inflammatory indicators were retrieved from the files. A questionnaire for the 24-h food record and the food intake frequency was administered in face-to-face interviews, and dietary patterns of subjects were assessed.

In individuals with COVID-19, the hip circumference, the waist-hip ratio and the body fat percentage were significantly higher (p < 0.05), and the muscle mass percentage was significantly lower (p < 0.05). Mediterranean diet adherence screener (MEDAS), dietary approaches to stop hypertension (DASH) and healthy eating ındex-2015 (HEI-2015) scores were low in the two groups. A linear correlation of DASH scores was found with the muscle mass percentage (p = 0.046) and a significant inverse correlation of with the body fat percentage (p = 0.006). HEI-2015 scores were significantly and negatively correlated with body weight, body mass index, waist circumference, hip circumference and neck circumference (p < 0.05). Every one-unit increase in MEDAS, DASH and HEI-2015 scores caused reductions in C-reactive protein levels at different magnitudes. Troponin-I was significantly and negatively correlated with fruit intake (p = 0.044), a component of a Mediterranean diet and with HEI-2015 total scores (p = 0.032).

The limitation of this study includes the small sample size and the lack of dietary interventions. Another limitation is the use of the food recall method for the assessment of dietary patterns. This way assessments were performed based on participants’ memory and statements.

Following a healthy diet pattern can help reduce the metabolic risks of COVİD-19 disease.

Despite these limitations, this study is valuable because, to the best of the authors’ knowledge, it is the first study demonstrating the association of dietary patterns with disease prognosis and metabolic risks concerning COVID-19. This study suggests that dietary patterns during the COVID-19 process may be associated with several metabolic risks and inflammatory biomarkers.

The purpose of this paper is to examine how a middle school science teacher, new to programming, supports students in learning to debug physical computing systems consisting of programmable sensors and data displays.

This case study draws on data collected during an inquiry-oriented instructional unit in which students learn to collect, display and interpret data from their surrounding environment by wiring and programming a physical computing system. Using interaction analysis, the authors analyzed video recordings of one teacher’s (Gabrielle) pedagogical moves as she supported students in debugging their systems as they drew upon a variety of embodied, material and social resources.

This study presents Gabrielle’s debugging interactional grammar, highlighting the pedagogical possibilities for supporting students in systematic ways, providing affective support (e.g. showing them care and encouragement) and positioning herself as a learner with the students. Gabrielle’s practice, and therefore her pedagogy, has the potential to support students in becoming better debuggers on their own in the future.

While much of the prior work on learning to debug focuses on learner actions and possible errors, this case focuses on an educator’s debugging pedagogy centered on the educator debugging with the learners. This case study illustrates the need for educators to exhibit deft facilitation, vulnerability and orchestration skills to support student development of their own process for and agency in debugging.

This study aims to analyze the time-varying correlation between the cryptocurrency policy uncertainty (UCRY Policy) and cryptocurrency returns. More specifically, it analyzes whether these correlations vary according to the uncertainty attributable to salient events such as China banning ICOs, cryptocurrency exchanges attacks, Coronavirus (Covid-19) pandemic crisis and the United States (U.S.) Security and Exchange Commission’s (SEC’s) announcement about Ripple.

To measure the dynamic relationship, it uses the dynamic conditional correlation (DCC) model of Engle (2002) to consider time variation in UCRY Policy and cryptocurrency returns. The data set encompasses the weekly frequency data of the UCRY Policy and Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), Stellar (XLM), Dash (DASH), Monero (XMR) from 4 September 2016, to 21 February 2021.

Empirical findings indicate that the correlations between the UCRY Policy and the BTC, ETH, LTC, XRP, XLM, DASH and XMR returns are consistently negative. Thus, an increase in the volatility of the UCRY Policy can lead to a decrease in volatility for BTC, ETH, LTC, XRP, XLM, DASH and XMR returns. Besides, these findings indicate that the estimated DCC is not only time-varying but also substantially responsive to salient events, such as China banning ICOs, cryptocurrency exchanges attacks, the Covid-19 pandemic crisis and SEC’s announcement about Ripple. Besides, empirical findings show that cryptocurrency returns are adversely impacted by UCRY Policy during the salient events (China bans ICOs, the hack of cryptocurrency exchanges, Covid-19 crisis), suggesting their failure to act as a hedge or safe-haven asset.

To the best of the author’s knowledge, this study investigates the time-varying correlation between UCRY Policy and cryptocurrency returns. Besides, this study may be useful for new studies and fill a gap in the finance literature, due to the limited number of studies on the UCRY Policy in the finance literature.

Computer simulation techniques are used to evaluate the performance of a welding assembly line which requires modification to achieve some of the philosophies of a just‐in‐time manufacturing environment. The simulation of the existing line using GPSS/H confirmed the operational problems which had been previously identified, and allowed the validation of the computer model. The computer model was then altered to evaluate the performance of several alternative modified assembly lines which would avoid the present operational problems. Discusses some of the techniques used to develop these models, their simulation using the GPSS/H simulation language, and the benefits of using simulation in analysing manufacturing processes.