Search results for: Concomitant Variables

Authors: Xingyu Gao, Qiang Wu

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Patents play a crucial role in protecting innovation and intellectual property. Early prediction of the impact of artificial intelligence (AI) patents helps researchers and companies allocate resources and make better decisions. Understanding the key factors that influence patent impact can assist researchers in gaining a better understanding of the evolution of AI technology and innovation trends. Therefore, identifying highly impactful patents early and providing support for them holds immeasurable value in accelerating technological progress, reducing research and development costs, and mitigating market positioning risks. Despite the extensive research on AI patents, accurately predicting their early impact remains a challenge. Traditional methods often consider only single factors or simple combinations, failing to comprehensively and accurately reflect the actual impact of patents. This paper utilized the artificial intelligence patent database from the United States Patent and Trademark Office and the Len.org patent retrieval platform to obtain specific information on 35,708 AI patents. Using six machine learning models, namely Multiple Linear Regression, Random Forest Regression, XGBoost Regression, LightGBM Regression, Support Vector Machine Regression, and K-Nearest Neighbors Regression, and using early indicators of patents as features, the paper comprehensively predicted the impact of patents from three aspects: technical, social, and economic. These aspects include the technical leadership of patents, the number of citations they receive, and their shared value. The SHAP (Shapley Additive exPlanations) metric was used to explain the predictions of the best model, quantifying the contribution of each feature to the model's predictions. The experimental results on the AI patent dataset indicate that, for all three target variables, LightGBM regression shows the best predictive performance. Specifically, patent novelty has the greatest impact on predicting the technical impact of patents and has a positive effect. Additionally, the number of owners, the number of backward citations, and the number of independent claims are all crucial and have a positive influence on predicting technical impact. In predicting the social impact of patents, the number of applicants is considered the most critical input variable, but it has a negative impact on social impact. At the same time, the number of independent claims, the number of owners, and the number of backward citations are also important predictive factors, and they have a positive effect on social impact. For predicting the economic impact of patents, the number of independent claims is considered the most important factor and has a positive impact on economic impact. The number of owners, the number of sibling countries or regions, and the size of the extended patent family also have a positive influence on economic impact. The study primarily relies on data from the United States Patent and Trademark Office for artificial intelligence patents. Future research could consider more comprehensive data sources, including artificial intelligence patent data, from a global perspective. While the study takes into account various factors, there may still be other important features not considered. In the future, factors such as patent implementation and market applications may be considered as they could have an impact on the influence of patents.

Keywords: patent influence, interpretable machine learning, predictive models, SHAP

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Authors: Maria Amelia V. C. Araujo, Billy J. Araujo, Brian Greenwood

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The flow over a backward-facing step is characterized by the presence of flow separation, recirculation and reattachment, for a simple geometry. This type of fluid behaviour takes place in many practical engineering applications, hence the reason for being investigated. Historically, fluid flows over a backward-facing step have been examined in many experiments using a variety of measuring techniques such as laser Doppler velocimetry (LDV), hot-wire anemometry, particle image velocimetry or hot-film sensors. However, some of these techniques cannot conveniently be used in separated flows or are too complicated and expensive. In this work, the applicability of the acoustic Doppler velocimetry (ADV) technique is investigated to such type of flows, at various Reynolds numbers corresponding to different flow regimes. The use of this measuring technique in separated flows is very difficult to find in literature. Besides, most of the situations where the Reynolds number effect is evaluated in separated flows are in numerical modelling. The ADV technique has the advantage in providing nearly non-invasive measurements, which is important in resolving turbulence. The ADV Nortek Vectrino+ was used to characterize the flow, in a recirculating laboratory flume, at various Reynolds Numbers (Reh = 3738, 5452, 7908 and 17388) based on the step height (h), in order to capture different flow regimes, and the results compared to those obtained using other measuring techniques. To compare results with other researchers, the step height, expansion ratio and the positions upstream and downstream the step were reproduced. The post-processing of the AVD records was performed using a customized numerical code, which implements several filtering techniques. Subsequently, the Vectrino noise level was evaluated by computing the power spectral density for the stream-wise horizontal velocity component. The normalized mean stream-wise velocity profiles, skin-friction coefficients and reattachment lengths were obtained for each Reh. Turbulent kinetic energy, Reynolds shear stresses and normal Reynolds stresses were determined for Reh = 7908. An uncertainty analysis was carried out, for the measured variables, using the moving block bootstrap technique. Low noise levels were obtained after implementing the post-processing techniques, showing their effectiveness. Besides, the errors obtained in the uncertainty analysis were relatively low, in general. For Reh = 7908, the normalized mean stream-wise velocity and turbulence profiles were compared directly with those acquired by other researchers using the LDV technique and a good agreement was found. The ADV technique proved to be able to characterize the flow properly over a backward-facing step, although additional caution should be taken for measurements very close to the bottom. The ADV measurements showed reliable results regarding: a) the stream-wise velocity profiles; b) the turbulent shear stress; c) the reattachment length; d) the identification of the transition from transitional to turbulent flows. Despite being a relatively inexpensive technique, acoustic Doppler velocimetry can be used with confidence in separated flows and thus very useful for numerical model validation. However, it is very important to perform adequate post-processing of the acquired data, to obtain low noise levels, thus decreasing the uncertainty.

Keywords: ADV, experimental data, multiple Reynolds number, post-processing

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Authors: Aura Alejandra Burbano Patino, Mariela Agotegaray, Veronica Lassalle, Fernanda Horst

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Water pollution is of worldwide concern due to its importance as an essential resource for life. Industrial and urbanistic growth are anthropogenic activities that have caused an increase of undesirable compounds in water. In the last decade, emerging pollutants have become of great interest since, at very low concentrations (µg/L and ng/L), they exhibit a hazardous effect on wildlife, aquatic ecosystems, and human organisms. One group of emerging pollutants that are a matter of study are pharmaceuticals. Their high consumption rate and their inappropriate disposal have led to their detection in wastewater treatment plant influent, effluent, surface water, and drinking water. In consequence, numerous technologies have been developed to efficiently treat these pollutants. Adsorption appears like an easy and cost-effective technology. One of the most used adsorbents of emerging pollutants removal is carbon-based materials such as hydrochars. This study aims to use a magnetic hydrochar nanocomposite to be employed as an adsorbent for diclofenac removal. Kinetics models and the adsorption efficiency in real water samples were analyzed. For this purpose, a magnetic hydrochar nanocomposite was synthesized through the hydrothermal carbonization (HTC) technique hybridized to co-precipitation to add the magnetic component into the hydrochar, based on iron oxide nanoparticles. The hydrochar was obtained from sunflower husk residue as the precursor. TEM, TGA, FTIR, Zeta potential as a function of pH, DLS, BET technique, and elemental analysis were employed to characterize the material in terms of composition and chemical structure. Adsorption kinetics were carried out in distilled water and real water at room temperature, pH of 5.5 for distilled water and natural pH for real water samples, 1:1 adsorbent: adsorbate dosage ratio, contact times from 10-120 minutes, and 50% dosage concentration of DCF. Results have demonstrated that magnetic hydrochar presents superparamagnetic properties with a saturation magnetization value of 55.28 emu/g. Besides, it is mesoporous with a surface area of 55.52 m²/g. It is composed of magnetite nanoparticles incorporated into the hydrochar matrix, as can be proven by TEM micrographs, FTIR spectra, and zeta potential. On the other hand, kinetic studies were carried out using DCF models, finding percent removal efficiencies up to 85.34% after 80 minutes of contact time. In addition, after 120 minutes of contact time, desorption of emerging pollutants from active sites took place, which indicated that the material got saturated after that t time. In real water samples, percent removal efficiencies decrease up to 57.39%, ascribable to a possible mechanism of competitive adsorption of organic or inorganic compounds, ions for active sites of the magnetic hydrochar. The main suggested adsorption mechanism between the magnetic hydrochar and diclofenac include hydrophobic and electrostatic interactions as well as hydrogen bonds. It can be concluded that the magnetic hydrochar nanocomposite could be valorized into a by-product which appears as an efficient adsorbent for DCF removal as a model emerging pollutant. These results are being complemented by modifying experimental variables such as pollutant’s initial concentration, adsorbent: adsorbate dosage ratio, and temperature. Currently, adsorption assays of other emerging pollutants are being been carried out.

Keywords: environmental remediation, emerging pollutants, hydrochar, magnetite nanoparticles

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Authors: Lise Paesen, Marielle Leijten

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People diagnosed with probable Alzheimer disease suffer from an impairment of their language capacities; a gradual impairment which affects both their spoken and written communication. Our study aims at characterising the language decline in DAT patients with the use of congeneric elicited writing tasks. Within these tasks, a descriptive text has to be written based upon images with which the participants are confronted. A randomised set of images allows us to present the participants with a different task on every encounter, thus allowing us to avoid a recognition effect in this iterative study. This method is a revision from previous studies, in which participants were presented with a larger picture depicting an entire scene. In order to create the randomised set of images, existing pictures were adapted following strict criteria (e.g. frequency, AoA, colour, ...). The resulting data set contained 50 images, belonging to several categories (vehicles, animals, humans, and objects). A pre-test was constructed to validate the created picture set; most images had been used before in spoken picture naming tasks. Hence the same reaction times ought to be triggered in the typed picture naming task. Once validated, the effectiveness of the descriptive tasks was assessed. First, the participants (n=60 students, n=40 healthy elderly) performed a typing task, which provided information about the typing speed of each individual. Secondly, two descriptive writing tasks were carried out, one simple and one complex. The simple task contains 4 images (1 animal, 2 objects, 1 vehicle) and only contains elements with high frequency, a young AoA (<6 years), and fast reaction times. Slow reaction times, a later AoA (≥ 6 years) and low frequency were criteria for the complex task. This task uses 6 images (2 animals, 1 human, 2 objects and 1 vehicle). The data were collected with the keystroke logging programme Inputlog. Keystroke logging tools log and time stamp keystroke activity to reconstruct and describe text production processes. The data were analysed using a selection of writing process and product variables, such as general writing process measures, detailed pause analysis, linguistic analysis, and text length. As a covariate, the intrapersonal interkey transition times from the typing task were taken into account. The pre-test indicated that the new images lead to similar or even faster reaction times compared to the original images. All the images were therefore used in the main study. The produced texts of the description tasks were significantly longer compared to previous studies, providing sufficient text and process data for analyses. Preliminary analysis shows that the amount of words produced differed significantly between the healthy elderly and the students, as did the mean length of production bursts, even though both groups needed the same time to produce their texts. However, the elderly took significantly more time to produce the complex task than the simple task. Nevertheless, the amount of words per minute remained comparable between simple and complex. The pauses within and before words varied, even when taking personal typing abilities (obtained by the typing task) into account.

Keywords: Alzheimer's disease, experimental design, language decline, writing process

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Authors: Lynn Sheridan, Dennis Alonzo, Hoa Nguyen, Andy Gao, Tracy Durksen

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Disruptions to teaching (e.g., COVID-related) have increased work demands for teachers. There is an opportunity for research to explore evidence-informed steps to support teachers. Collective evidence informs data on teachers’ personal attributes (e.g., self-efficacy beliefs) in the workplace are seen to promote success in teaching and support teacher engagement. Teacher engagement plays a role in students’ learning and teachers’ effectiveness. Engaged teachers are better at overcoming work-related stress, burnout and are more likely to take on active roles. Teachers’ commitment is influenced by a host of personal (e.g., teacher well-being) and environmental factors (e.g., job stresses). The job demands-resources model provided a conceptual basis for examining how teachers’ well-being, and is influenced by job demands and job resources. Job demands potentially evoke strain and exceed the employee’s capability to adapt. Job resources entail what the job offers to individual teachers (e.g., organisational support), helping to reduce job demands. The application of the job demands-resources model involves gathering an evidence-base of and connection to personal attributes (job resources). The study explored the association between constructs (resilience, adaptability, commitment, self/collective efficacy) and a teacher’s engagement with the job. The paper sought to elaborate on the model and determine the associations between key constructs of well-being (resilience, adaptability), commitment, and motivation (self and collective-efficacy beliefs) to teachers’ engagement in teaching. Data collection involved online a multi-dimensional instrument using validated items distributed from 2020-2022. The instrument was designed to identify construct relationships. The participant number was 170. Data Analysis: The reliability coefficients, means, standard deviations, skewness, and kurtosis statistics for the six variables were completed. All scales have good reliability coefficients (.72-.96). A confirmatory factor analysis (CFA) and structural equation model (SEM) were performed to provide measurement support and to obtain latent correlations among factors. The final analysis was performed using structural equation modelling. Several fit indices were used to evaluate the model fit, including chi-square statistics and root mean square error of approximation. The CFA and SEM analysis was performed. The correlations of constructs indicated positive correlations exist, with the highest found between teacher engagement and resilience (r=.80) and the lowest between teacher adaptability and collective teacher efficacy (r=.22). Given the associations; we proceeded with CFA. The CFA yielded adequate fit: CFA fit: X (270, 1019) = 1836.79, p < .001, RMSEA = .04, and CFI = .94, TLI = .93 and SRMR = .04. All values were within the threshold values, indicating a good model fit. Results indicate that increasing teacher self-efficacy beliefs will increase a teacher’s level of engagement; that teacher ‘adaptability and resilience are positively associated with self-efficacy beliefs, as are collective teacher efficacy beliefs. Implications for school leaders and school systems: 1. investing in increasing teachers’ sense of efficacy beliefs to manage work demands; 2. leadership approaches can enhance teachers' adaptability and resilience; and 3. a culture of collective efficacy support. Preparing teachers for now and in the future offers an important reminder to policymakers and school leaders on the importance of supporting teachers’ personal attributes when faced with the challenging demands of the job.

Keywords: collective teacher efficacy, teacher self-efficacy, job demands, teacher engagement

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Authors: Supriyo Das, Elbir Jove, Ajay Singh, Sophie Corbet, Noel Carr, Martin Deetz

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Water is a critical commodity in the healthcare and medical field. The utility of medical-grade water spans from washing surgical equipment, drug preparation to the key element of life-saving therapy such as hydrotherapy and hemodialysis for patients. A properly treated medical water reduces the bioburden load and mitigates the risk of infection, ensuring patient safety. However, any compromised condition during the production of medical-grade water can create a favorable environment for microbial growth putting patient safety at high risk. Therefore, proper upstream treatment of the medical water is essential before its application in healthcare, pharma and medical space. Reverse Osmosis (RO) is one of the most preferred treatments within healthcare industries and is recommended by all International Pharmacopeias to achieve the quality level demanded by global regulatory bodies. The RO process can remove up to 99.5% of constituents from feed water sources, eliminating bacteria, proteins and particles sizes of 100 Dalton and above. The combination of RO with other downstream water treatment technologies such as Electrodeionization and Ultrafiltration meet the quality requirements of various pharmacopeia monographs to produce highly purified water or water for injection for medical use. In the reverse osmosis process, the water from a liquid with a high concentration of dissolved solids is forced to flow through an especially engineered semi-permeable membrane to the low concentration side, resulting in high-quality grade water. However, these specially engineered RO membranes need to be sanitized either chemically or at high temperatures at regular intervals to keep the bio-burden at the minimum required level. In this paper, we talk about Dupont´s FilmTec Heat Sanitizable Reverse Osmosis membrane (HSRO) for the production of medical-grade water. An HSRO element must be pre-conditioned prior to initial use by exposure to hot water (80°C-85°C) for its stable performance and to meet the manufacturer’s specifications. Without pre-conditioning, the membrane will show variations in feed pressure operations and salt rejection. The paper will discuss the critical variables of pre-conditioning steps that can affect the overall performance of the HSRO membrane and demonstrate the data to support the need for pre-conditioning of HSRO elements. Our preliminary data suggests that there can be up to 35 % reduction in flow due to initial heat treatment, which also positively affects the increase in salt rejection. The paper will go into detail about the fundamental understanding of the performance change of HSRO after the pre-conditioning step and its effect on the quality of medical water produced. The paper will also discuss another critical point, “regular hot water sanitization” of these HSRO membranes. Regular hot water sanitization (at 80°C-85°C) is necessary to keep the membrane bioburden free; however, it can negatively impact the performance of the membrane over time. We will demonstrate several data points on hot water sanitization using FilmTec HSRO elements and challenge its robustness to produce quality medical water. The last part of this paper will discuss the construction details of the FilmTec HSRO membrane and features that make it suitable to pre-condition and sanitize at high temperatures.

Keywords: heat sanitizable reverse osmosis, HSRO, medical water, hemodialysis water, water for Injection, pre-conditioning, heat sanitization

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Authors: Syed Jamil H. Kazmi, Faiza Sarwar

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The Indus delta of Sindh Province forms the largest creeks zone of Pakistan. The Sindh coast starts from the mouth of Hab River and terminates at Sir Creek area. In this paper, we have considered the major creeks from the site of Bin Qasim Port in Karachi to Jetty of Keti Bunder in Thatta District. A general decline in the mangrove forest has been observed that within a span of last 25 years. The unprecedented human interventions damage the creeks habitat badly which includes haphazard urban development, industrial and sewage disposal, illegal cutting of mangroves forest, reduced and inconsistent fresh water flow mainly from Jhang and Indus rivers. These activities not only harm the creeks habitat but affected the fish stock substantially. Fishing is the main livelihood of coastal people but with the above-mentioned threats, it is also under enormous pressure by fish catches resulted in unchecked overutilization of the fish resources. This pressure is almost unbearable when it joins with deleterious fishing methods, uncontrolled fleet size, increase trash and by-catch of juvenile and illegal mesh size. Along with these anthropogenic interventions study area is under the red zone of tropical cyclones and active seismicity causing floods, sea intrusion, damage mangroves forests and devastation of fish stock. In order to sustain the natural resources of the Indus Creeks, this study was initiated with the support of FAO, WWF and NIO, the main purpose was to develop a Geo-Spatial dataset for fish stock assessment. The study has been spread over a year (2013-14) on monthly basis which mainly includes detailed fish stock survey, water analysis and few other environmental analyses. Environmental analysis also includes the habitat classification of study area which has done through remote sensing techniques for 22 years’ time series (1992-2014). Furthermore, out of 252 species collected, fifteen species from estuarine and marine groups were short-listed to measure the weight, health and growth of fish species at each creek under GIS data through SPSS system. Furthermore, habitat suitability analysis has been conducted by assessing the surface topographic and aspect derivation through different GIS techniques. The output variables then overlaid in GIS system to measure the creeks productivity. Which provided the results in terms of subsequent classes: extremely productive, highly productive, productive, moderately productive and less productive. This study has revealed the Geospatial tools utilization along with the evaluation of the fisheries resources and creeks habitat risk zone mapping. It has also been identified that the geo-spatial technologies are highly beneficial to identify the areas of high environmental risk in Sindh Creeks. This has been clearly discovered from this study that creeks with high rugosity are more productive than the creeks with low levels of rugosity. The study area has the immense potential to boost the economy of Pakistan in terms of fish export, if geo-spatial techniques are implemented instead of conventional techniques.

Keywords: fish stock, geo-spatial, productivity analysis, risk

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Authors: Juergen Junk, Franz Ronellenfitsch, Michael Eickermann

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An appropriate weed management in autumn is a prerequisite for an economically successful harvest in the following year. In Luxembourg oilseed rape, wheat and barley is sown from August until October, accompanied by a chemical weed control with soil active herbicides, depending on the state of the weeds and the meteorological conditions. Based on regular ground and surface water-analysis, high levels of contamination by transformation products of respective herbicide compounds have been found in Luxembourg. The most ideal conditions for incorporating soil active herbicides are single rain events. Weed control may be reduced if application is made when weeds are under drought stress or if repeated light rain events followed by dry spells, because the herbicides tend to bind tightly to the soil particles. These effects have been frequently reported for Luxembourg throughout the last years. In the framework of a multisite long-term field experiment (EFFO) weed monitoring, plants observations and corresponding meteorological measurements were conducted. Long-term time series (1947-2016) from the SYNOP station Findel-Airport (WMO ID = 06590) showed a decrease in the number of days with precipitation. As the total precipitation amount has not significantly changed, this indicates a trend towards rain events with higher intensity. All analyses are based on decades (10-day periods) for September and October of each individual year. To assess the future meteorological conditions for Luxembourg, two different approaches were applied. First, multi-model ensembles from the CORDEX experiments (spatial resolution ~12.5 km; transient projections until 2100) were analysed for two different Representative Concentration Pathways (RCP8.5 and RCP4.5), covering the time span from 2005 until 2100. The multi-model ensemble approach allows for the quantification of the uncertainties and also to assess the differences between the two emission scenarios. Second, to assess smaller scale differences within the country a high resolution model projection using the COSMO-LM model was used (spatial resolution 1.3 km). To account for the higher computational demands, caused by the increased spatial resolution, only 10-year time slices have been simulated (reference period 1991-2000; near future 2041-2050 and far future 2091-2100). Statistically significant trends towards higher air temperatures, +1.6 K for September (+5.3 K far future) and +1.3 K for October (+4.3 K), were predicted for the near future compared to the reference period. Precipitation simultaneously decreased by 9.4 mm (September) and 5.0 mm (October) for the near future and -49 mm (September) and -10 mm (October) in the far future. Beside the monthly values also decades were analyzed for the two future time periods of the CLM model. For all decades of September and October the number of days with precipitation decreased for the projected near and far future. Changes in meteorological variables such as air temperature and precipitation did already induce transformations in weed societies (composition, late-emerging etc.) of arable ecosystems in Europe. Therefore, adaptations of agronomic practices as well as effective weed control strategies must be developed to maintain crop yield.

Keywords: CORDEX projections, dry spells, ensembles, weed management

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Authors: Awatef Ahmed Ben Ramadan, Aqsa Arshad

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Background: There is a huge Muslim migration movement to North America and Europe for several reasons, primarily refuge from war areas and partly to search for better work and educational chances. There are always concerns regarding first-Generation Immigrant women's health and computer literacy, an adequate understanding of the health systems, and the use of the existing healthcare technology and services effectively and efficiently. Language proficiency level, preference for cultural and traditional remedies, socioeconomic factors, fear of stereotyping, limited accessibility to health services, and general unfamiliarity with the existing health services and resources are familiar variables among these women. Aims: The current study aims to assess the health and digital literacy of first-generation Muslim women in Atlanta city. Also, the study aims to examine how the COVID-19 pandemic has encouraged the use of health information technology and increased technology awareness among the targeted women. Methods: The study design is cross-sectional correlational research. The study will be conducted to produce preliminary results that the investigators want to have to supplement an NIH grant application about leveraging information technology to reduce the health inequalities amongst the first-generation immigrant Muslim women in Atlanta City. The investigators will collect the study data in two phases using different tools. Phase one was conducted in June 2022; the investigators used tools to measure health and digital literacy amongst 42 first-generation immigrant Muslim women. Phase two was conducted in November 2022; the investigators measured the Knowledge, Attitude, and Practice (KAP) of using health information technology such as telehealth from a sample of 45 first-generation Muslim immigrant women in Atlanta; in addition, the investigators measured how the current pandemic has affected their KAP to use telemedicine and telehealth services. Both phases' study participants were recruited using convenience sampling methodology. The investigators collected around 40 of 18 years old or older first-generation Muslim immigrant women for both study phases. The study excluded Immigrants who hold work visas and second-generation immigrants. Results: At the point of submitting this abstract, the investigators are still analyzing the study data to produce preliminary results to apply for an NIH grant entitled "Leveraging Health Information Technology (Health IT) to Address and Reduce Health Care Disparities (R01 Clinical Trial Optional)". This research will be the first step of a comprehensive research project to assess and measure health and digital literacy amongst a vulnerable community group. The targeted group might have different points of view from the U.S.-born inhabitants on how to: promote their health, gain healthy lifestyles and habits, screen for diseases, adhere to health treatment and follow-up plans, perceive the importance of using available and affordable technology to communicate with their providers and improve their health, and help in making serious decisions for their health. The investigators aim to develop an educational and instructional health mobile application considering the language and cultural factors that affect immigrants' ability to access different health and social support sources, know their health rights and obligations in their communities, and improve their health behavior and behavior lifestyles.

Keywords: first-generation immigrant Muslim women, telehealth, COVID-19 pandemic, health information technology, health and digital literacy

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Authors: Crysovaladou Kefaloudi, Kassiani Mellou, Eirini Saranti-Papasaranti, Athanasios Koustenis, Chrysoula Botsi, Agapios Terzidis

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Recent migration crisis led to a vast migrant – refugees movement to Greece which created an urgent need for hosting settlements. Taken into account the protection of public health from possible pathogens related to water and food supply as well as waste and sewage accumulation, a 'Living Conditions Recording Form' was created in the context of 'PHILOS' European Program funded by the Asylum Migration and Integration Fund (AMIF) of EU’s DG Migration and Home Affairs, in order to assess a number of environmental sanitation parameters, in refugees – migrants camps in mainland. The assessment will be completed until the end of July. From March to June 2017, mobile unit teams comprised of health inspectors of sub-action 2 of “PHILOS” proceeded with the assessment of living conditions in twenty-two out of thirty-one camps and 'Stata' was used for the statistical analysis of obtained information. Variables were grouped into the following categories: 1) Camp administration, 2) hosted population number, 3) accommodation, 4) heating installations, 5) personal hygiene, 6) sewage collection and disposal, 7) water supply, 8) waste collection and management, 9) pest control, 10) fire safety, 11) food handling and safety. Preliminary analysis of the results showed that camp administration was performed in 90% of the camps by a public authority with the coordination of various NGOs. The median number of hosted population was 222 ranging from 62 to 3200, and the median value of hosted population per accommodation type was 4 in 19 camps. Heating facilities were provided in 86.1% of camps. In 18.2 % of the camps, one personal hygiene facility was available per 6 people ranging in the rest of the camps from 1 per 3 to 1 per 20 hosted refugees-migrants. Waste and sewage collection was performed depending on populations demand in an adequate way in all recorded camps. In 90% of camps, water was supplied through the central water supply system. In 85% of camps quantity and quality of water supply inside camps was regularly monitored for microbial and chemical indices. Pest control was implemented in 86.4% of the camps as well as fire safety measures. Food was supplied by catering companies in 50% of the camps, and the quality and quantity food was monitored at a regular basis. In 77% of camps, food was prepared by the hosted population with the availability of proper storage conditions. Furthermore, in all camps, hosted population was provided with personal hygiene items and health sanitary educational programs were implemented in 77.3% of camps. In conclusion, in the majority of the camps, environmental sanitation parameters were satisfactory. However, waste and sewage accumulation, as well as inadequate pest control measures were recorded in some camps. The obtained data have led to a number of recommendations for the improvement of sanitary conditions, disseminated to all relevant stakeholders. Special emphasis was given to hygiene measures implementation during food handling by migrants – refugees, as well as to waste and sewage accumulation taking in to account the population’s cultural background.

Keywords: environmental sanitation parameters, food borne diseases risk assessment, refugee – migrants camps, water borne diseases risk assessment

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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Controlling blood pressure is critical to reducing the risk of cardiovascular disease. However, BP control rates (systolic BP < 140 mm Hg and diastolic BP < 90 mm Hg) have declined since 2013, warranting further analysis to identify contributing factors and potential interventions. This study investigates the factors associated with the decline in blood pressure (BP) control among U.S. adults with hypertension over the past decade. Data from the U.S. National Health and Nutrition Examination Survey (NHANES) were used to assess BP control trends between 2013 and 2020. The analysis included 18,927 U.S. adults with hypertension aged 18 years and older who completed study interviews and examinations. The dataset, obtained from the cardioStatsUSA and RNHANES R packages, was merged based on survey IDs. Key variables analyzed included demographic factors, lifestyle behaviors, hypertension status, BMI, comorbidities, antihypertensive medication use, and cardiovascular disease history. The prevalence of BP control declined from 78.0% in 2013-2014 to 71.6% in 2017-2020. Non-Hispanic Whites had the highest BP control prevalence (33.6% in 2013-2014), but this declined to 26.5% by 2017-2020. In contrast, BP control among Non-Hispanic Blacks increased slightly. Younger adults (aged 18-44) exhibited better BP control, but control rates declined over time. Obesity prevalence increased, contributing to poorer BP control. Antihypertensive medication use rose from 26.1% to 29.2% across the study period. Lifestyle behaviors, such as smoking and diet, also affected BP control, with nonsmokers and those with better diets showing higher control rates. Key findings indicate significant disparities in blood pressure control across racial/ethnic groups. Non-Hispanic Black participants had consistently higher odds (OR ranging from 1.84 to 2.33) of poor blood pressure control compared to Non-Hispanic Whites, while odds among Non-Hispanic Asians varied by cycle. Younger age groups (18-44 and 45-64) showed significantly lower odds of poor blood pressure control compared to those aged 75+, highlighting better control in younger populations. Men had consistently higher odds of poor control compared to women, though this disparity slightly decreased in 2017-2020. Medical comorbidities such as diabetes and chronic kidney disease were associated with significantly higher odds of poor blood pressure control across all cycles. Participants with chronic kidney disease had particularly elevated odds (OR=5.54 in 2015-2016), underscoring the challenge of managing hypertension in these populations. Antihypertensive medication use was also linked with higher odds of poor control, suggesting potential difficulties in achieving target blood pressure despite treatment. Lifestyle factors such as alcohol consumption and physical activity showed no consistent association with blood pressure control. However, dietary quality appeared protective, with those reporting an excellent diet showing lower odds (OR=0.64) of poor control in the overall sample. Increased BMI was associated with higher odds of poor blood pressure control, particularly in the 30-35 and 35+ BMI categories during 2015-2016. The study highlights a significant decline in BP control among U.S. adults with hypertension, particularly among certain demographic groups and those with increasing obesity rates. Lifestyle behaviors, antihypertensive medication use, and socioeconomic factors all played a role in these trends.

Keywords: diabetes, blood pressure, obesity, logistic regression, odd ratio

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Authors: Maria Lazari, Lorenzo Sanavia

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Mechanics of unsaturated soils has been an active field of research in the last decades. Efficient constitutive models that take into account the partial saturation of soil are necessary to solve a number of engineering problems e.g. instability of slopes and cuts due to heavy rainfalls. A large number of constitutive models can now be found in the literature that considers fundamental issues associated with the unsaturated soil behaviour, like the volume change and shear strength behaviour with suction or saturation changes. Partially saturated soils may either expand or collapse upon wetting depending on the stress level, and it is also possible that a soil might experience a reversal in the volumetric behaviour during wetting. Shear strength of soils also changes dramatically with changes in the degree of saturation, and a related engineering problem is slope failures caused by rainfall. There are several states of the art reviews over the last years for studying the topic, usually providing a thorough discussion of the stress state, the advantages, and disadvantages of specific constitutive models as well as the latest developments in the area of unsaturated soil modelling. However, only a few studies focused on the coupling between partial saturation states and time effects on the behaviour of geomaterials. Rate dependency is experimentally observed in the mechanical response of granular materials, and a viscoplastic constitutive model is capable of reproducing creep and relaxation processes. Therefore, in this work an elasto-viscoplastic constitutive model for unsaturated soils is proposed and validated on the basis of experimental data. The model constitutes an extension of an existing elastoplastic strain-hardening constitutive model capable of capturing the behaviour of variably saturated soils, based on energy conjugated stress variables in the framework of superposed continua. The purpose was to develop a model able to deal with possible mechanical instabilities within a consistent energy framework. The model shares the same conceptual structure of the elastoplastic laws proposed to deal with bonded geomaterials subject to weathering or diagenesis and is capable of modelling several kinds of instabilities induced by the loss of hydraulic bonding contributions. The novelty of the proposed formulation is enhanced with the incorporation of density dependent stiffness and hardening coefficients in order to allow the modeling of the pycnotropy behaviour of granular materials with a single set of material constants. The model has been implemented in the commercial FE platform PLAXIS, widely used in Europe for advanced geotechnical design. The algorithmic strategies adopted for the stress-point algorithm had to be revised to take into account the different approach adopted by PLAXIS developers in the solution of the discrete non-linear equilibrium equations. An extensive comparison between models with a series of experimental data reported by different authors is presented to validate the model and illustrate the capability of the newly developed model. After the validation, the effectiveness of the viscoplastic model is displayed by numerical simulations of a partially saturated slope failure of the laboratory scale and the effect of viscosity and degree of saturation on slope’s stability is discussed.

Keywords: PLAXIS software, slope, unsaturated soils, Viscoplasticity

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Authors: Ho-Won Jung, Donghun Lee, Hyung-Jin Kim

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Predictive analytics (data analysis) uses a subset of measurements (the features, predictor, or independent variable) to predict another measurement (the outcome, target, or dependent variable) on a single person or unit. It applies empirical methods in statistics, operations research, and machine learning to predict the future, or otherwise unknown events or outcome on a single or person or unit, based on patterns in data. Most analyses of metabolic syndrome are not predictive analytics but statistical explanatory studies that build a proposed model (theory building) and then validate metabolic syndrome predictors hypothesized (theory testing). A proposed theoretical model forms with causal hypotheses that specify how and why certain empirical phenomena occur. Predictive analytics and explanatory modeling have their own territories in analysis. However, predictive analytics can perform vital roles in explanatory studies, i.e., scientific activities such as theory building, theory testing, and relevance assessment. In the context, this study is to demonstrate how to use our predictive analytics to support theory building (i.e., hypothesis generation). For the purpose, this study utilized a big data predictive analytics platform TM based on a co-occurrence graph. The co-occurrence graph is depicted with nodes (e.g., items in a basket) and arcs (direct connections between two nodes), where items in a basket are fully connected. A cluster is a collection of fully connected items, where the specific group of items has co-occurred in several rows in a data set. Clusters can be ranked using importance metrics, such as node size (number of items), frequency, surprise (observed frequency vs. expected), among others. The size of a graph can be represented by the numbers of nodes and arcs. Since the size of a co-occurrence graph does not depend directly on the number of observations (transactions), huge amounts of transactions can be represented and processed efficiently. For a demonstration, a total of 13,254 metabolic syndrome training data is plugged into the analytics platform to generate rules (potential hypotheses). Each observation includes 31 predictors, for example, associated with sociodemographic, habits, and activities. Some are intentionally included to get predictive analytics insights on variable selection such as cancer examination, house type, and vaccination. The platform automatically generates plausible hypotheses (rules) without statistical modeling. Then the rules are validated with an external testing dataset including 4,090 observations. Results as a kind of inductive reasoning show potential hypotheses extracted as a set of association rules. Most statistical models generate just one estimated equation. On the other hand, a set of rules (many estimated equations from a statistical perspective) in this study may imply heterogeneity in a population (i.e., different subpopulations with unique features are aggregated). Next step of theory development, i.e., theory testing, statistically tests whether a proposed theoretical model is a plausible explanation of a phenomenon interested in. If hypotheses generated are tested statistically with several thousand observations, most of the variables will become significant as the p-values approach zero. Thus, theory validation needs statistical methods utilizing a part of observations such as bootstrap resampling with an appropriate sample size.

Keywords: explanatory modeling, metabolic syndrome, predictive analytics, theory building

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Authors: Rasha Elsawy

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Aside from the rapid growth of global information technology and the Internet, another key trend is the swift proliferation of wearable technologies. The future of wearable technologies is very bright as an emerging revolution in this technological world. Beyond this, individual continuance intention toward IT is an important area that drew academics' and practitioners' attention. The literature review exhibits that continuance usage is an important concern that needs to be addressed for any technology to be advantageous and for consumers to succeed. However, consumers noticeably abandon their wearable devices soon after purchase, losing all subsequent benefits that can only be achieved through continued usage. Purpose-This thesis aims to develop an integrated model designed to explain and predict consumers' behavioural intention(BI) and continued use (CU) of wearable fitness technology (WFT) to identify the determinants of the CU of technology. Because of this, the question arises as to whether there are differences between technology adoption and post-adoption (CU) factors. Design/methodology/approach- The study employs the unified theory of acceptance and use of technology2 (UTAUT2), which has the best explanatory power, as an underpinning framework—extending it with further factors, along with user-specific personal characteristics as moderators. All items will be adapted from previous literature and slightly modified according to the WFT/SW context. A longitudinal investigation will be carried out to examine the research model, wherein a survey will include these constructs involved in the conceptual model. A quantitative approach based on a questionnaire survey will collect data from existing wearable technology users. Data will be analysed using the structural equation modelling (SEM) method based on IBM SPSS statistics and AMOS 28.0. Findings- The research findings will provide unique perspectives on user behaviour, intention, and actual continuance usage when accepting WFT. Originality/value- Unlike previous works, the current thesis comprehensively explores factors that affect consumers' decisions to continue using wearable technology. That is influenced by technological/utilitarian, affective, emotional, psychological, and social factors, along with the role of proposed moderators. That novel research framework is proposed by extending the UTAUT2 model with additional contextual variables classified into Performance Expectancy, Effort Expectancy, Social Influence (societal pressure regarding body image), Facilitating Conditions, Hedonic Motivation (to be split up into two concepts: perceived enjoyment and perceived device annoyance), Price value, and Habit-forming techniques; adding technology upgradability as determinants of consumers' behavioural intention and continuance usage of Information Technology (IT). Further, using personality traits theory and proposing relevant user-specific personal characteristics (openness to technological innovativeness, conscientiousness in health, extraversion, neuroticism, and agreeableness) to moderate the research model. Thus, the present thesis obtains a more convincing explanation expected to provide theoretical foundations for future emerging IT (such as wearable fitness devices) research from a behavioural perspective.

Keywords: wearable technology, wearable fitness devices/smartwatches, continuance use, behavioural intention, upgradability, longitudinal study

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Walid A. Alkeridy, Arwa Aljasser, Khalid Mohammed Alayed, Saad Alsaad, Amani S. Alqahtani, Claire Ann Lim, Sultan H. Alamri, Doaa Zainhom Mekkawy, Mohammed Al-Sofiani

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Introduction: The history of publicly funded Home Health Care (HHC) service in Saudi Arabia dates back to 1991. The first HC program was launched to provide palliative home care services for patients with terminal cancer. Thereafter, more programs launched across Saudi Arabia most remarkably was launching the national program for HHC by the Ministry Of Health (MOH) in 2008. The national HHC MOH program is mainly providing long-term care home care services for over 40,000 Saudi citizens. The scope of the HHC service program provided by the Saudi MOH is quite diverse, ranging from basic nursing care to specialized care programs, e.g., home peritoneal dialysis, home ventilation, home infusion therapy, etc. Objectives: The primary aim of our study is to report the prevalence of chronic conditions among Saudi people receiving long-term HHC services. Secondary aims include identifying the predictors of mortality among individuals receiving long-term HHC services and studying the association between frailty and poor health outcomes among HHC users. Methods: We conducted a retrospective and cross-sectional data collection from participants receiving HHC services at King Saud University Medical City, Riyadh, Saudi Arabia. Data were collected from electronic health records (EHR), patient charts, and interviewing caregivers from the year 2019 to 2022. We assessed functional performance by Katz's activity of daily living and the Bristol Activity of Daily Living Scale (BADLS). A trained health care provider assessed frailty using the Clinical Frailty Scale (CFS). Mortality was assessed by reviewing the death certificates if patients were hospitalized through discharge status ascertainment from EHR. Results: The mean age for deceased individuals in HHC was 78.3 years. Over twenty percent of individuals receiving HHC services were readmitted to the hospital. The following variables were statistically significant between deceased and alive individuals receiving HHC services; clinical frailty scale, the total number of comorbid conditions, and functional performance based on the KATZ activity of daily living scale and the BADLS. We found that the strongest predictors for mortality were pressure ulcers which had an odds ratio of 3.75 and p-value of < 0.0001, and the clinical frailty scale, which had an odds ratio of 1.69 and p-value of 0.002, using multivariate regression analysis. In conclusion, our study found that pressure ulcers and frailty are the strongest predictors of mortality for individuals receiving home health care services. Moreover, we found a high rate of annual readmission for individuals enrolled in HHC, which requires further analysis to understand the possible contributing factors for the increased rate of hospital readmission and develop strategies to address them. Future studies should focus on designing quality improvement projects aimed at improving the quality of life for individuals receiving HHC services, especially those who have pressure ulcers at the end of life.

Keywords: homecare, Saudi, prevalence, chronic

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Authors: Jawaad Saleem, Hannah Wright, Peter Sommerville, Adrian Hopper

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Introduction: Inpatient falls are the most commonly reported patient safety incidents, and carry a significant burden on resources, morbidity, and mortality. Ensuring adequate post falls management of patients by staff is therefore paramount to maintaining patient safety especially in out of hours and resource stretched settings. Aims: This quality improvement project aims to improve the current practice of falls management at Guys St Thomas Hospital, London as compared to our 2016 Quality Improvement Project findings. Furthermore, it looks to increase current junior doctors confidence in managing falls and their use of new guidance protocols. Methods: Multifaceted Interventions implemented included: the development of new trust wide guidelines detailing management pathways for patients post falls, available for intranet access. Furthermore, the production of 2000 lanyard cards distributed amongst junior doctors and staff which summarised these guidelines. Additionally, a ‘safety signal’ email was sent from the Trust chief medical officer to all staff raising awareness of falls and the guidelines. Formal falls teaching was also implemented for new doctors at induction. Using an established incident database, 189 consecutive falls in 2017were retrospectively analysed electronically to assess and compared to the variables measured in 2016 post interventions. A separate serious incident database was used to analyse 50 falls from May 2015 to March 2018 to ascertain the statistical significance of the impact of our interventions on serious incidents. A similar questionnaire for the 2017 cohort of foundation year one (FY1) doctors was performed and compared to 2016 results. Results: Questionnaire data demonstrated improved awareness and utility of guidelines and increased confidence as well as an increase in training. 97% of FY1 trainees felt that the interventions had increased their awareness of the impact of falls on patients in the trust. Data from the incident database demonstrated the time to review patients post fall had decreased from an average of 130 to 86 minutes. Improvement was also demonstrated in the reduced time to order and schedule X-ray and CT imaging, 3 and 5 hours respectively. Data from the serious incident database show that ‘the time from fall until harm was detected’ was statistically significantly lower (P = 0.044) post intervention. We also showed the incidence of significant delays in detecting harm ( > 10 hours) reduced post intervention. Conclusions: Our interventions have helped to significantly reduce the average time to assess, order and schedule appropriate imaging post falls. Delays of over ten hours to detect serious injuries after falls were commonplace; since the intervention, their frequency has markedly reduced. We suggest this will lead to identifying patient harm sooner, reduced clinical incidents relating to falls and thus improve overall patient safety. Our interventions have also helped increase clinical staff confidence, management, and awareness of falls in the trust. Next steps include expanding teaching sessions, improving multidisciplinary team involvement to aid this improvement.

Keywords: patient safety, quality improvement, serious incidents, falls, clinical care

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Authors: Arzyana Sunkar, Yanto Santosa, Siti Badriyah Rushayati

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Indonesia has experienced annual forest fires that have rapidly destroyed and degraded its forests. Fires in the peat swamp forests of Riau Province, have set the stage for problems to worsen, this being the ecosystem most prone to fires (which are also the most difficult, to extinguish). Despite various efforts to curb deforestation, and forest degradation processes, severe forest fires are still occurring. To find an effective solution, the basic causes of the problems must be identified. It is therefore critical to have an in-depth understanding of the underlying causal factors that have contributed to deforestation and forest degradation as a whole, in order to attain reductions in their rates. An assessment of the drivers of deforestation and forest degradation was carried out, in order to design and implement measures that could slow these destructive processes. Research was conducted in Giam Siak Kecil–Bukit Batu Biosphere Reserve (GSKBB BR), in the Riau Province of Sumatera, Indonesia. A biosphere reserve was selected as the study site because such reserves aim to reconcile conservation with sustainable development. A biosphere reserve should promote a range of local human activities, together with development values that are in line spatially and economically with the area conservation values, through use of a zoning system. Moreover, GSKBB BR is an area with vast peatlands, and is experiencing forest fires annually. Various factors were analysed to assess the drivers of deforestation and forest degradation in GSKBB BR; data were collected from focus group discussions with stakeholders, key informant interviews with key stakeholders, field observation and a literature review. Landsat satellite imagery was used to map forest-cover changes for various periods. Analysis of landsat images, taken during the period 2010-2014, revealed that within the non-protected area of core zone, there was a trend towards decreasing peat swamp forest areas, increasing land clearance, and increasing areas of community oil-palm and rubber plantations. Fire was used for land clearing and most of the forest fires occurred in the most populous area (the transition area). The study found a relationship between the deforested/ degraded areas, and certain distance variables, i.e. distance from roads, villages and the borders between the core area and the buffer zone. The further the distance from the core area of the reserve, the higher was the degree of deforestation and forest degradation. Research findings suggested that agricultural expansion may be the direct cause of deforestation and forest degradation in the reserve, whereas socio-economic factors were the underlying driver of forest cover changes; such factors consisting of a combination of socio-cultural, infrastructural, technological, institutional (policy and governance), demographic (population pressure) and economic (market demand) considerations. These findings indicated that local factors/problems were the critical causes of deforestation and degradation in GSKBB BR. This research therefore concluded that reductions in deforestation and forest degradation in GSKBB BR could be achieved through ‘local actor’-tailored approaches such as community empowerment

Keywords: Actor-led solution, community empowerment, drivers of deforestation and forest degradation, Giam Siak Kecil – Bukit Batu Biosphere Reserve

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Authors: Terryann Clark, Kabir Dasgupta, Sonia Lewycka, Gail Pacheco, Alexander Plum

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This paper focused on two main research aims using data from the Growing Up in New Zealand (GUINZ) birth cohort: 1. To examine ethnic differences in life-course trajectories in the use and experience of healthcare services in early childhood years (namely immunisation, dental checks and use of General Practitioners (GPs)) 2. To quantify the contribution of relevant explanatory factors to ethnic differences. Current policy in New Zealand indicates there should be, in terms of associated direct costs, equitable access by ethnicity for healthcare services. However, empirical evidence points to persistent ethnic gaps in several domains. For example, the data highlighted that Māori have the lowest immunisation rates, across a number of time points in early childhood – despite having a higher antenatal intention to immunise relative to NZ European. Further to that, NZ European are much more likely to have their first-choice lead maternity caregiver (LMC) and use child dental services compared to all ethnicities. Method: This research explored the underlying mechanisms behind ethnic differences in the use and experience of child healthcare services. First, a multivariate regression analysis was used to adjust raw ethnic gaps in child health care utilisation by relevant covariates. This included a range of factors, encompassing mobility, socio-economic status, mother and child characteristics, household characteristics and other social aspects. Second, a decomposition analysis was used to assess the proportion of each ethnic gap that can be explained, as well as the main drivers behind the explained component. The analysis for both econometric approaches was repeated for each data time point available, which included antenatal, 9 months, 2 years and 4 years post-birth. Results: The following findings emerged: There is consistent evidence that Asian and Pacific peoples have a higher likelihood of child immunisation relative to NZ Europeans and Māori. This was evident at all time points except one. Pacific peoples had a lower rate relative to NZ European for receiving all first-year immunisations on time. For a number of potential individual and household predictors of healthcare service utilisation, the association is time-variant across early childhood. For example, socio-economic status appears highly relevant for timely immunisations in a child’s first year, but is then insignificant for the 15 month immunisations and those at age 4. Social factors play a key role. This included discouragement or encouragement regarding child immunisation. When broken down by source, discouragement by family has the largest marginal effect, followed by health professionals; whereas for encouragement, medical professionals have the largest positive influence. Perceived ethnically motivated discrimination by a health professional was significant with respect to both reducing the likelihood of achieving first choice LMC, and also satisfaction levels with child’s GP. Some ethnic gaps were largely unexplained, despite the wealth of factors employed as independent variables in our analysis. This included understanding why Pacific mothers are much less likely to achieve their first choice LMC compared to NZ Europeans; and also the ethnic gaps for both Māori and Pacific peoples relative to NZ Europeans concerning dental service use.

Keywords: child health, cohort analysis, ethnic disparities, primary healthcare

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Authors: M. Skibinska, A. Rajewska-Rager, M. Dmitrzak-Weglarz, N. Lepczynska, P. Sibilski, P. Kapelski, J. Pawlak, J. Twarowska-Hauser

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Introduction: Neurotrophic factors have been implicated in neuropsychiatric disorders. Brain-Derived Neurotrophic Factor (BDNF) influences neuron differentiation in development as well as synaptic plasticity and neuron survival in adulthood. BDNF is widely studied in mood disorders and has been proposed as a biomarker for depression. BDNF is synthesized as precursor protein – proBDNF. Both forms are biologically active and exert opposite effects on neurons. Aim: The aim of the study was to examine the serum levels of BDNF and proBDNF in unipolar and bipolar young patients below 24 years old during hypo/manic, depressive episodes and in remission compared to healthy control group. Methods: In a prospective 2 years follow-up study, we investigated alterations in levels of BDNF and proBDNF in 79 patients (23 males, mean age 19.08, SD 3.3 and 56 females, mean age 18.39, SD 3.28) diagnosed with mood disorders: unipolar and bipolar disorder compared with 35 healthy control subjects (7 males, mean age 20.43, SD 4.23 and 28 females, mean age 21.25, SD 2.11). Clinical characteristics including mood, comorbidity, family history, and treatment, were evaluated during control visits and clinical symptoms were rated using the Hamilton Depression Rating Scale and Young Mania Rating Scale. Serum BDNF and proBDNF concentrations were determined by Enzyme-Linked Immunosorbent Assays (ELISA) method. Serum BDNF and proBDNF levels were analysed with covariates: sex, age, age > 18 and < 18 years old, family history of affective disorders, drug-free vs. medicated status. Normality of the data was tested using Shapiro-Wilk test. Levene’s test was used to calculate homogeneity of variance. Non-parametric Tests: Mann-Whitney U test, Kruskal-Wallis ANOVA, Friedman’s ANOVA, Wilcoxon signed rank test, Spearman correlation coefficient were applied in analyses The statistical significance level was set at p < 0.05. Results: BDNF and proBDNF serum levels did not differ between patients at baseline and controls as well as comparing patients in acute episode of depression/hypo/mania at baseline and euthymia (at month 3 or 6). Comparing BDNF and proBDNF levels between patients in euthymia and control group no differences have been found. Increased BDNF level in women compared to men at baseline (p=0.01) have been observed. BDNF level at baseline was negatively correlated with depression and mania occurence at 24 month (p=0.04). BDNF level at 12 month was negatively correlated with depression and mania occurence at 12 month (p=0.01). Correlation of BDNF level with sex have been detected (p=0.01). proBDNF levels at month 3, 6 and 12 negatively correlated with disease status (p=0.02, p=0.008, p=0.009, respectively). No other correlations of BDNF and proBDNF levels with clinical and demographical variables have been detected. Discussion: Our results did not show any differences in BDNF and proBDNF levels between depression, mania, euthymia, and controls. Imbalance in BDNF/proBDNF signalling may be involved in pathogenesis of mood disorders. Further studies on larger groups are recommended. Grant was founded by National Science Center in Poland no 2011/03/D/NZ5/06146.

Keywords: bipolar disorder, Brain-Derived Neurotrophic Factor (BDNF), proBDNF, unipolar depression

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Authors: K. C. Apil, Keshab Sharma, Bigul Pokharel

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Kathmandu Valley, capital city of Nepal houses numerous historical monuments as well as religious structures which are as old as from the 4th century A.D. The city alone is home to seven UNESCO’s world heritage sites including various public squares and religious sanctums which are often regarded as living heritages by various historians and archeological explorers. Recently on April 25, 2015, the capital city including other nearby locations was struck with Gorkha earthquake of moment magnitude (Mw) 7.8, followed by the strongest aftershock of moment magnitude (Mw) 7.3 on May 12. This study reports structural failures and collapse of heritage structures in Kathmandu Valley during the earthquake and presents preliminary findings as to the causes of failures and collapses. Field reconnaissance was carried immediately after the main shock and the aftershock, in major heritage sites: UNESCO world heritage sites, a number of temples and historic buildings in Kathmandu Durbar Square, Patan Durbar Square, and Bhaktapur Durbar Square. Despite such catastrophe, a significant number of heritage structures stood high, performing very well during the earthquake. Preliminary reports from archeological department suggest that 721 of such structures were severely affected, whereas numbers within the valley only were 444 including 76 structures which were completely collapsed. This study presents recorded accelerograms and geology of Kathmandu Valley. Structural typology and architecture of the heritage structures in Kathmandu Valley are briefly described. Case histories of damaged heritage structures, the patterns, and the failure mechanisms are also discussed in this paper. It was observed that performance of heritage structures was influenced by the multiple factors such as structural and architecture typology, configuration, and structural deficiency, local ground site effects and ground motion characteristics, age and maintenance level, material quality etc. Most of such heritage structures are of masonry type using bricks and earth-mortar as a bonding agent. The walls' resistance is mainly compressive, thus capable of withstanding vertical static gravitational load but not horizontal dynamic seismic load. There was no definitive pattern of damage to heritage structures as most of them behaved as a composite structure. Some structures were extensively damaged in some locations, while structures with similar configuration at nearby location had little or no damage. Out of major heritage structures, Dome, Pagoda (2, 3 or 5 tiered temples) and Shikhara structures were studied with similar variables. Studying varying degrees of damages in such structures, it was found that Shikhara structures were most vulnerable one where Dome structures were found to be the most stable one, followed by Pagoda structures. The seismic performance of the masonry-timber and stone masonry structures were slightly better than that of the masonry structures. Regular maintenance and periodic seismic retrofitting seems to have played pivotal role in strengthening seismic performance of the structure. The study also recommends some key functions to strengthen the seismic performance of such structures through study based on structural analysis, building material behavior and retrofitting details. The result also recognises the importance of documentation of traditional knowledge and its revised transformation in modern technology.

Keywords: Gorkha earthquake, field observation, heritage structure, seismic performance, masonry building

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Authors: Nervana Elbakary, Sami Ouanes, Sadaf Riaz, Oraib Abdallah, Islam Mahran, Noriya Al-Khuzaei, Yassin Eltorki

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Major Depressive Disorder (MDD) requires therapeutic interventions during the initial month after being diagnosed for better disease outcomes. International guidelines recommend a duration of 4–12 weeks for an initial antidepressant (IAD) trial at an optimized dose to get a response. If depressive symptoms persist after this duration, guidelines recommend switching, augmenting, or combining strategies as the next step. Most patients with MDD in the mental health setting have been labeled incorrectly as treatment-resistant where in fact they have not been subjected to an adequate trial of guideline-recommended therapy. Premature discontinuation of IAD due to ineffectiveness can cause unfavorable consequences. Avoiding irrational practices such as subtherapeutic doses of IAD, premature switching between the ADs, and refraining from unjustified polypharmacy can help the disease to go into a remission phase We aimed to determine the prevalence and the patterns of strategies applied after an IAD was changed because of a suboptimal response as a primary outcome. Secondary outcomes included the median survival time on IAD before any change; and the predictors that were associated with IAD change. This was a retrospective cross- sectional study conducted in Mental Health Services in Qatar. A dataset between January 1, 2018, and December 31, 2019, was extracted from the electronic health records. Inclusion and exclusion criteria were defined and applied. The sample size was calculated to be at least 379 patients. Descriptive statistics were reported as frequencies and percentages, in addition, to mean and standard deviation. The median time of IAD to any change strategy was calculated using survival analysis. Associated predictors were examined using two unadjusted and adjusted cox regression models. A total of 487 patients met the inclusion criteria of the study. The average age for participants was 39.1 ± 12.3 years. Patients with first experience MDD episode 255 (52%) constituted a major part of our sample comparing to the relapse group 206(42%). About 431 (88%) of the patients had an occurrence of IAD change to any strategy before end of the study. Almost half of the sample (212 (49%); 95% CI [44–53%]) had their IAD changed less than or equal to 30 days. Switching was consistently more common than combination or augmentation at any timepoint. The median time to IAD change was 43 days with 95% CI [33.2–52.7]. Five independent variables (age, bothersome side effects, un-optimization of the dose before any change, comorbid anxiety, first onset episode) were significantly associated with the likelihood of IAD change in the unadjusted analysis. The factors statistically associated with higher hazard of IAD change in the adjusted analysis were: younger age, un-optimization of the IAD dose before any change, and comorbid anxiety. Because almost half of the patients in this study changed their IAD as early as within the first month, efforts to avoid treatment failure are needed to ensure patient-treatment targets are met. The findings of this study can have direct clinical guidance for health care professionals since an optimized, evidence-based use of AD medication can improve the clinical outcomes of patients with MDD; and also, to identify high-risk factors that could worsen the survival time on IAD such as young age and comorbid anxiety

Keywords: initial antidepressant, dose optimization, major depressive disorder, comorbid anxiety, combination, augmentation, switching, premature discontinuation

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Authors: Salvatore Brischetto, Domenico Cesare

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Major improvements in future aircraft and spacecraft could be those dependent on an increasing use of conventional and unconventional multilayered structures embedding composite materials, functionally graded materials, piezoelectric or piezomagnetic materials, and soft foam or honeycomb cores. Layers made of such materials can be combined in different ways to obtain structures that are able to fulfill several structural requirements. The next generation of aircraft and spacecraft will be manufactured as multilayered structures under the action of a combination of two or more physical fields. In multifield problems for multilayered structures, several physical fields (thermal, hygroscopic, electric and magnetic ones) interact each other with different levels of influence and importance. An exact 3D shell model is here proposed for these types of analyses. This model is based on a coupled system including 3D equilibrium equations, 3D Fourier heat conduction equation, 3D Fick diffusion equation and electric and magnetic divergence equations. The set of partial differential equations of second order in z is written using a mixed curvilinear orthogonal reference system valid for spherical and cylindrical shell panels, cylinders and plates. The order of partial differential equations is reduced to the first one thanks to the redoubling of the number of variables. The solution in the thickness z direction is obtained by means of the exponential matrix method and the correct imposition of interlaminar continuity conditions in terms of displacements, transverse stresses, electric and magnetic potentials, temperature, moisture content and transverse normal multifield fluxes. The investigated structures have simply supported sides in order to obtain a closed form solution in the in-plane directions. Moreover, a layerwise approach is proposed which allows a 3D correct description of multilayered anisotropic structures subjected to field loads. Several results will be proposed in tabular and graphical formto evaluate displacements, stresses and strains when mechanical loads, temperature gradients, moisture content gradients, electric potentials and magnetic potentials are applied at the external surfaces of the structures in steady-state conditions. In the case of inclusions of piezoelectric and piezomagnetic layers in the multilayered structures, so called smart structures are obtained. In this case, a free vibration analysis in open and closed circuit configurations and a static analysis for sensor and actuator applications will be proposed. The proposed results will be useful to better understand the physical and structural behaviour of multilayered advanced composite structures in the case of multifield interactions. Moreover, these analytical results could be used as reference solutions for those scientists interested in the development of 3D and 2D numerical shell/plate models based, for example, on the finite element approach or on the differential quadrature methodology. The correct impositions of boundary geometrical and load conditions, interlaminar continuity conditions and the zigzag behaviour description due to transverse anisotropy will be also discussed and verified.

Keywords: composite structures, 3D shell model, stress analysis, multifield loads, exponential matrix method, layer wise approach

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Authors: Naoum Mylonas, Eugenia Petridou

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Objectives: The objectives of this research are firstly, to develop an integrated model of predicting factors to new ventures performance, taking into account certain issues and specificities related to creative industry and female entrepreneurship based on the prior research; secondly, to determine the appropriate measures of venture performance in a creative industry context, drawing upon previous surveys; thirdly, to illustrate the importance of entrepreneurial orientation, networking ties, environment dynamism and access to financial capital on new ventures performance. Prior Work: An extant review of the creative industry literature highlights the special nature of entrepreneurship in this field. Entrepreneurs in creative industry share certain specific characteristics and intensions, such as to produce something aesthetic, to enrich their talents and their creativity, and to combine their entrepreneurial with their artistic orientation. Thus, assessing venture performance and success in creative industry entails an examination of how creative people or artists conceptualize success. Moreover, female entrepreneurs manifest more positive attitudes towards sectors primarily based on creativity, rather than innovation in which males outbalance. As creative industry entrepreneurship based mainly on the creative personality of the creator / artist, a high interest is accrued to examine female entrepreneurship in the creative industry. Hypotheses development: H1a: Female entrepreneurs who are more entrepreneurially-oriented show a higher financial performance. H1b: Female entrepreneurs who are more artistically-oriented show a higher creative performance. H2: Female entrepreneurs who have personality that is more creative perform better. H3: Female entrepreneurs who participate in or belong to networks perform better. H4: Female entrepreneurs who have been consulted by a mentor perform better. Η5a: Female entrepreneurs who are motivated more by pull-factors perform better. H5b: Female entrepreneurs who are motivated more by push-factors perform worse. Approach: A mixed method triangulation design has been adopted for the collection and analysis of data. The data are collected through a structured questionnaire for the quantitative part and through semi-structured interviews for the qualitative part as well. The sample is 293 Greek female entrepreneurs in the creative industry. Main findings: All research hypotheses are accepted. The majority of creative industry entrepreneurs evaluate themselves in creative performance terms rather than financial ones. The individuals who are closely related to traditional arts sectors have no EO but also evaluate themselves highly in terms of venture performance. Creative personality of creators is appeared as the most important predictor of venture performance. Pull factors in accordance with our hypothesis lead to higher levels of performance compared to push factors. Networking and mentoring are viewed as very important, particularly now during the turbulent economic environment in Greece. Implications-Value: Our research provides an integrated model with several moderating variables to predict ventures performance in the creative industry, taking also into account the complicated nature of arts and the way artists and creators define success. At the end, the findings may be used for the appropriate design of educational programs in creative industry entrepreneurship. This research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund.

Keywords: venture performance, female entrepreneurship, creative industry, networks

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Authors: Pedro Llanos, Diego García

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Spaceflight is considered the last frontier in terms of science, technology, and engineering. But it is also the next frontier in terms of human physiology and performance. After more than 200,000 years humans have evolved under earth’s gravity and atmospheric conditions, spaceflight poses environmental stresses for which human physiology is not adapted. Hypoxia, accelerations, and radiation are among such stressors, our research involves suborbital flights aiming to develop effective countermeasures in order to assure sustainable human space presence. The physiologic baseline of spaceflight participants is subject to great variability driven by age, gender, fitness, and metabolic reserve. The objective of the present study is to characterize different physiologic variables in a population of STEM practitioners during an aerobatic flight. Cardiovascular and pulmonary responses were determined in Science Astronaut Candidates (SACs) during unusual attitude aerobatic flight indoctrination. Physiologic data recordings from 20 subjects participating in high-G flight training were analyzed. These recordings were registered by wearable sensor-vest that monitored electrocardiographic tracings (ECGs), signs of dysrhythmias or other electric disturbances during all the flight. The same cardiovascular parameters were also collected approximately 10 min pre-flight, during each high-G/unusual attitude maneuver and 10 min after the flights. The ratio (pre-flight/in-flight/post-flight) of the cardiovascular responses was calculated for comparison of inter-individual differences. The resulting tracings depicting the cardiovascular responses of the subjects were compared against the G-loads (Gs) during the aerobatic flights to analyze cardiovascular variability aspects and fluid/pressure shifts due to the high Gs. In-flight ECG revealed cardiac variability patterns associated with rapid Gs onset in terms of reduced heart rate (HR) and some scattered dysrhythmic patterns (15% premature ventricular contractions-type) that were considered as triggered physiological responses to high-G/unusual attitude training and some were considered as instrument artifact. Variation events were observed in subjects during the +Gz and –Gz maneuvers and these may be due to preload and afterload, sudden shift. Our data reveal that aerobatic flight influenced the breathing rate of the subject, due in part by the various levels of energy expenditure due to the increased use of muscle work during these aerobatic maneuvers. Noteworthy was the high heterogeneity in the different physiological responses among a relatively small group of SACs exposed to similar aerobatic flights with similar Gs exposures. The cardiovascular responses clearly demonstrated that SACs were subjected to significant flight stress. Routine ECG monitoring during high-G/unusual attitude flight training is recommended to capture pathology underlying dangerous dysrhythmias in suborbital flight safety. More research is currently being conducted to further facilitate the development of robust medical screening, medical risk assessment approaches, and suborbital flight training in the context of the evolving commercial human suborbital spaceflight industry. A more mature and integrative medical assessment method is required to understand the physiology state and response variability among highly diverse populations of prospective suborbital flight participants.

Keywords: g force, aerobatic maneuvers, suborbital flight, hypoxia, commercial astronauts

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Authors: Ichiro Takahashi

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One of the major assumptions of mainstream macroeconomics is the path independence of capital stock. This paper challenges this assumption by employing an agent-based approach. The simulation results showed the existence of multiple "quasi-steady state" equilibria of the capital stock, which may cast serious doubt on the validity of the assumption. The finding would give a better understanding of many phenomena that involve hysteresis, including the causes of poverty. The "market-clearing view" has been widely shared among major schools of macroeconomics. They understand that the capital stock, the labor force, and technology, determine the "full-employment" equilibrium growth path and demand/supply shocks can move the economy away from the path only temporarily: the dichotomy between the short-run business cycles and the long-run equilibrium path. The view then implicitly assumes the long-run capital stock to be independent of how the economy has evolved. In contrast, "Old Keynesians" have recognized fluctuations in output as arising largely from fluctuations in real aggregate demand. It will then be an interesting question to ask if an agent-based macroeconomic model, which is known to have path dependence, can generate multiple full-employment equilibrium trajectories of the capital stock in the super-long run. If the answer is yes, the equilibrium level of capital stock, an important supply-side factor, would no longer be independent of the business cycle phenomenon. This paper attempts to answer the above question by using the agent-based macroeconomic model developed by Takahashi and Okada (2010). The model would serve this purpose well because it has neither population growth nor technology progress. The objective of the paper is twofold: (1) to explore the causes of long-term business cycle, and (2) to examine the super-long behaviors of the capital stock of full-employment economies. (1) The simulated behaviors of the key macroeconomic variables such as output, employment, real wages showed widely diversified macro-economies. They were often remarkably stable but exhibited both short-term and long-term fluctuations. The long-term fluctuations occur through the following two adjustments: the quantity and relative cost adjustments of capital stock. The first one is obvious and assumed by many business cycle theorists. The reduced aggregate demand lowers prices, which raises real wages, thereby decreasing the relative cost of capital stock with respect to labor. (2) The long-term business cycles/fluctuations were synthesized with the hysteresis of real wages, interest rates, and investments. In particular, a sequence of the simulation runs with a super-long simulation period generated a wide range of perfectly stable paths, many of which achieved full employment: all the macroeconomic trajectories, including capital stock, output, and employment, were perfectly horizontal over 100,000 periods. Moreover, the full-employment level of capital stock was influenced by the history of unemployment, which was itself path-dependent. Thus, an experience of severe unemployment in the past kept the real wage low, which discouraged a relatively costly investment in capital stock. Meanwhile, a history of good performance sometimes brought about a low capital stock due to a high-interest rate that was consistent with a strong investment.

Keywords: agent-based macroeconomic model, business cycle, hysteresis, stability

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Authors: Eunice Oluwakemi Chukwuma-Nwuba

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Nigeria is a developing country with an increasing rate of graduate unemployment. This has triggered successive government administrations to promote the variety of programmes to address the situation. However, none of these efforts yielded the desired outcome. Accordingly, in 2006 the government included entrepreneurship module in the curriculum of universities as a compulsory general programme for all undergraduate courses. This is in the hope that the programme will help to promote entrepreneurial mind-set and new venture creation among graduates and as a result reduce the rate of graduate unemployment. The study explores the effectiveness of entrepreneurship education in promoting entrepreneurship. This study is significant in view of the endemic graduate unemployment in Nigeria and the social consequences such as youth restiveness and militancy. It is guided by the theory of planned behaviour. It employed the two-stage structural equation modelling (AMOS) to model entrepreneurial intentions as a function of innovative teaching methods, traditional teaching methods and culture Personal attitude and subjective norm are proposed to mediate the relationships between the exogenous and the endogenous variables. The first stage was tested using multi-group confirmatory factor analysis (MGCFA) framework to confirm that the two groups assign the same meaning to the scale items and to obtain goodness-of-fit indices. The multi-group confirmatory factor analysis included the tests of configural, metric and scalar invariance. With the attainment of full configural invariance and partial metric and scalar invariance, the second stage – the structural model was applied hypothesising that, the entrepreneurial intentions of graduates (respondents who have participated in the compulsory entrepreneurship programme) will be higher than those of undergraduates (respondents who are yet to participate in the programme). The study uses the quasi-experimental design. The samples comprised 409 graduates (experimental group) and 402 undergraduates (control group) from six federal universities in Nigeria. Our findings suggest that personal attitude is positively related with entrepreneurial intentions, largely confirming prior literature. However, unlike previous studies, our results indicate that subjective norm has significant direct and indirect impact on entrepreneurial intentions indicating that reference people of the participants have important roles to play in their decision to be entrepreneurial. Furthermore, unlike the assertions in prior studies, the result suggests that traditional teaching methods have indirect effect on entrepreneurial intentions supporting that since personal characteristics can change in an educational situation, an education purposively directed at entrepreneurship might achieve similar results if not better. This study has implication for practice and theory. The research extends to the theoretical understanding of the formation of entrepreneurial intentions and explains the role of the reference others in relation to how graduates perceive entrepreneurship. Further, the study adds to the body of knowledge on entrepreneurship education in Nigeria universities and provides a developing country perspective. It proposes further research in the exploration of entrepreneurship education and entrepreneurial intentions of graduates from across the country’s universities as necessary and imperative.

Keywords: entrepreneurship education, entrepreneurial intention, structural equation modeling, theory of planned behaviour

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Authors: Milos Travar, Jovana Nikolov, Andrej Vranicar, Natasa Todorovic

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Over the years, High Purity Germanium (HPGe) detectors proved to be an excellent practical tool and, as such, have established their today's wide use in low background γ-spectrometry. One of the advantages of gamma-ray spectrometry is its easy sample preparation as chemical processing and separation of the studied subject are not required. Thus, with a single measurement, one can simultaneously perform both qualitative and quantitative analysis. One of the most prominent features of HPGe detectors, besides their excellent efficiency, is their superior resolution. This feature virtually allows a researcher to perform a thorough analysis by discriminating photons of similar energies in the studied spectra where otherwise they would superimpose within a single-energy peak and, as such, could potentially scathe analysis and produce wrongly assessed results. Naturally, this feature is of great importance when the identification of radionuclides, as well as their activity concentrations, is being practiced where high precision comes as a necessity. In measurements of this nature, in order to be able to reproduce good and trustworthy results, one has to have initially performed an adequate full-energy peak (FEP) efficiency calibration of the used equipment. However, experimental determination of the response, i.e., efficiency curves for a given detector-sample configuration and its geometry, is not always easy and requires a certain set of reference calibration sources in order to account for and cover broader energy ranges of interest. With the goal of overcoming these difficulties, a lot of researches turned towards the application of different software toolkits that implement the Monte Carlo method (e.g., MCNP, FLUKA, PENELOPE, Geant4, etc.), as it has proven time and time again to be a very powerful tool. In the process of creating a reliable model, one has to have well-established and described specifications of the detector. Unfortunately, the documentation that manufacturers provide alongside the equipment is rarely sufficient enough for this purpose. Furthermore, certain parameters tend to evolve and change over time, especially with older equipment. Deterioration of these parameters consequently decreases the active volume of the crystal and can thus affect the efficiencies by a large margin if they are not properly taken into account. In this study, the optimisation method of two HPGe detectors through the implementation of the Geant4 toolkit developed by CERN is described, with the goal of further improving simulation accuracy in calculations of FEP efficiencies by investigating the influence of certain detector variables (e.g., crystal-to-window distance, dead layer thicknesses, inner crystal’s void dimensions, etc.). Detectors on which the optimisation procedures were carried out were a standard traditional co-axial extended range detector (XtRa HPGe, CANBERRA) and a broad energy range planar detector (BEGe, CANBERRA). Optimised models were verified through comparison with experimentally obtained data from measurements of a set of point-like radioactive sources. Acquired results of both detectors displayed good agreement with experimental data that falls under an average statistical uncertainty of ∼ 4.6% for XtRa and ∼ 1.8% for BEGe detector within the energy range of 59.4−1836.1 [keV] and 59.4−1212.9 [keV], respectively.

Keywords: HPGe detector, γ spectrometry, efficiency, Geant4 simulation, Monte Carlo method

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Authors: Swati Bhomia, C. M. Kishtawal, Neeru Jaiswal

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Traditionally, monsoon forecasts have encountered many difficulties that stem from numerous issues such as lack of adequate upper air observations, mesoscale nature of convection, proper resolution, radiative interactions, planetary boundary layer physics, mesoscale air-sea fluxes, representation of orography, etc. Uncertainties in any of these areas lead to large systematic errors. Global circulation models (GCMs), which are developed independently at different institutes, each of which carries somewhat different representation of the above processes, can be combined to reduce the collective local biases in space, time, and for different variables from different models. This is the basic concept behind the multi-model superensemble and comprises of a training and a forecast phase. The training phase learns from the recent past performances of models and is used to determine statistical weights from a least square minimization via a simple multiple regression. These weights are then used in the forecast phase. The superensemble forecasts carry the highest skill compared to simple ensemble mean, bias corrected ensemble mean and the best model out of the participating member models. This approach is a powerful post-processing method for the estimation of weather forecast parameters reducing the direct model output errors. Although it can be applied successfully to the continuous parameters like temperature, humidity, wind speed, mean sea level pressure etc., in this paper, this approach is applied to rainfall, a parameter quite difficult to handle with standard post-processing methods, due to its high temporal and spatial variability. The present study aims at the development of advanced superensemble schemes comprising of 1-5 day daily precipitation forecasts from five state-of-the-art global circulation models (GCMs), i.e., European Centre for Medium Range Weather Forecasts (Europe), National Center for Environmental Prediction (USA), China Meteorological Administration (China), Canadian Meteorological Centre (Canada) and U.K. Meteorological Office (U.K.) obtained from THORPEX Interactive Grand Global Ensemble (TIGGE), which is one of the most complete data set available. The novel approaches include the dynamical model selection approach in which the selection of the superior models from the participating member models at each grid and for each forecast step in the training period is carried out. Multi-model superensemble based on the training using similar conditions is also discussed in the present study, which is based on the assumption that training with the similar type of conditions may provide the better forecasts in spite of the sequential training which is being used in the conventional multi-model ensemble (MME) approaches. Further, a variety of methods that incorporate a 'neighborhood' around each grid point which is available in literature to allow for spatial error or uncertainty, have also been experimented with the above mentioned approaches. The comparison of these schemes with respect to the observations verifies that the newly developed approaches provide more unified and skillful prediction of the summer monsoon (viz. June to September) rainfall compared to the conventional multi-model approach and the member models.

Keywords: multi-model superensemble, dynamical model selection, similarity criteria, neighborhood technique, rainfall prediction

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Authors: André Python

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To this day, terrorism persists as a worldwide threat, exemplified by the recent deadly attacks in January 2015 in Paris and the ongoing massacres perpetrated by ISIS in Iraq and Syria. In response to this threat, states deploy various counterterrorism measures, the cost of which could be reduced through effective preventive measures. In order to increase the efficiency of preventive measures, policy-makers may benefit from accurate predictive models that are able to capture the complex spatial dynamics of terrorism occurring at a local scale. Despite empirical research carried out at country-level that has confirmed theories explaining the diffusion processes of terrorism across space and time, scholars have failed to assess diffusion’s theories on a local scale. Moreover, since scholars have not made the most of recent statistical modelling approaches, they have been unable to build up predictive models accurate in both space and time. In an effort to address these shortcomings, this research suggests a novel approach to systematically assess the theories of terrorism’s diffusion on a local scale and provide a predictive model of the local spatial dynamics of terrorism worldwide. With a focus on the lethal terrorist events that occurred after 9/11, this paper addresses the following question: why and how does lethal terrorism diffuse in space and time? Based on geolocalised data on worldwide terrorist attacks and covariates gathered from 2002 to 2013, a binomial spatio-temporal point process is used to model the probability of terrorist attacks on a sphere (the world), the surface of which is discretised in the form of Delaunay triangles and refined in areas of specific interest. Within a Bayesian framework, the model is fitted through an integrated nested Laplace approximation - a recent fitting approach that computes fast and accurate estimates of posterior marginals. Hence, for each location in the world, the model provides a probability of encountering a lethal terrorist attack and measures of volatility, which inform on the model’s predictability. Diffusion processes are visualised through interactive maps that highlight space-time variations in the probability and volatility of encountering a lethal attack from 2002 to 2013. Based on the previous twelve years of observation, the location and lethality of terrorist events in 2014 are statistically accurately predicted. Throughout the global scope of this research, local diffusion processes such as escalation and relocation are systematically examined: the former process describes an expansion from high concentration areas of lethal terrorist events (hotspots) to neighbouring areas, while the latter is characterised by changes in the location of hotspots. By controlling for the effect of geographical, economical and demographic variables, the results of the model suggest that the diffusion processes of lethal terrorism are jointly driven by contagious and non-contagious factors that operate on a local scale – as predicted by theories of diffusion. Moreover, by providing a quantitative measure of predictability, the model prevents policy-makers from making decisions based on highly uncertain predictions. Ultimately, this research may provide important complementary tools to enhance the efficiency of policies that aim to prevent and combat terrorism.

Keywords: diffusion process, terrorism, spatial dynamics, spatio-temporal modeling

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