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This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city centre, sub-centres and outskirts or suburbs. It estimates the Italian long-term house price index, city based in real terms, and shows a combination of methods to deal with large time-series data.

This paper builds long-term cycles based on the city (real) data by estimating the common components of cointegrated time series and extracting the unobservable signals to build real house price index for sub-regions in Italy. Three different econometric methodologies are used: Johansen cointegration test and VAR models to identify the long-term pattern of prices at the estimated aggregate level; principal components to obtain the common (permanent and transitory) components; and signal extraction in ARIMA time series–model-based approach method to extract the unobserved time signals.

Results show three long-term cycle-trends during the period and identify several one-direction causal non-permanent relationships among house prices from different Italian areas. There is no evidence of convergence among regional’s house prices suggesting that the Italian housing prices converge inside the local market with only short diffusion effects at larger regional level.

Data are measured as the average price in squared meters, and the resulting index is not quality controlled.

The long-term trends on housing prices serve to implement further research and know deeply the evolution of Italian housing prices.

This paper contains new and unknown information about the evolution of housing prices in Italian regions and cities.

With the global financial crisis, the United Arab Emirates (UAE) experienced its own unraveling of macro-financial imbalances and thus presents an interesting case to analyze the underlying fragilities in federal governments. The purpose of this paper is to investigate the evolution of fiscal policy in the UAE at consolidated and subnational levels in the run-up and after the crisis, and provide pertinent insights about the importance of policy coordination in other federal fiscal systems – and monetary unions, as brought to light by the recent developments in Europe.

In measuring the cyclicality of fiscal balances at the consolidated and emirate level in the UAE, this paper uses the non-hydrocarbon primary budget balance, excluding interest spending and hydrocarbon revenues, investment income of the sovereign wealth fund, scaled by non-hydrocarbon GDP. The cyclically adjusted primary balance is estimated by deducting cyclical components from the actual balance. It is important to correct for cyclical changes because the budget balance tends to vary endogenously according the state of the economy – deteriorating during a bust and improving in a boom. Furthermore, since hydrocarbon revenues are dependent on the erratic behavior of hydrocarbon prices, the cyclically adjusted non-hydrocarbon primary balance is computed, using the elasticity of non-hydrocarbon revenues and primary expenditures relative to non-hydrocarbon GDP, to assess whether fiscal policy exacerbates economic fluctuations in the UAE at the aggregate and emirate levels.

The empirical findings show that procyclical fiscal policies prior to the crisis reinforced the financial sector cycle, exacerbated the economic upswing, and thereby contributed to the build-up of macro-financial vulnerabilities. The paper also sets out policy lessons to develop a rule-based fiscal framework that would help strengthen fiscal policy coordination between the various layers of government and ensure long-term fiscal sustainability and a more equitable intergenerational distribution of wealth.

The lack of fiscal policy coordination among subnational governments complicates macro-economic management at the federal level. Since the UAE has a pegged exchange rate regime and consequently a limited scope to use monetary policy, the burden of macro-economic stabilization falls on fiscal policy. Accordingly, this paper shows that procyclical fiscal policies prior to the crisis reinforced the “financial accelerator” effect, exacerbated the economic cycle, and thereby contributed to the build-up of economic and financial vulnerabilities in the UAE.

The developments of complex systems have increased the demand for condition monitoring techniques so as to maximize operational availability and safety while decreasing the costs. Signal analysis is one of the methods used to develop condition monitoring in order to extract important information contained in the sensory signals, which can be used for health assessment. However, extraction of such information from collected data in a practical working environment is always a great challenge as sensory signals are usually multi-dimensional and obscured by noise. The paper aims to discuss this issue.

This paper presents a method for trends extraction from multi-dimensional sensory data, which are then used for machinery health monitoring and maintenance needs. The proposed method is based on extracting successive features from machinery sensory signals. Then, unsupervised feature selection on the features domain is applied without making any assumptions concerning the source of the signals and the number of the extracted features. Finally, empirical mode decomposition (EMD) algorithm is applied on the projected features with the purpose of following the evolution of data in a compact representation over time.

The method is demonstrated on accelerated degradation data set of bearings acquired from PRONOSTIA experimental platform and a second data set acquired form NASA repository.

The method showed that it is able to extract interesting signal trends which can be used for health monitoring and remaining useful life prediction.

Access to daily store level scanner data has been increasingly easier in recent years in Japan and time series analysis based on a sales response model is becoming realistic. Introduces a new method of combining time series analysis and regression analysis on the price promotion effect, which enables simultaneous decomposition of store level scanner sales into trend (including seasonality), day‐of‐the‐week effect and explanatory variable effect due to price promotion. The method was applied to daily store level scanner sales of milk, showing evidence of the existence of day‐of‐the‐week effect. Further, a method of incorporating several kinds of price‐cut variables in regression analysis and the analyzed results were presented.

This article explores the recent turn within academic publishing towards ‘seamless access’, an approach to content provision that ensures users do not have to continually authenticate in order to access journal content.

Through a critical exploration of Get Full Text Research, a service developed collaboratively by five of the world's largest academic publishers to provide such seamless access to academic research, the article shows how publishers are seeking to control the ways in which readers access publications in order to trace, control and ultimately monetise user interactions on their platforms.

Theorised as a process of individuation through infrastructure, the article reveals how publishers are attempting an ontological shift to position the individual, quantifiable researcher, rather than the published content, at the centre of the scholarly communication universe.

The implications of the shift towards individuation are revealed as part of a broader trend in scholarly communication infrastructure towards data extraction, mirroring a trend within digital capitalism more generally.

This paper aims to present a new method for identification of some flight modes, including natural and non-standard modes, and extraction of their characteristics, directly from measurements of flight parameters in the time domain.

The Hilbert-Huang transform (HHT), as a novel prevailing tool in the signal analysis field, is used to attain the purpose. The study shows that the HHT has superior potential capabilities to improve the airplane flying quality analysis and to conquer some drawbacks of the classical method in flight dynamics.

The proposed method reveals the existence of some non-standard modes with small damping ratios at non-linear flight regions and obtains their characteristics.

The paper examines only airplane longitudinal dynamics. Further research is needed regarding lateral-directional dynamic modes and coupling effects of the longitudinal and lateral modes.

Application of the proposed method to the flight test data may result in real-time flying quality analysis, especially at the non-linear flight regions.

First, to utilize the empirical mode decomposition (EMD) capabilities in real time, a local-online algorithm is introduced which estimates the signal trend by the Savitzky-Golay sifting process and eliminates it from the signal in the EMD algorithm. Second, based on the local-online EMD algorithm, a systematic method is proposed to identify flight modes from flight parameters in the time domain.

The amount of food wasted every year is 1.3 billion metric tonne (MT), out of which 0.5 billion MT is contributed by the fruits processing industries. The waste includes by-products such as peels, pomace and seeds and is a good source of bioactive compounds like phenolic compounds, flavonoids, pectin lipids and dietary fibres. Hence, the purpose of the present study is to review the novel extraction techniques used for the extraction of the bio active compounds from food waste for the selection of suitable extraction method.

Novel extraction techniques such as ultrasound-assisted extraction, microwave-assisted extraction, enzyme-assisted extraction, supercritical fluid extraction, pulsed electric field extraction and pressurized liquid extraction have emerged to overcome the drawbacks and constraints of conventional extraction techniques. Hence, this study is focussed on novel extraction techniques, their limitations and optimization for the extraction of bioactive compounds from fruit and vegetable waste.

This study presents a comprehensive review on the novel extraction processes that have been adopted for the extraction of bioactive compounds from food waste. This paper also summarizes bioactive compounds' optimum extraction condition from various food waste using novel extraction techniques.

Food waste is rich in bioactive compounds, and its efficient extraction may add value to the food processing industries. Hence, compressive analysis is needed to overcome the problem associated with the extraction and selection of suitable extraction techniques.

Selection of a suitable extraction method will not only add value to food waste but also reduce waste dumping and the cost of bioactive compounds.

This paper presents the research progress on the extraction of bioactive active compounds from food waste using novel extraction techniques.

This paper looks at the application of economic and financial series in forecasting IPD monthly rental series. The approach follows that employed in classical business cycle work that seeks to decompose series into trend, cyclical and noise components and is the first time that it has been applied to IPD monthly data. Trend extraction is obtained by means of the Hodrick‐Prescott filter. Several potential indicator series are investigated together with their lead characteristics. The short‐term forecasts of these series are compared with naïve methods and a composite indicator. The results show the naïve methods, especially the Holt‐Winters method, and certain leading indicator series produce satisfactory short‐term forecasts, but the success is both sector and time‐dependent. This suggests that it is a worthwhile endeavour in identifying potential leading indicator series. The methodology presented in this paper should be seen as complementing existing approaches that employ standard econometric procedures in modelling rental growth.

Reappraises the stylised facts of the contemporary UK business cycle and the robustness of associated sample moments to detrending under the Hodrick‐Prescott (HP) filter and an unobserved components (UC) model based on the structural time series mode of Harvey and advocated in this context by Harvey and Jaeger. For the majority of series considered, findings broadly confirm the earlier HP‐based results of Blackburn and Ravn, but important differences with previous results are reported for labour productivity, the real wage and the real interest rate. However, under neither detrending method are the anticipated cross‐correlations between output and the pivotal variables in standard real business cycle (RBC) models (labour productivity, real wages, the real interest rate and nominal variables) simultaneously confirmed. Indeed, on balance, these results may be interpreted as more suggestive of an orthodox demand‐led or policy‐induced cycle.

The purpose of this paper is to propose a framework to detect adverse drug reactions (ADRs) using internet user search data, so that ADR events can be identified early. Empirical investigation of Avandia, a type II diabetes treatment, is conducted to illustrate how to implement the proposed framework.

Typical ADR identification measures and time series processing techniques are used in the proposed framework. Google Trends Data are employed to represent user searches. The baseline model is a disproportionality analysis using official drug reaction reporting data from the US Food and Drug Administration’s Adverse Event Reporting System.

Results show that Google Trends series of Avandia side effects search reveal a significant early warning signal for the side effect emergence of Avandia. The proposed approach of using user search data to detect ADRs is proved to have a longer leading time than traditional drug reaction discovery methods. Three more drugs with known adverse reactions are investigated using the selected approach, and two are successfully identified.

Validation of Google Trends data’s representativeness of user search is yet to be explored. In future research, user search in other search engines and in healthcare web forums can be incorporated to obtain a more comprehensive ADR early warning mechanism.

Using internet data in drug safety management with a proper early warning mechanism may serve as an earlier signal than traditional drug adverse reaction. This has great potential in public health emergency management.

The research work proposes a novel framework of using user search data in ADR identification. User search is a voluntary drug adverse reaction exploration behavior. Furthermore, user search data series are more concise and accurate than text mining in forums. The proposed methods as well as the empirical results will shed some light on incorporating user search data as a new source in pharmacovigilance.