Degradation of Lake Ziway,Ethiopia: A study of the environmental perceptions of school students

Lake Ziway in Ethiopia is a freshwater lake supporting multitude uses, including irrigation, fishing, water supply and recreation. However, the lake is being degraded primarily because of various land‐ and water‐use activities in its watershed. Recognizing these human impacts on the lake's water quality and quantity, the perceptions of school students were surveyed under the belief that the future of the lake is largely dependent on this younger generation. The goal of this study was to better understand how students actually recognize the extent to which various human activities impact the water quantity and quality of Lake Ziway. As major stakeholders in their communities, the perceptions of school students from four high schools regarding the changes to the lake over time were investigated. Students were contacted by purposive sampling, based on criteria of student academic performances, and their proximity to the lake. Their perceptions were studied on the basis of an interview survey. The majority of students indicated that high levels of human activities, especially water abstractions and water pollution, were the main lake impacts. Students believe there is a lack of sustainable utilization and protection of the lake. They suggested coordinated activities among all lake stakeholders in the participation and decision‐making process to properly manage the lake. Provision of awareness to both local administrations and local communities, concern for establishing an appropriate regulatory body and allocating a sufficient budget, and strict controls on flower farm practices in the lakeshore, also was considered to be of high priority as a means of conserving the sustainability of the lake. The result derived from this survey has important implications for facilitating improved school curricula, and for federal, regional and district level water resource management authorities, in setting priorities and developing policies for the sustainability of Lake Ziway.     

Improved method for development of parsimonious orthonormal basis filter models

One of the major advantages of orthonormal basis filter (OBF) models is that they are parsimonious in parameters. However, this is true only if appropriate type of filter and reasonably accurate dominant poles of the system are used in developing the model. An arbitrary choice of filter type and poles may lead to non-parsimonious model. While the selection of the type of filter may be simple if the damping characteristics of the system are known, finding good estimates of the dominant pole(s) of the system is not a trivial task. Another important advantage of OBF model is the fact that time delays can be easily estimated and incorporated into the model. Currently, time delay of the system is estimated from the step response of the OBF model using the tangent method. While this method is effective in estimating the time delay of systems that can be accurately modeled by first order plus time delay (FOPTD) models, the accuracy is low for systems with second- and higher-order dynamics. In this paper, a scheme is proposed that will result in parsimonious OBF model and a better estimate of time delay starting from an arbitrary set of poles.     

An empirical study of software release notes

Release notes are an important source of information about a new software release. Such notes contain information regarding what is new, changed, and/or got fixed in a release. Despite the importance of release notes, they are rarely explored in the research literature. Little is known about the contained information, e.g., contents and structure, in release notes. To better understand the types of contained information in release notes, we manually analyzed 85 release notes across 15 different software systems. In our manual analysis, we identify six different types of information (e.g., caveats and addressed issues) that are contained in release notes. Addressed issues refer to new features, bugs, and improvements that were integrated in that particular release. We observe that most release notes list only a selected number of addressed issues (i.e., 6-26 % of all addressed issues in a release). We investigated nine different factors (e.g., issue priority and type) to better understand the likelihood of an issue being listed in release notes. The investigation is conducted on eight release notes of three software systems using four machine learning techniques. Results show that certain factors, e.g., issue type, have higher influence on the likelihood of an issue to be listed in release notes. We use machine learning techniques to automatically suggest the issues to be listed in release notes. Our results show that issues listed in all release notes can be automatically determined with an average precision of 84 % and an average recall of 90 %. To train and build the classification models, we also explored three scenarios: (a) having the user label some issues for a release and automatically suggest the remaining issues for that particular release, (b) using the previous release notes for the same software system, and (c) using prior releases for the current software system and the rest of the studied software systems. Our results show that the content of release notes vary between software systems and across the versions of the same software system. Nevertheless, automated techniques can provide reasonable support to the writers of such notes with little training data. Our study provides developers with empirically-supported advice about release notes instead of simply relying on adhoc advice from on-line inquiries.     

Radiant floors integrated with PCM for indoor temperature control

The control of indoor thermal comfort in buildings through thermal inertia during the summer season plays a fundamental role in the design of energy efficient buildings, especially in the Mediterranean climate. In fact, lightweight, highly insulated buildings cannot provide the necessary mass to buffer thermal gains. Phase change materials (PCM) have been used to provide lightweight building components with the required thermal inertia without increasing their overall mass. So far the integration of PCM into lightweight piped radiant floors for the control of thermal comfort during summer cooling regimes has not been investigated. This paper reports the development of a lightweight piped radiant floor prototype with an integrated PCM layer aimed at buffering internal gains at constant temperature during summer cooling regimes without affecting its winter warming capacity. Both the construction of the laboratory specimens and the development of the optimized finite element models are detailed and the assessment of the floor performance in a simulated room is discussed.     

Closed-loop identification of systems with uncertain time delays using ARX-OBF structure

Closed-loop identification of systems with known time delays can be effectively carried out with simple model structures like Autoregressive with Exogenous Input (ARX) and Autoregressive Moving Average with Exogenous Input (ARMAX). However, when the system contains large uncertain time delay, such structures may lead to inaccurate models with significant bias if the time delay estimate used in the identification is less accurate. On the other hand, conventional orthonormal basis filter (OBF) model structures are very effective in capturing the dynamics of systems with uncertain time delays. However, they are not effective for closed-loop identification. In this paper, an ARX-OBF model structure which is obtained by modifying the ARX structure is shown to be effective in handling closed-loop identification of systems with uncertain time delays. In addition, the paper shows that this advantage of ARX-OBF models over simple ARX model is considerable in multi-step ahead predictions.     

A temporal domain audio watermarking technique

Audio watermarking techniques can be used to embed extra information into audio signals. The goal is to hide prespecified data carrying some information into the audio stream such that it is not audible to the human ear (i.e., transparent) and is, at the same time, resistant to removal attacks (i.e., robust). In the currently known watermarking systems, the above challenges are not always adequately resolved. We present an alternative audio watermarking technique that mitigates these and other related shortcomings. The system is referred to as modified audio signal keying (MASK). In MASK, the short-time envelope of the audio signal is modified in such a way that the change is imperceptible to the human listener. The MASK system can easily be tailored for a wide range of applications. Moreover, informal experimental results show that it has a good robustness and audibility behavior.     

Investigating the performance of satellite and reanalysis rainfall products at monthly timescales across different rainfall regimes of Ethiopia

Continuous availability of a variety of satellite and reanalysis rainfall products have triggered the use of such products as an alternate source of rainfall data in sparsely gauge networked areas. However, before utilizing them a detailed validation of these datasets are essential to have some level of guarantee. In many parts of Africa in general and most parts of Ethiopia particularly in the lowland areas, gauge stations are very sparse and unevenly distributed. In addition, due to the nature of complex topography and geographical location, Ethiopian rainfall shows high variability both temporally and spatially. In view of the above, the present study is aimed at statistically evaluating such rainfall products across different rainfall regimes (regions with different rainfall characteristics as defined by National Meteorological Agency (NMA) of Ethiopia). In the current study, five satellite and two reanalysis rainfall products such as African Rainfall Climatology version 2 (ARC2), Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT), Tropical Rainfall Measuring Mission-3B43 version 7 (TRMM 3B43v7), Climate Prediction Center Morphing Technique (CMORPH), Climate Hazards Group Infrared Precipitation with Stations version 2 (CHIRPSv2), the Climate Forecast System Reanalysis (CFSR) and the European Center for Medium Range Weather Forecast Reanalysis (ERA-Interim) are considered based on their spatial coverage, spatial resolution, temporal resolution, latency period and length of data records. Evaluation is done at monthly and seasonal time scales against the observed gauge rainfall data provided by the National Meteorological Agency of Ethiopia across entire Ethiopia in two different manners, first by considering the entire country as one homogeneous unit and secondly in a distributed manner across the three rainfall regimes of Ethiopia. The obtained results show that: (i) CHIRPSv2 and TRMM 3B43v7 show better performance during June to September (the main rainfall season) and during February to May (the smaller rainfall season) in regimes 1 and 2. (ii) In regime 3 these products show good performance from October to November (smaller rainy season of this regime) and March to May (main rainy season of this regime); (iii).CMORPH, TAMSAT and ARC2 show moderate performance in all three regimes; (iv) CFSR and ERA-Interim exhibit poor performance in all rainfall regimes. Overall, the detailed analysis of statistical evaluation results of the rainfall products at monthly timescale shows that CHIRPSv2 performs comparatively better than the other tested rainfall products across all rainfall regimes. However, the best performance of CHIRPSv2 is obtained in regime 2 followed by regime 1 and regime 3.     

3D Scaffolds to Study Basic Cell Biology

Mimicking the properties of the extracellular matrix is crucial for developing in vitro models of the physiological microenvironment of living cells. Among other techniques, 3D direct laser writing (DLW) has emerged as a promising technology for realizing tailored 3D scaffolds for cell biology studies. Here, results based on DLW addressing basic biological issues, e.g., cell‐force measurements and selective 3D cell spreading on functionalized structures are reviewed. Continuous future progress in DLW materials engineering and innovative approaches for scaffold fabrication will enable further applications of DLW in applied biomedical research and tissue engineering.     

Review of the natural conditions and anthropogenic threats to the Ethiopian Rift Valley rivers and lakes

This review article considers the lakes found within the Ethiopian Rift Valley system and the rivers draining from the highlands towards them. The Ethiopian Rift Valley system extends in a north‐east–south‐west direction, bisecting the Ethiopian highlands. They differ in sizes and hydrological and hydrogeological settings. The lakes support a variety of flora and fauna, mainly fishes, reptiles, birds and mammals. The lakes and their feeder rivers, however, are currently being subjected to more human use pressure than previously experienced, attributable mainly to water abstractions for irrigation, household water supply and industrial activities in the catchments. Soil erosion, primarily attributable to the lack of soil conservation practices on farm lands, overgrazing and deforestation, along with unregulated irrigation practices, are generating large quantities of silt reaching the lakes. Improper utilization of water and land resources in the catchments, along with population growth and climate change impacts, is escalating the threats to the health of the lake and river ecosystems. As a result, some of the lakes exhibited reduced water levels and increased salinity. This article reviews the major environmental changes happening to these lakes and their feeder rivers. Visits were made to the area to update previous information while also consolidating the trends of change in the aquatic systems and the watersheds attributed to intense development practices.     

Integrated OBF-NN models with enhanced extrapolation capability for nonlinear systems

This paper proposes a nonlinear system identification using parallel linear-plus-neural network models that provide more accurate predictions on the process behavior even on extrapolated regions. For this purpose, a residuals-based identification algorithm using parallel integration of linear orthonormal basis filters (OBF) and neural networks model is developed and analyzed under range extrapolations. Results on the van de Vusse reactor case study show enhanced extrapolation capability when compared to the conventional neural network (NN) and the series Wiener-NN models.