Predetermined Threshold Value Research Articles

A Verifiable Multi-Secret Sharing Scheme for Hierarchical Access Structure

Sharing confidential information is a critical concern in today’s world. Secret sharing schemes facilitate the sharing of secrets in a way that ensures only authorized participants (shareholders) can access the secret using their allocated shares. Hierarchical secret sharing schemes (HSSSs) build upon Shamir’s scheme by organizing participants into different levels based on priority. Within HSSS, participants at each level can reconstruct the secret if a specified number, denoted as the threshold value (t), or more of them are present. Each level has a predetermined threshold value. If the number of participants falls below the threshold at any level, higher-level participants must be involved in reconstructing the secret at lower levels. Our paper proposes schemes that implement hierarchical access structures and enable the sharing of multiple secrets. Additionally, our proposed scheme includes share verification. We have analyzed potential attacks and demonstrated the scheme’s resistance against them. Through security analysis and comparison with existing schemes, we highlight the novelty and superiority of our proposed approach, contributing to advancements in secure information-sharing practices.

Exposure to Traumatic Events at Work, Post-Traumatic Symptoms, and Professional Quality of Life among Italian Midwives: A Cross-Sectional Study.

The aim of this study is to investigate the potential occurrence of post-traumatic stress disorder (PTSD) symptoms, following exposure to traumatic events, in Italian midwives and their consequent influence on the quality of midwives' professional lives. In addition, data were collected on the major traumatic events described by midwives. A cross-sectional study related the socio-demographic characteristics of 286 midwives with the scores obtained on two assessment scales, one for post-traumatic stress disorder (IES-R) and the other for quality of life (ProQOL V). The percentage of midwives who obtained a score higher than the predetermined threshold value in both questionnaires was noted, and the correlations that emerged were highlighted. Through this qualitative method, their significant work-related traumatic events were investigated to finally detect the prevalence percentage of each category. The proportion of midwives scoring higher than 33 on the IES-R scale, indicating a higher likelihood of PTSD, was 48.6%. Freelancers or outpatient clinic midwives had lower mean IES scores (p = 0.049). A significant inverse correlation was observed between age and IES-R score and between the compassion satisfaction subscale and time since completing education (p = 0.028). A comparison between the IES-R and ProQOL scales showed a statistically significant correlation (p < 0.001), in particular, between the burnout (BO) (p < 0.001) and secondary traumatic stress (STS) (p < 0.001) subscales. The thematic categorization of traumatic events included mother/child death, mother/child medical complications, relational problems with patients or team members, and organizational problems/medical staff's inexperience. The emerging data may confirm the data in the literature, namely those showing that midwives are prone to developing work-related PTSD, particularly due to their exposure to traumatic events such as maternal and neonatal death.

The relationship and diagnostic efficacy of N-terminal propeptide type III collagen in pathological changes associated with non-alcoholic steatohepatitis.

This study delves into the role of N-terminal propeptide type III collagen (PIIINP) in the diagnosis and management of liver pathological changes associated with non-alcoholic steatohepatitis (NASH). We collected baseline information, pathological data, and serum PIIINP levels of 168 patients diagnosed with non-alcoholic fatty liver disease (NAFLD) via ultrasound imaging in our hospital. Based on the non-alcoholic fatty liver disease activity score (NAS), patients with different NAFLD patterns were divided into a Definite NASH group and a Not/borderline group. Differences in PIIINP levels and pathological features between the two groups were compared and analyzed. The diagnostic value of PIIINP for NASH was evaluated using the receiver operating characteristic (ROC) curve. Patients with NASH exhibited significantly higher values of homeostatic model assessment for insulin resistance (HOMA-IR), fibrosis biomarker fibrosis-4 (FIB-4), aminotransferase-to-platelet ratio index (APRI), and serum PIIINP levels than those classified as Not/borderline. A marked increase in the serum concentrations of PIIINP was observed with the severity of fatty degeneration, lobular inflammation, and hepatocellular ballooning. The AUC of PIIINP for diagnosing definite NASH was 0.766 (95% CI: 0.694, 0.839), APRI was 0.634 (95% CI: 0.549, 0.718), and FIB-4 was 0.621 (95% CI: 0.534, 0.708). The AUC of PIIINP for diagnosing definite NASH was significantly higher than that of APRI and FIB-4 (all p<0.05). Utilizing the predetermined threshold values for diagnostic parameters, the PIIINP measure demonstrated a sensitivity of 71.6% and a specificity of 73.6% in diagnosing definitive NASH when its value exceeded 7.72 ng/dL. This yielded a Youden index of 0.45. Similarly, when the APRI measure exceeded 0.21, it exhibited a sensitivity of 60.5% and a specificity of 63.2%, resulting in a Youden index of 0.24. Moreover, when the FIB-4 index surpassed 0.26, it showed a sensitivity of 46.9% and a specificity of 79.3%, culminating in a Youden index of 0.26. NASH patients in this study exhibited significantly elevated PIIINP serum levels, which were closely associated with hepatocyte pathological changes. PIIINP demonstrated superior competence in diagnosing NASH than APRI and FIB-4 and thus offers a viable alternative for the clinical diagnosis of NASH.

Investigation of Novel Solid Dielectric Material for Transformer Windings.

Improvement techniques aimed at enhancing the dielectric strength and minimizing the dielectric loss of insulation materials have piqued the interest of many researchers. It is worth noting that the electrical breakdown traits of insulation material are determined by their electrochemical and mechanical performance. Possible good mechanical, electrical, and chemical properties of new materials are considered during the generation process. Thermoplastic polyurethane (TPU) is often used as a high-voltage insulator due to its favorable mechanical properties, high insulation resistance, lightweight qualities, recovery, large actuation strain, and cost-effectiveness. The elastomer structure of thermoplastic polyurethane (TPU) enables its application in a broad range of high-voltage (HV) insulation systems. This study aims to evaluate the feasibility of using TPU on transformer windings as a solid insulator instead of pressboards. The investigation conducted through experiments sheds light on the potential of TPU in expanding the range of insulating materials for HV transformers. Transformers play a crucial role in HV systems, hence the selection of suitable materials like cellulose and polyurethane is of utmost importance. This study involved the preparation of an experimental setup in the laboratory. Breakdown tests were conducted by generating a non-uniform electric field using a needle-plane electrode configuration in a test chamber filled with mineral oil. Various voltages ranging from 14.4 kV to 25.2 kV were applied to induce electric field stress with a step rise of 3.6 kV. The partial discharges and peak numbers were measured based on the predetermined threshold values. The study investigated and compared the behaviors of two solid insulating materials under differing non-electric field stress conditions. Harmonic component analysis was utilized to observe the differences between the two materials. Notably, at 21.6 kV and 25.2 kV, polyurethane demonstrated superior performance compared to pressboard with regards to the threshold value of leakage current.

Daya Jerap dan Efisiensi Adsorpsi Ion Tembaga Menggunakan Biosorben Serat Daun Nanas (Ananas comosus)

One of the essential heavy metals that living things require in small amounts is the copper ion (Cu2+). However, the presence of dissolved copper metal is toxic to organisms and can pollute the environment if it exceeds a predetermined threshold value. Pineapple leaf waste with high cellulose content can be used as a copper ion biosorbent. This research studied the effect of pineapple leaf fiber particle size and contact time at the adsorption of copper ions on the adsorption capacity and effectiveness of pineapple leaf fiber biosorbents in adsorbing copper ions. The research began with cleaning, drying, and grinding pineapple leaves. Then, the activation process was carried out for 70 minutes using 9% NaOH. The adsorption process of copper ions was carried out using 1 g of pineapple leaf fiber biosorbent based on the varying sizes of the biosorbent particles size of the biosorbent -40/+60; -60/+80; and -80/+100 mesh and contact time 30; 60; 90; and 120 minutes at a stirring speed of 90 rpm and a concentration of 10 ppm copper ion adsorbate to measure the remaining copper ions in solution after the adsorption process. The activated pineapple leaf fiber biosorbent has a water content of 14.3%; 12.4%; and 11.2% for each biosorbent particle size -40/+60; -60/+80; and -80/+100 meshes. The highest adsorption effectiveness was 94.93%, which occurred at a contact time of 90 minutes with a biosorbent particle size of -80/+100 mesh and an adsorption value of 0.94 mg/g.

Optimal entanglement generation in GHZ-type states

The entanglement production is key for many applications in the realm of quantum information, but so is the identification of processes that allow to create entanglement in a fast and sustained way. Most of the advances in this direction have been circumscribed to bipartite systems only, and the rate of entanglement in multipartite system has been much less explored. Here we contribute to the identification of processes that favor the fastest and sustained generation of tripartite entanglement in a class of 3-qubit GHZ-type states. By considering a three-party interaction Hamiltonian, we analyze the dynamics of the 3-tangle and the entanglement rate to identify the optimal local operations that supplement the Hamiltonian evolution in order to speed-up the generation of three-way entanglement, and to prevent its decay below a predetermined threshold value. The appropriate local operation that maximizes the speed at which a highly-entangled state is reached has the advantage of requiring access to only one of the qubits, yet depends on the actual state of the system. Other universal (state-independent) local operations are found that conform schemes to maintain a sufficiently high amount of 3-tangle. Our results expand our understanding of entanglement rates to multipartite systems, and offer guidance regarding the strategies that improve the efficiency in various quantum information processing tasks.

Bicluster Analysis of Cheng and Church's Algorithm to Identify Patterns of People's Welfare in Indonesia

Biclustering is a method of grouping numerical data where rows and columns are grouped simultaneously. The Cheng and Church (CC) algorithm is one of the bi-clustering algorithms that try to find the maximum bi-cluster with a high similarity value, called MSR (Mean Square Residue). The association of rows and columns is called a bi-cluster if the MSR is lower than a predetermined threshold value (delta). Detection of people's welfare in Indonesia using Bi-Clustering is essential to get an overview of the characteristics of people's interest in each province in Indonesia. Bi-Clustering using the CC algorithm requires a threshold value (delta) determined by finding the MSR value of the actual data. The threshold value (delta) must be smaller than the MSR of the actual data. This study's threshold values are 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8. After evaluating the optimum delta by considering the MSR value and the bi-cluster formed, the optimum delta is obtained as 0.1, with the number of bi-cluster included as 4.

Solution of discrete time–cost trade-off problem with adaptive search domain

PurposeExact solution of time–cost trade-off problem (TCTP) by the state-of-the-art meta-heuristic algorithms can be obtained for small- and medium-scale problems, while satisfactory results cannot be obtained for large construction projects. In this study, a hybrid heuristic meta-heuristic algorithm that adapts the search domain is developed to solve the large-scale discrete TCTP more efficiently.Design/methodology/approachMinimum cost slope–based heuristic network analysis algorithm (NAA), which eliminates the unfeasible search domain, is embedded into differential evolution meta-heuristic algorithm. Heuristic NAA narrows the search domain at the initial phase of the optimization. Moreover, activities with float durations higher than the predetermined threshold value are eliminated and then the meta-heuristic algorithm starts and searches the global optimum through the narrowed search space. However, narrowing the search space may increase the probability of obtaining a local optimum. Therefore, adaptive search domain approach is employed to make reintroduction of the eliminated activities to the design variable set possible, which reduces the possibility of converging into local minima.FindingsThe developed algorithm is compared with plain meta-heuristic algorithm with two separate analyses. In the first analysis, both algorithms have the same computational demand, and in the latter analysis, the meta-heuristic algorithm has fivefold computational demand. The tests on case study problems reveal that the developed algorithm presents lower total project costs according to the dependent t-test for paired samples with α = 0.0005.Research limitations/implicationsIn this study, TCTP is solved without considering quality or restrictions on the resources.Originality/valueThe proposed method enables to adapt the number of parameters, that is, the search domain and provides the opportunity of obtaining significant improvements on the meta-heuristic algorithms for other engineering optimization problems, which is the theoretical contribution of this study. The proposed approach reduces the total construction cost of the large-scale projects, which can be the practical benefit of this study.

ANALYSIS OF AMBIENT AIR QUALITY AND GENSET EMISSIONS AT PT. REKSO NASIONAL FOOD (MC. DONALDS) GORONTALO CITY

Air, as one of the components of the environment, is the most essential requirement in supporting life. Metabolism in the bodies of living things cannot occur without air oxygen. Air can be grouped into outdoor air and indoor air. Indoor air quality dramatically affects human health because almost 90% of human life is indoors. Indoor air quality is a problem that needs attention because it will affect human health. The emergence of poor indoor air quality is generally caused by several things, namely lack of air ventilation (52%), sources of indoor pollution (16%), outdoor pollution (10%), microbes (5%), and building materials (4 %). ), others (13%). Sources of air pollution can also come from household activities, starting from the kitchen in the form of smoke. According to several studies, air pollution from kitchens contributes significantly to ARI. The novelty of this research is that it examines ambient air quality and generator emissions. The study aimed to analyze Ambient Air Quality and Generator Emissions at PT Rekso National Foods (MC Donalds) in Gorontalo City. The results showed that the surrounding air quality still met the requirements, both CO, TSP, PM10, PM 2.5 and generator emissions. However, the noise factor still does not meet the threshold standard. This study concludes that all variables still meet predetermined threshold values except for the noise variable, which exceeds the threshold value.

Grinding burn classification with surface Barkhausen noise measurements

Industrial Barkhausen noise (BN) measurements are commonly utilized for final quality control after machining operations such as grinding to point out grinding burns. Grinding burns might compromise the final use and fatigue lifetime of the ground component. The industrial BN method itself is based on a pre-determined threshold value of the BN rootmean-square value (RMS). Elevated RMS values indicate detrimental changes in the component. Usually, the evaluation of grinding burn severity is not carried out. In this study, real ground cylindrical samples were collected that were rejected based on an industrial quality control with a BN unit. A more detailed BN analysis was carried out for 41 individual grinding burn locations followed by X-ray diffraction based residual stress (RS) surface measurements and residual stress and diffraction peak full-width-at-halfmaximum (FWHM) depth profiles. K-means clustering was applied to profiles to label the data points related to grinding burns of different severity. Three classes of grinding burns were identified and verified by micrographs and hardness. A linear discriminant classification model was then identified between the surface BN measurement features and labeled data points. The classification results were reasonable with about 80 % classification accuracy at worst. They showed that the classes identified can be detected with the surface BN measurements. Thus, the approach presented in this paper shows great potential in the practical use of BN measurement where grinding burns can be detected and evaluated with a surface BN measurement.

A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy

This study focuses on faults in the thrusters of unmanned surface vehicles, which are fatal to the integrity of their missions. As for the fault conditions, the breakage of the thruster blade and the entanglement of floating objects were selected, and a data-driven method was used to diagnose the faults. In the data-driven method, it is important to select the sensitive fault feature. In this study, vibration, current consumption, rotational speed and input voltage were selected as fault features. An experiment was conducted in an engineering water tank to obtain and analyze data on fault conditions to verify the validity of the selected features. In addition, a new fault diagnosis algorithm combining principal component analysis and Shannon entropy was applied for analyzing the correlations among fault features. This algorithm reduces the dimensionality of data while preserving their structure and characteristics, and diagnoses faults by quantifying entropy values. A fault is detected by comparing the entropy value and a predetermined threshold value, and is diagnosed by analyzing the entropy value and visualized 2D or 3D principal component results. Moreover, the fault diagnosis performance of the unmanned surface vehicle’s thruster was verified by analyzing the results for each fault condition.

Probabilistic criteria for time-series predictability estimation

Assessing the time series predictability is necessary for forecasting models validating, for classifying series to optimize the choice of the model and its parameters, and for analyzing the results. The difficulties in assessing predictability occur due to large heteroscedasticity of errors obtained when predicting several series of different nature and characteristics. In this work, the internal predictability of predictive modeling objects is investigated. Using the example of time series forecasting, we explore the possibility of quantifying internal predictability in terms of the probability (frequency) of obtaining a forecast with an error greater than some certain level. We also try to determine the relationship of such a measure with the characteristics of the time series themselves. The idea of the proposed method is to estimate the internal predictability by the probability of an error exceeding a predetermined threshold value. The studies were carried out on data from open sources containing more than seven thousand time series of stock market prices. We compare the probability of errors which exceed the allowable value (miss probabilities) for the same series on different forecasting models. We show that these probabilities differ insignificantly for different forecasting models with the same series, and hence, the probability can be a measure of predictability. We also show the relationship of the miss probability values with entropy, the Hurst exponent, and other characteristics of the series according to which the predictability can be estimated. It has been established that the resulting measure makes it possible to compare the predictability of time series with pronounced heteroscedasticity of forecast errors and when using different models. The measure is related to the characteristics of the time series and is interpretable. The results can be generalized to any objects of predictive modeling and forecasting quality scores. It can be useful to developers of predictive modeling algorithms, machine learning specialists in solving practical problems of forecasting.

An advanced short-circuit protection scheme for a bipolar DC microgrid

In order to provide quick and accurate fault detection in a DC microgrid, a new protection strategy is developed in this study. It is based on the multi-resolution analysis of travelling waves. The multi-resolution analysis of travelling waves is carried out using discrete wavelet transform (DWT). The dyadic filter bank theory is used to obtain the DWT coefficient of the voltage travelling wave. The energy of the DWT coefficient is then determined, and the fault will be identified if this energy content exceeds the predetermined threshold value. Initially, the efficacy of the proposed algorithm is verified on a 500 V ring-type bipolar DC microgrid test model, which is developed with MATLAB/Simulink environment. Then the results obtained were later validated with the real-time simulator Opal-RT. The various simulation results indicate that the proposed algorithm can successfully be implemented in both modes, i.e., grid-integrated and islanded. It can also provide protection against high resistance fault (HRF) up to 900 Ω in grid-integrated mode, and up to 1,300 Ω in islanded mode. The operating time of the proposed algorithm is also quite fast, i.e., 0.64 m.

Intelligent splicing technology of off-axis aspheric mirrors based on Zygo interferometer

With the increasing size of the aspheric, manufacturing and testing technologies face enormous challenges. A Zygo interferometer-based intelligent splicing technology for off-axis aspherical mirrors is proposed in this paper, which provides a "small to big" manufacturing strategy for large aspherical mirrors. In the proposed method, a Zygo interferometer, a splicing adjustment platform, and a computer-integrated algorithm are combined to achieve closed-loop adjustment of the off-axis aspherical mirrors. Taking any one of the off-axis aspherical sub-mirrors as a reference, the point cloud information on the surfaces of all off-axis sub-mirrors is obtained by combining the optimal spherical wavefront data with the Zygo interferometer data. The splicing adjustment platform performs positional corrections to the to-be-adjusted off-axis aspherical mirrors using the calculated point cloud registration parameters as the adjustment amount. The iterative operation of point cloud alignment and splicing platform adjustment is done until the positional deviation of the adjusted off-axis aspherical mirror surface reaches a predetermined threshold value, at which point the adjustment stops. The experimental verification employed two off-axis aspherical sub-mirrors with a 100 mm aperture that belonged to the same aspherical surface. The result shows that large aspherical mirrors can be made quickly and effectively with intelligent splicing technology.

A Sound-Based Monitoring and Evaluation System for Small-Scale Dairy Operations

Continuous monitoring of livestock operations is vitally important for a sustainable production system. Monitoring systems based on cameras are not sufficient in livestock barns since they require visual inspection and ignore vocal conditions within the barn. These systems are also quite expensive for most small family operations. A prototype device that costed $ 470 was developed to remotely monitor the barn based on sound sensors (microphones) data. This device also warns the operator by sending an SMS at sound intensities exceeding the predetermined durations and threshold values. It also makes it possible to listen to the barn by phone if needed. The device and associated web database was tested in this study. The main challenge was the determination of threshold values at which sensors are to generate warning SMS messages. As a method, Z-score of 2.33 which corresponds to area left of the 99% of the normally distributed data curve was determined representing the highest values with a possibility of 1% observation for each sensor. An average value of 97 dB was determined to be a threshold suggestion for future studies. A customizable web-based MySQL database was created to monitor and evaluate the long term data collected via the system.

A Prospective Comparative study between the Tobin Index and Integrative Weaning Index to predict the outcome of trials of weaning from mechanical ventilation

Weaning indices that are simple to derive and apply have been developed for predicting the likeliness of a patient to tolerate weaning. This study compared the Tobin Index and Integrative Weaning Index (IWI) to determine the better predictor for a successful weaning. This was a prospective observational study done on 60 patients on mechanical ventilation meeting the defined criteria whose physicians considered clinically stable to be given a weaning trial. Pre-determined threshold values of the Tobin index and IWI for determining a successful or unsuccessful weaning were used. A daily assessment was made with calculation of the Tobin index in GroupTI for extubation when Tobin Index≤105 breaths/min/L and IWI in GroupIWI when IWI&gt;25 ml/min/cm H2O, with each group having 30 patients. A spontaneous breathing trial of 2 hours was given. The variables compared between the two groups were the outcome of weaning, duration of mechanical ventilation, ICU stay and hospital stay in days, and mortality. Comparison was done using Chi square test / Fisher’s exact test and Mann Whitney test. There was no significant comparison in terms of weaning success or failure, durations of mechanical ventilation, hospital stay and mortality. The duration of ICU stay was less and of statistical significance in the IWI group. However, the percentage of successful weanings while using the indices, both considered separately, was higher than the failures. Both the predictive indices studied did not successfully predict weaning outcomes in all the cases thus limiting their use in clinical practice. Key words: successful weaning, mechanical ventilation, weaning predictors, Tobin index, integrated weaning index

Sports Video Panorama Synthesis Technology Based on Edge Computing and Video Shot Boundary Detection

The development requirements for sports panorama synthesis technology, which rely on modern network technology, abandon traditional basketball training forms, and make effective use of the application and development of video panorama technology in the actual training process, are not only an important response to the current characteristics of students’ physical education learning and physical and mental growth but also an important response to the current characteristics of students’ physical education learning and physical and mental growth. In sports video analysis technology, a sports video panorama is a technical tool that converts an action video into a static action panorama to achieve the effect of action freezing and make overall analysis and mastery of the action easier. Shot segmentation is the foundation of hierarchical video structure, and it necessitates the accurate detection of all types of complex edited shot boundaries, as well as the effective distinction of motion changes in shots, in order to avoid shot boundary recognition being hampered. The synthesis technology for sports video panorama is investigated in this paper using edge computing and video shot boundary detection. After obtaining the boundary feature that describes the video shot, a comparison of this feature with a predetermined threshold value can be used to determine whether there is shot shear.

Image Masking and Enhancement System for Melanoma Early Stage Detection

Early stage melanoma detection (ESMD) is crucial as late detection kills. Computer aided diagnosis systems (CADS) integrated with high level algorithms are major tools capable of ESMD with high degree of accuracy, specificity, and sensitivity. CADS use the image and the information within the pixels of the image. Pixels’ characteristics and orientations determine the colour and shapes of the images as the pixels and associated environment are closely interrelated with the lesion. CADS integrated with Convolutional Neural Networks (CNN) specifically play a major role for ESMD with high degree of accuracy. The proposed system has two steps to produce high degree of accuracy; the first is the Fuzzy-Logic (FL) based maskProd and pixel Enhancement vectors and the second is integration with deep CNN algorithms. The vectors, maskProd and pixel Enhancement, are based on lesion masking and image enhancement via fuzzy logic as hSvMask production has three inputs; sChannel, entropyFilter and distanceVector. The originality of this paper is based on the regional enhancement of each pixel of the image by fuzzy-logic. To change the brightness of an image by the conventional methods (CM), it is required to multiply the value of all and each pixels of the image by the same constant or weight value in order to generate new values of the pixels. But in the proposed method, FL determines a special and specific constant or weight value for every single pixel of the image; called a variable weight value. Secondly via conventional or classical methods of image processing, mask production is realized based on the threshold value predetermined, and this threshold value decides whether the image lesion is melanoma or not. CM use constant weight values but in the method suggested, FL determines a specific variable weight value for every single pixel of the image. Secondly CMs realize the mask production using predetermined threshold values, but proposed method suggests a new way capable of detecting pixels in the lesion-free regions, to do that, a system using FL was developed for each and every single pixel via the data obtained from three sources: the s-channel of the HSV (Hue, Saturation and Value) colour space, distance vector, and image entropy vector. The proposed method and its integration with algorithms; such as AlexNet, GoogLeNet, Resnet18, VGG-16, VGG-19, Inception-V3, ShuffleNet and Xception, detected the melanoma with an accuracy of 86.95, 85.60, 86.45, 86.70, 85.47, 86.76, 86.45 and 85.30 percentages, respectively.

Development of a general purpose First-In-First-Out (FIFO) core

First-In-First-Out cores (FIFOs) are memory storage elements that are used in digital systems for buffering data through a system for later processing. This paper presents the design and implementation of a General Purpose FIFO Core which allows adjustment of the capacity along with the size of each data word. Status indicators were provided to indicate whether or not the FIFO was empty, half-full, or full. The number of data words stored in the FIFO was also indicated by designated output ports of the system. A status flag was also available to indicate when the size reached a predetermined threshold value. A separate interface was provided that allowed the data at any address to be accessed for reading. It was also possible to write a data word to the back of the FIFO while another data word was read from the front simultaneously. The FSM-D architectural model was applied to the design of the FIFO Core and the implementation was done in VHDL using the Xilinx ISE 14.7. The implemented core was simulated using ISim Logic Simulator of the Xilinx ISE platform, and it was found that the system core behaved as specified by the test cases.

Beyond average population density: Measuring sprawl with density-allocation indicators

Average population density has been a traditional urban sprawl indicator, but it is insufficient to encapsulate its detrimental impacts. We demonstrate that cities of identical average population density may be subject to very different levels of car dependency, CO2 emissions and public budget deficits. To capture this, we shed light into the properties of two “density-allocation” indicators: the percentage of urban land in which population density falls below a predetermined threshold value, and the fraction of the population living in such areas. Using unique data, we measure these indicators in more than 1100 urban areas of 29 OECD countries and at three time points: 1990, 2000 and 2014. We identify four different patterns for the coevolution of average population density and these indicators. The results show that densification of urban cores concurs with rapid suburbanization in many cities. We perform intertemporal cross-city and cross-country analyses and discuss the multiple policy implications emerging from them.