One of the most important components of pavement management systems is predicting the deterioration of the network through performance models. The accuracy of the prediction model is important for prioritizing maintenance action. This paper describes how the accuracy of prediction models can have an effect on the decision-making process in terms of the cost of maintenance and rehabilitation activities. The process is simulating the propagation of the error between the actual and predicted values of pavement performance indicators. Different rate of error (10%, 30%, 50%, 70%, and 90%) was added into the result of prediction models. The results showed a strong correlation between the prediction models’ accuracy and the cost of maintenance and rehabilitation activities. The cost of treatment (in millions of dollars) over 20 years for five different scenarios increased from ($54.07–$92.95), ($53.89–$155.48), and ($74.41–$107.77) for asphalt, composite, and concrete pavement types, respectively. Increasing the rate of error also contributed to the prediction model, resulting in a higher benefit reduction rate.

This paper describes the process and outcome of deterioration modeling for three different pavement types (asphalt, concrete, and composite) in the state of Iowa. Pavement condition data is collected by the Iowa Department of Transportation (DOT) and stored in a Pavement-Management Information System (PMIS). In the state of Iowa, the overall pavement condition is quantified using the Pavement Condition Index (PCI), which is a weighted average of indices representing different types of distress, roughness, and deflection. Deterioration models of PCI as a function of time were developed for the different pavement types using two modeling approaches. The first approach is the long/short-term memory (LSTM), a subset of a recurrent neural network. The second approach, used by the Iowa DOT, is developing individual regression models for each section of the different pavement types. A comparison is made between the two approaches to assess the accuracy of each model. The results show that the LSTM model achieved a higher prediction accuracy over time for all different pavement types.

This paper describes the process and outcome of deterioration modeling for three different pavement types in the state of Iowa. Pavement condition data is collected by the Iowa Department of Transportation (DOT) and stored in a Pavement-Management Information System (PMIS). Typically, the overall pavement condition is quantified using the Pavement Condition Index (PCI), which is a weighted average of indices representing different types of distress, roughness, and deflection. Deterioration models of PCI as a function of time were developed for the different pavement types using two modeling approaches. The first approach is the Long/Short Term Memory (LSTM), a subset of a recurrent neural network. The second approach, used by the Iowa DOT, is developing individual regression models for each section of the different pavement types. A comparison is made between the two approaches to assess the accuracy of each model. The results show that while the individual regression models achieved higher prediction accuracy with respect to asphalt pavements, the LSTM model achieved a higher prediction accuracy over time for concrete and composite pavement types.

This paper is a tutorial review on important issues related to code-division multiple-access (CDMA) systems such as channel capacity, power control, and optimum codes; specifically, we consider optimum overloaded codes that achieve errorless transmission in the absence of noise for the binary and nonbinary cases. A survey of lower and upper bounds for the sum channel capacity of such systems is given in the presence and absence of channel noise. The asymptotic results for the channel capacity are also investigated. The channel capacity, errorless transmission codes, and power estimation for near-far effects are also explored. The emphasis of this tutorial review is on the overloaded CDMA systems.Keywords: Code Division Multiple Access (CDMA), Optimum codes, channel capacity bounds, near-far effects, power control I Introductioncode-division multiple access (CDMA) has been the most important multiple access technology for the 3rd generation GSM and American Cellular systems [1]. Optical CDMA systems have become an alternative multiple access for fiber optics and optical wireless systems [2][3][4].In CDMA systems, each user is assigned a unique code signature that consists of a number of chips. The signature length (also called chip rate) is defined as the number of chips in each signature code. Each user signature is multiplied by the respective data, and the transmitted vectors are added up in the common channel. The resultant vector is then observed at the received user end. In order to decode the received signal, the detector of the received user should know its own unique signature. These codes should be designed such that the cross-correlations between the code of the desired user and the codes of the other users are minimal.For the wireless case, the most well-known binary (Endnote a) code for the synchronous case is Hadamard orthogonal code that is appropriate for fully and underloaded CDMA systems. (Endnote b) But because of bandwidth limitation in the communication systems, we are interested in finding codes that can support more users than the chip rate (overloaded case). In the overloaded case, we cannot use Hadamard codes; Even random codes create interference that cannot be eliminated completely [5][6][7]. Optical orthogonal codes (OOC) [3,8] are not really orthogonal; however, using interference cancelation, we can remove interference completely. Most of the research in the overloaded case is related to code design and multi-access interference (MAI) cancelation in order to decrease the probability of error. Examples of these types of codes are pseudo random spreading [9,10], Welsh Bound Equality (WBE) codes with minimal total square correlation (TSC) [11][12][13][14], OCDMA [15][16][17], Multiple-OCDMA [18], and PN/ OCDMA [19] signature sets. None of the above codes guarantee errorless transmission in the absence of channel noise for overloaded CDMA systems. There are also some codes that are not designed upon cross-correlation and are designed such that they can provide one-to-one transfor...

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Background: Corticosteroids have been the mainstay of treatment for alopecia areata (AA). Recently, the 308-nm excimer laser has been proposed for treating AA. Objectives: To compare the efficacy and safety of excimer laser with intralesional corticosteroid (ILCS) in AA. Methods: Patients with at least two alopecic patches were randomly assigned to receive weekly excimer laser treatments or monthly injections of ILCS. Photographs and trichoscopy images were examined at baseline, the last treatment session, and after one month of follow-up. The hair regrowth score was evaluated on a 6-point scale.Results: Sixteen patients with 99 alopecic patches completed the study. At the last treatment session, the mean score of hair regrowth for the laser was significantly lower than the ILCS (p = 0.003). However, after a month of follow-up, the difference was not statistically significant (p = 0.148). Positive response in hair regrowth (≥50%) was achieved in 47% of laser-treated patches and 66% in ILCStreated ones. Four (25%) and 8 (50%) patients experienced severe adverse events of laser and ILCS, respectively. Conclusions: The excimer laser was safe and effective in AA. The effect of laser on hair regrowth might be delayed as compared with ILCS.

One of the most important components of pavement management systems is predicting the deterioration of the network through performance models. The accuracy of the prediction model is important for prioritizing maintenance action. This paper describes how the accuracy of prediction models can have an effect on the decision-making process in terms of the cost of maintenance and rehabilitation activities. The process is simulating the propagation of the error between the actual and predicted values of pavement performance indicators. Different rate of error was added into the result of prediction models. The results showed a strong correlation between the prediction models’ accuracy and the cost of maintenance and rehabilitation activities. Also, increasing the rate of error contribution to the prediction model resulting in a higher benefit reduction rate.

Objective: Acknowledging the key role of hardiness, importance of health and its various dimensions, the present study aimed to investigate the simultaneous relationship between hardiness components and spiritual health, burnout, and general health, among Baqiyatallah University of Medical Sciences staff. Method: 307 Baqiyatallah University of Medical Sciences’ staff in Tehran with at least five years work experience participated in this cross-sectional study using simple random sampling. Four questionnaires were used to evaluate the participants: the 28-item General Health Questionnaire (GHQ) to assess general mental problems with four subscales, 22-item Maslach Burnout Inventory (MBI) with two aspects (frequency and intensity) and three subscales of emotional exhaustion, depersonalization and personal accomplishment, the 20-item Spiritual Well Being Scale (SWBS) Questionnaire with two subscales of religious well-being and existential well-being and the 50-item Kobasa Hardiness Questionnaire to measure psychological hardiness with three subscales of control, commitment and challenge. At the end, two conceptual models which have shown effect of hardiness and its subscales on general health, Spiritual health and burnout were evaluated by path analysis. Results: According to the path analysis results, it was found that hardiness and its subscales, which were approved by univariable and multivariable analyses, had significant relationship with general health (direct effect: -0.525, P < 0.001), spiritual health (direct effect: 0.555, P < 0.001) and burnout (direct effect of frequency aspect: -0.523, P < 0.001). Thus, by increasing hardiness and its subscales, spiritual health increases while symptoms of illness and burnout decrease. Conclusion: Spiritual health increases as hardiness and its subscales increase as well; therefore, symptoms of illness and burnout decrease as hardiness and its subscales increase.