---

Rotating machines in particular induction electrical machines are important industry instruments. In manufacturing, electrical motors are exposed to many damages, and this causes stators and rotors not to work correctly. In this paper we addressed modal analysis and an intelligent method to detect motor load condition and also the stator faults such as turn-to-turn and coil-to-coil faults using motor vibration analysis. A three-phase induction motor with a special winding was used to create the faults artificially. The vibration signal of motor in different states such as working without fault, with various faults and with various loads was acquired. Some spectral analysis was done using the spectrum and the spectrograph of vibration signals and differences due to different states of motor were observed. Suitable features such as Linear Prediction Cepstral Coefficients and Fourier Transform Filter Bank Coefficients were extracted from vibration signals and were then applied to non-supervised (SOM) and supervised (LVQ) neural networks in order to classify motor faults and its load condition. Many experiments were conducted to evaluate the effect of neural network type, type and length of feature vector, length of training signal etc. In brief, using SOM and LVQ neural networks, 20 element Filter Bank feature vectors, and 600ms of the training data, performance of 93.6% and 94.2% were obtained for load and fault detection respectively.

---

Aims: Hepatitis B Virus (HBV) has infected more than million hundreds of people worldwide. Hence, a high rate of morbidity and mortality caused by liver-related diseases is due to HBV infection. However, a strong and effective treatment should be based on an accurate and correct diagnostic method. Hence, the present research provided a multidimensional study comparing and analyzing patients’ molecular and serological tests results.
Materials & Methods: In this research, the HBV DNA molecular tests results were studied by examining patients’ gender, age, and HBsAg strip results.
Findings: Among the female patients (29 persons) studied in this research, 55.1% were positive for HBV DNA and HBsAg strip tests, and 17.3% were negative for both tests. Also, among the male patients  (44 persons), 65.9% were positive, and 6.8% were negative for both tests.
Conclusion: The present study results shed light on the correlation between the HBV DNA and HBsAg tests. Also, the significance of HBV DNA tests was highlighted for particular diagnostic purposes and for the differentiation and interpretation of the pathophysiological conditions of patients with hepatitis B.

---

Background: The current narrative review aims to describe microbial agents causing pneumonia briefly. In addition, the ongoing review tries to introduce the diagnostic methods from biochemical to molecular tests used routinely and the promising molecular methods which will be used in near future.
Methods: PubMed was searched for all review and original articles related to the lung infection. Studies providing insights into clinical symptoms, microbiology, risk factors, and diagnosis were included.
Rasult & Conclusion: Untreated respiratory infections are one of the most common health care problems worldwide. We tried to provide a collective view of new aspects of bacteriology and diagnosis methodology of lung infection detection.

---

The genus Latibulus Gistel, 1848 (Hymenoptera: Ichneumonidae) is taxonomically reviewed in Iran. Specimens were collected using Malaise traps in the Isfahan province, during 2013–2015. Two species, Latibulus argiolus (Rossi, 1790) (spring form) and Latibulus orientalis (Horstmann, 1987) (summer form) are identified, of which L. orientalis is a new record for the fauna of Iran. In addition, L. argiolus is recorded from central part of Iran (Isfahan) for the first time. The geographical distribution of the recorded species in relation to the overall knowledge in the target area and adjacent regions is also discussed.

---

The genera Psilocera Walker and Stinoplus Thomson (Hymenoptera: Pteromalidae, Pteromalinae) are recorded for the first time from Iran. They are represented by two species, Psilocera obscura Walker, 1833 and Stinoplus etearchus (Walker, 1848), respectively. First species was collected from central part (Isfahan Province) of Iran, by the Malaise-trap and the second was collected from North East (North Khorasan Province) by sweeping net. Brief diagnosis, with illustrations of the morphological characters are provided for each species.

---

Different methods for detecting broken bars in induction motors can be found in literature. Many of these methods are based on evaluating special frequency magnitudes in machine signals spectrums. Current, power, flux, etc are among these signals. Frequencies related to broken rotor fault depend on slip. In industrial environment due to some phenomena - such as load oscillation, other faults and disturbances – obtrusive frequency components appear in the vicinity of fault components; therefore, correct diagnosis of fault depends on accurate determination of motor velocity and slip. The traditional methods typically require several sensors that should be pre-installed in some cases. This paper presents a diagnosing method based on vibration spectrum. Motor velocity oscillation due to broken rotor causes frequency components at twice slip frequency (2sf) difference around speed frequency in vibration spectrum. Speed frequency and its harmonics as well as twice supply frequency, can easily and accurately be found in vibration spectrum, therefore the motor slip can be computed. Now components related to rotor fault can be found. Evaluation of these fault components magnitudes can be a good measure for fault diagnosis.

---

Males of the subgenus Eofoersteria Mathot (Hym., Mymaridae, Camptoptera Foerster) are diagnosed, described, and illustrated for the first time, based on examination of specimens from Tamil Nadu and from photographs of the male paratype of Camptoptera matcheta Subba Rao from Karnataka. New distributional records of C. (Eofoersteria) manipurensis (Rehmat & Anis) from Karnataka and Kerala states of India are documented.

---

Backgrounds: Delay in the diagnosis of tuberculosis (TB) leads to poor response to treatment and the disease transmission to susceptible individuals. The Xpert MTB/RIF assay efficiently detects Mycobacterium tuberculosis (MTB). The present study aimed to compare acid-fast bacilli (AFB) microscopy, culture, and Xpert MTB/RIF assay in the diagnosis of pulmonary and extrapulmonary tuberculosis cases.
Materials & Methods: This retrospective study was conducted in the Department of Microbiology, Government Medical College, Srinagar, India over 18 months from February 2019 to July 2020. Samples were processed and evaluated using AFB microscopy, culture, and Xpert MTB/RIF assay.
Findings: Among the 1862 samples evaluated, 224 samples were found to be positive using AFB microscopy, culture, and Xpert MTB/RIF assay. The overall sensitivity and specificity of the Xpert MTB/RIF assay in diagnosing pulmonary TB cases was 98.23 and 97.69%, respectively. Among the smear-negative extrapulmonary samples, 52 (5.75%) and 86 (9.6%) samples were positive in culture and the Xpert MTB/RIF assay, respectively. The maximum recovery of MTB by Xpert MTB/RIF assay was from tissue biopsy specimens. Rifampicin resistance was observed in six samples.
Conclusion: Both culture and Xpert MTB/RIF methods were sensitive in detecting smear-positive samples. Although both techniques missed some smear-negative pulmonary and extrapulmonary TB cases, the Xpert MTB/RIF assay enhanced the detection rate of MTB compared to culture. The Xpert MTB/RIF assay enabled the accurate diagnosis of tuberculosis cases with a rapid turnaround time; therefore, it could assist clinicians to start timely therapeutic interventions for these patients.

---

Background: Non-fermenting gram-negative bacteria (NFGNB) pose a threat to the healthcare system. Thus, the purpose of this study was to determine the species diversity of this group isolated from the wound.
Materials & Methods: For species identification during the research period, the MALDI-TOF method of mass spectrometry using the Microflex LT mass spectrometer was applied. As a result, from 2018 to 2022, 7610 microbiological studies were conducted, no microflora growth was detected in 2039 cultures, 1797 strains were isolated and identified in 1523 cultures.
Findings: 261 cultures were found in monospecies; 34 cultures were represented by two or more strains of NFGNB; in 189 cultures, two or more genera of NFGNB were found together with another microflora; in 1039 cultures there was only one NFGNB representative as a part of a mixed culture containing another microflora. The following genera of NFGNB were most common (number of strains): Acinetobacter spp. (1002), Pseudomonas spp. (699), Stenotrophomonas spp. (52), Alcaligenes spp.  (27), Achromobacter spp. (13), Burkholderia spp. (4). Within 5 years, an increase in the share of Acinetobacter spp. by 6.01% was noted; the share of Pseudomonas spp. decreased by 8.39%.
Conclusion: Many rare species have been found, so it is obligatory to ascertain whether penetration into the wound was an accident or the consequence of acquiring new pathogenic properties previously not typical for these microorganisms. No microflora growth was detected in more than 26% of cultures, which requires measures to improve the efficiency of microbiological diagnostics.

---

Aim: Considering the importance and effectiveness of disease prevention awareness campaigns in healthcare and their limited use in Iran and worldwide, more widespread implementation of these campaigns could have significant positive impacts on public health outcomes. The aim of this study was to evaluate the effectiveness of disease prevention awareness campaigns.
Methods: For this literature review, we conducted a systematic search of papers published on disease prevention awareness campaigns between 2010 and 2022, in both Persian and English. We limited our search to papers with full text available and searched across multiple credible scientific databases, including ScienceDirect, PubMed, Scopus, Google Scholar, SID and Magiran. We excluded papers that did not align with our research objectives.
Finding: Out of the 44 papers searched on disease prevention awareness campaigns, 18 relevant papers were selected and their results were investigated, of which two cases were in Persian and 16 in English. Most areas used in the awareness campaigns were related to diseases, such as acute coronary syndrome, diabetes, cancers, sexually transmitted diseases, and infectious diseases. The results of this study suggested the effectiveness of organizing awareness campaigns in preventing diseases.
Conclusion: The evidence suggests that awareness campaigns have had a positive impact on reducing the risk of disease and preventing its development. Therefore, it is important to make concerted efforts to develop effective and appropriate awareness campaigns for all individuals at risk.

---

Aims: Every year, the prevalence of mental disorders continues to increase, presenting diverse scenarios that emphasize the importance of early identification and efficient intervention. This project aimed to create a culture-based cadre empowerment model to increase the capacity of individuals in the community to identify mental disorders at an early stage.
Instrument & Methods: This descriptive cross-sectional study was conducted from September to October 2022 in the entire population of mental health cadres in Lamongan Regency. The sample consisted of 110 cadres. The Partial Least Square test was used in data analysis. The culture-based cadre empowerment approach consisted of formal, informal, family, technology, and cultural factors.
Findings: Formal factors (t=3.385), informal factors (t=2.059), family factors (t=3.117), cultural factors (t=2.395), technological factors (t=3.798), and personal values (t=12.173) had direct significant relationships with culture-based cadre empowerment. Additionally, cultural factors (t=2.084) and technological factors (t=2.606) had direct significant relationships with cadre capabilities.
Conclusion: Cultivating feelings of empowerment improves the cadres' ability to detect early mental disorders.
 

---

Objective: In this study, the possibility of prenatal diagnosis of Down syndrome with Real-Time PCR method was evaluated. In this context, optimization of a suitable method for purification of high quality DNA from amniotic fluid samples was also considered. Materials and Methods: Pregnant women who had the high risk of having babies with Down syndrome were selected according to the biochemical and sonographic data and referred to the amniocentesis center. The DNA of total 59 amniotic fluid samples were extracted with different methods including boiling method, salting out method, Procedures of DNA extraction from Blood and Cell Culture by DNPTM Kit (CinnaGen), Procedure of DNA extraction from cells by DNA Isolation Kit for cells and tissues (Roche), Procedure of DNA extraction from Tissue by MagNa Pure DNA Isolation kit (Roche), and QIAamp DNA Micro Kit (Qiagen). Then, the quality and quantity of the extracted DNA were evaluated by the NanoDrop® ND- 1000 spectrophotometer device. Real-Time PCR reaction using fluorescent dye SYBR Green I (Applied Biosystems, UK) was performed to specifically amplify DSCAM and DYRK1A2 genes and the reference gene (PMP22). Data analysis was performed using comparative cycle threshold method for the determination of the gene dosage and determining the number of copies of chromosome 21. Results: This study showed that DNA extracted from amniotic fluid samples using QIAamp DNA Micro Kit (Qiagen) has the desirable quantity and quality for Real-Time PCR. Specific proliferation of targets and reference genes was achieved and difference between normal and affected groups based on differences between their gene dosages was determined. Conclusion: Prenatal diagnosis of Down syndrome is feasible by the Real-Time PCR method using DNA samples from amniotic fluid cells extracted by QIAamp DNA Micro Kit (Qiagen). The results are comparable to the corresponding results from conventional cytogenetic methods.

---

Viral symptoms indicative of Iris yellow spot virus (IYSV) were observed on onion in several fields near Chenaran in Khorasan Razavi Province. Mechanical inoculation of herbaceous hosts with onion sap extracts from symptomatic plants showed similar symptoms to those described for IYSV. The mechanically transmitted virus reacted only with antisera specific to IYSV in DAS-ELISA but not with antisera specific to seven other tospoviruses. In RT-PCR, a DNA fragment approximately 822 bp in size was amplified from infected Nicotiana benthamiana by using primers specific to the nucleocapsid (N) gene of IYSV. After cloning and sequencing, the deduced N protein sequence of two isolates (GenBank accession no. HQ148173 and HQ148174) showed 98% amino acid identity with a Sri Lankan isolate, 96% with a Dutch isolate and 92% with a Brazilian isolate. To our knowledge, this is the first molecular characterization of IYSV in Iran.

---

Abstract- This paper uses data fusion based on fuzzy measure and fuzzy integral theory for stator winding inter-turn short circuit fault diagnosis in induction motors. Data fusion be considered in two level: feature level and decision level. Three-phase current signals of induction motor are used for fault diagnosis. Time-domain features are extracted from current signals, and a technique based on fuzzy density is proposed to choose appropriate features. The fuzzy c-mean analysis method is employed to classify different modes. It is used to choose the membership values of each feature for each fault mode. Finally, different features are fused at feature-level using Sugeno fuzzy integral data fusion and at decision-level using Choquet fuzzy integral data fusion to produce diagnostic results. Results show that fuzzy data fusion method performs very well for fault diagnosis in a 4hp laboratory induction motor.

Key words: Fuzzy integral; Data fusion; Fault diagnosis; Induction motor; Stator three-phase current.

---

Today, fast and accurate fault detection is one of the major concerns in the industry. Although many advanced algorithms have been implemented in the past decade for this purpose, they were very complicated or did not provide the desired results. Hence, in this paper, we have proposed an emerging method for deep groove ball bearing fault diagnosis and classification. In the first step, the vibration test signals, related to the normal and faulty bearings have been used for both of the drive-end and fan-end bearings of an electrical motor. After that, we have employed the one dimensional Meyer wavelet transform for signal processing in the frequency domain. Hence, the unique coefficients for each kind of fault were extracted and directed to the adaptive neuro-fuzzy system for fault classification. The intelligent adaptive neuro-fuzzy system was adopted to enhance the fault classification performance due to its flexibility and ability in dealing with uncertainty and robustness to noise. This system classifies the input data to the faults in the race or the balls of each of the fan-end and the drive-end bearings with specific fault diameters. In the final part of this study, the new experimental signals were processed in order to verify the results of the proposed method. The results reveal that this method has more accuracy and better classification performance in comparison with other methods, proposed in the literature.

---

This paper presents a new scheme based on state estimation to diagnosis an actuator or plant fault in a class of nonlinear systems that represent the nonlinear dynamic model of gas turbine engine. An optimal nonlinear observer is designed for the nonlinear system. By utilizing Lyapunov's direct method, the observer is proved to be optimal with respect to a performance function, including the magnitude of the observer gain and the convergence time. The observer gain is obtained by using approximation of Hamilton -Jacobi -Bellman (HJB) equation. The approximation is determined via an online trained neural network (NN). Using the proposed observer, the system states and the fault signal can be estimated and diagnosed, respectively. The proposed approach is implemented for state estimation and fault detection of a gas turbine model subject to compressor mass flow fault. The simulation results illustrate that the proposed fault detection scheme is a promising tool for the gas turbine diagnostics.

---

Oil pipeline leakages, if not properly treated, can result in huge losses. The first step in tackling these leakages is to diagnose their location. This paper employs a data-driven Fault Detection and Isolation (FDI) system not only to detect the occurrence and location of a leakage fault, but also to estimate its severity (size) with extreme accuracy. In the present study, the Golkhari-Binak pipeline, located in southern Iran, is modeled in the OLGA software. The data used to train the data-driven FDI system is acquired by this model. Different leakage scenarios are applied to the pipeline model; then, the corresponding inlet pressure and outlet flow rates are recorded as the training data. The time-domain data are transformed into the wavelet domain; then, the statistical features of the data are extracted from both the wavelet and the time domains. Each of these features are then fed into a Multi-Layer Perceptron Neural Network (MLPNN) which functions as the FDI system. The results show that the system with the wavelet-based statistical features outperforms that of the time-domain based features. The proposed FDI system is also able to diagnose the leakage location and severity with a low False Alarm Rate (FAR) and a high Correct Classification Rate (CCR).

---

In this study, an intelligent diagnosis systems have been developed and applied for classifying six types of cooling radiator conditions by means of infrared thermal images; namely, radiator tube blockage, radiator fin blockage, loose connections between fins and tubes, radiator door failure, coolant leakage and normal. The proposed system is consisted of several subsequent procedures including thermal image acquisition, preprocessing, of images via two dimensional discrete wavelet transform (2D-DWT), feature extraction, feature selection, and classification. The 2D-DWT was implemented to decompose the thermal images. Subsequently, statistical texture features were extracted from the original and decomposed thermal images. Consequently, statistical texture features are extracted from the original and decomposed thermal images to develop ANFIS classifiers. In this paper, the significant and relevant features are selected based on genetic algorithm (GA) in order to enhance the performance of ANFIS classifier. For evaluating ANFIS classifier performance, the values of the confusion matrix, such as specificity, sensitivity, precision and accuracy were computed. The overall accuracy of the classifier was 94.11 %. The results demonstrated that this system can be employed satisfactorily as an intelligent condition monitoring and fault diagnosis for a class of cooling radiator.

---

Design of fault detection and diagnosis systems (FDDS), although extending the control strategies, they are challenged by controller interferences in fault diagnosis. In this study, in order to improve performance and accuracy of FDDS in the fault detection process, considering influential parameters and the level of corresponding interferences is investigated. To achieve this enterprise, a powerful method in fault pattern recognition of industrial plants based on dynamic behavior and dynamic model by using soft computing is designed and tested on simulated suspension system of a vehicle. The suspension system is one the parts, most affecting reliability and safety of the vehicle. For investigating the level of interference caused by the control unite, the simulations of both passive and active (equipped with hydraulic actuator) suspension systems are utilized in association with the control unite. The results of tests under variable circumstances (using random values) demonstrate that the presence of control unite, strict the FDDS process and reduces the robustness of the system against disturbances and noise. Considering the way in which the control unite affects the process, application of suggested solutions in this research, have a considerable impact on amendment of the adverse effects.
Fault detection program which is provided by Matlab software benefits special possibilities to investigate and define the effect of controlling unite and can be considered as a useful device to facilitate and precipitate conduction of tests in different stages of the research.

---

Any industry needs an efficient predictive plan in order to optimize the management of resources and improve the economy of the plant by reducing unnecessary costs and increasing the level of safety. Rotating machinery is the most common machinery in industry and the root of the faults in rotatingmachinery is often faulty rolling element bearings. Because of a transitory characteristic vibration of bearing faults, combining Continuous wavelet transforms with envelope analysis is applied for signal proseccing. This paper studies the application of independent component analysis and support vector machines to for automated diagnosis of localized faults in rolling element bearings. The independent component analysis is used for feature extraction and data reduction from original features. The principal components analysis is also applied in feature extraction process for comparison with independent component analysis does. In this paper, support vector machines-based multi-class classification is applied to do faults classification process and utilized a cross-validation technique in order to choose the optimal values of kernel parameters.