Machine learning-enhanced drug testing for simultaneous morphine and methadone detection in urinary biofluids.

The simultaneous identification of drugs has considerable difficulties due to the intricate interplay of analytes and the interference present in biological matrices. In this study, we introduce an innovative electrochemical sensor that overcomes these hurdles, enabling the precise and simultaneous determination of morphine (MOR), methadone (MET), and uric acid (UA) in urine samples. The sensor harnesses the strategically adapted carbon nanotubes (CNT) modified with graphitic carbon nitride (g-C3N4) nanosheets to ensure exceptional precision and sensitivity for the targeted analytes. Through systematic optimization of pivotal parameters, we attained accurate and quantitative measurements of the analytes within intricate matrices employing the fast Fourier transform (FFT) voltammetry technique. The sensor's performance was validated using 17 training and 12 test solutions, employing the widely acclaimed machine learning method, partial least squares (PLS), for predictive modeling. The root mean square error of cross-validation (RMSECV) values for morphine, methadone, and uric acid were significantly low, measuring 0.1827 µM, 0.1951 µM, and 0.1584 µM, respectively, with corresponding root mean square error of prediction (RMSEP) values of 0.1925 µM, 0.2035 µM, and 0.1659 µM. These results showcased the robust resiliency and reliability of our predictive model. Our sensor's efficacy in real urine samples was demonstrated by the narrow range of relative standard deviation (RSD) values, ranging from 3.71 to 5.26%, and recovery percentages from 96 to 106%. This performance underscores the potential of the sensor for practical and clinical applications, offering precise measurements even in complex and variable biological matrices. The successful integration of g-C3N4-CNT nanocomposites and the robust PLS method has driven the evolution of sophisticated electrochemical sensors, initiating a transformative era in drug analysis.

Consumer Segmentation Based on Location and Timing Dimensions Using Big Data from Business-to-Customer Retailing Marketplaces.

Consumer segmentation is an electronic marketing practice that involves dividing consumers into groups with similar features to discover their preferences. In the business-to-customer (B2C) retailing industry, marketers explore big data to segment consumers based on various dimensions. However, among these dimensions, the motives of location and time of shopping have received relatively less attention. In this study, we use the recency, frequency, monetary, and tenure (RFMT) method to segment consumers into 10 groups based on their time and geographical features. To explore location, we investigate market distribution, revenue distribution, and consumer distribution. Geographical coordinates and peculiarities are estimated based on consumer density. Regarding time exploration, we evaluate the accuracy of product delivery and the timing of promotions. To pinpoint the target consumers, we display the main hotspots on the distribution heatmap. Furthermore, we identify the optimal time for purchase and the most densely populated locations of beneficial consumers. In addition, we evaluate product distribution to determine the most popular product categories. Based on the RFMT segmentation and product popularity, we have developed a product recommender system to assist marketers in attracting and engaging potential consumers. Through a case study using data from massive B2C retailing, we conclude that the proposed segmentation provides superior insights into consumer behavior and improves product recommendation performance.

Evolutionary and phylogenetic analyses of the barcoding region suggest geographical relationships among Blastocystis sp., ST3 in humans.

Blastocystis sp., has 21 distinct subtypes of which ST3 thought to be the most prevalent subtype. This study aims to analyze the global variations of ST3. In total, 496 sequences with more than 400 nucleotides from Asia, Europe, Africa, and America were included in this study. Results show that allele 34 was the most prevalent allele in all continents. The lowest and highest allele diversity were observed in Europe and Africa, respectively. The nucleotide diversity ranged from 0.0077 in Europe to 0.02 in Africa, and haplotype diversity ranged from 0.461 in America to 0.6 in Africa. The haplotype network and Bayesian structure showed at least two major clusters including Asia and Europe-Africa-America. Tajima's D values for all continents were negative and statistically significant, indicating an excess of rare nucleotide variants. Similarly, the Fu's FS test showed negative values for all regions, indicating an excess of rare haplotypes. Pairwise FST exhibited a high genetic differentiation between Asia and other continents. Mismatch analysis for all populations showed a unimodal distribution. Our findings indicate that there are two probable major clusters of Blastocystis sp. ST3, a cluster which is shared between Europe, Africa, and America, and a cluster which is restricted to Asia.

Investigating the role of internal layout of magnetic field-generating equipment on workers' exposure at power substations.

Introduction. This research is an attempt to show the role of interior layout of equipment in generating magnetic fields. Materials and methods. The levels of an extremely low-frequency magnetic field were measured in accordance with Standard No. IEEE 644:1994 in three substations and a control building in a petrochemical power plant in southern Iran. Then, workers' occupational exposure (time-weighted average [TWA]) was calculated and the sources of maximum magnetic field generation at each place were identified. Their interior design was changed to achieve the optimal layout of equipment subsequently; the workers' TWA was recalculated for the new situation. Results. The obtained results showed that electrical engineers and technicians were exposed to the maximum TWA of 10.14 µT. The operators in the control room were exposed to the lowest TWA of 0.84 µT. The results also showed that after the change of interior design and proper layout design of the equipment in the substations, the TWA was reduced by 0.73 µT. Conclusion. The research findings revealed that the most harmonious arrangement of equipment in an industrial unit plays a major role in reducing the exposure of workers to magnetic fields and ultimately increases the level of their health in the workplace.

Memory loss risk assessment for the students nearby high-voltage power lines-a case study.

With increasing sources of alternating current electromagnetic fields (EMFs) in everyday life, their possible harmful effects on human health are a main area of concern in many countries. Given that children are the most valuable assets of each country, it is of utmost importance to study the effect(s) of EMF exposure on various health aspects of members within this age group. The present research is the first systematic study of the effects of exposure to electric substations on the memory status of male students in the age group of 10 to 12 years. The flux density values of extremely low frequency magnetic field were measured at four elementary schools in Tehran in accordance with IEEE std 644-1994. The device was 3-axis (X, Y, and Z) Gauss Meter, model: TES-1394. The students from two schools nearby a high voltage electricity substation (at distances of 30 and 50 m) were selected as the exposed group, and the students of two other schools at further distances of 1390 and 610 m were considered as the control group. To determine the status of working memory in the students, the questionnaire was adapted from Wechsler Intelligence Scale for Children (WISC-IV). The completed questionnaires were analyzed by t test and chi-square using SPSS 20. The average magnetic flux density was 0.245 µT at case schools and 0.164 µT at control schools, P < 0.01. The demographic characteristics of the students in the two groups were not statistically different. However, the difference in working memory was significant at the level of 5 %. The results of the questionnaire data showed that students in the control group had better working memory compared to students in case group. The findings revealed a reverse correlation between magnetic flux density and working memory of students (R = -0.255). It is concluded that extremely low frequency magnetic field exposure may have a negative impact on the working memory of children, but further studies are necessary to reach a definitive conclusion.

Hazard zoning around electric substations of petrochemical industries by stimulation of extremely low-frequency magnetic fields.

Electromagnetic fields in recent years have been discussed as one of the occupational hazards at workplaces. Hence, control and assessment of these physical factors is very important to protect and promote the health of employees. The present study was conducted to determine hazard zones based on assessment of extremely low-frequency magnetic fields at electric substations of a petrochemical complex in southern Iran, using the single-axis HI-3604 device. In measurement of electromagnetic fields by the single-axis HI-3604 device, the sensor screen should be oriented in a way to be perpendicular to the field lines. Therefore, in places where power lines are located in different directions, it is required to keep the device towards three axes of x, y, and z. For further precision, the measurements should be repeated along each of the three axes. In this research, magnetic field was measured, for the first time, in three axes of x, y, and z whose resultant value was considered as the value of magnetic field. Measurements were done based on IEEE std 644-1994. Further, the spatial changes of the magnetic field surrounding electric substations were stimulated using MATLAB software. The obtained results indicated that the maximum magnetic flux density was 49.90 µT recorded from boiler substation, while the minimum magnetic flux density of 0.02 µT was measured at the control room of the complex. As the stimulation results suggest, the spaces around incoming panels, transformers, and cables were recognized as hazardous zones of indoor electric substations. Considering the health effects of chronic exposure to magnetic fields, it would be possible to minimize exposure to these contaminants at workplaces by identification of risky zones and observation of protective considerations.

Sleep quality and general health status of employees exposed to extremely low frequency magnetic fields in a petrochemical complex.

BACKGROUND: Advances in science and technology of electrical equipment, despite increasing human welfare in everyday life, have increased the number of people exposed to Electro-Magnetic Fields (EMFs). Because of possible adverse effects on the health of exposed individuals, the EMFs have being the center of attention. This study was performed to determine possible correlation between Extremely Low Frequency Electro-Magnetic Fields (ELF EMFs) and sleep quality and public health of those working in substation units of a petrochemical complex in southern Iran. MATERIALS AND METHOD: To begin with, magnetic flux density was measured at different parts of a Control Building and two substations in accordance with IEEE std 644-1994. Subsequently, the questionnaires "Pittsburgh Sleep Quality Index" (PSQI) and "General Health Quality (GHQ)" were used to investigate relationship between ELF exposure level and sleep quality and public health, respectively. Both questionnaires were placed at disposal of a total number of 40 workers at the complex. The filled out questionnaires were analyzed by T-test, Duncan and the Chi-square tests. RESULTS: The obtained results revealed that 28% of those in case group suffered from poor health status and 61% were diagnosed with a sleep disorder. However, all members in control group were in good health condition and only 4.5% of them had undesirable sleep quality. CONCLUSION: In spite of a significant difference between the case and control groups in terms of sleep quality and general health, no significant relationship was found between the exposure level and sleep quality and general health. It is worth noting that the measured EMF values were lower than the standard limits recommended by American Conference of Industrial Hygienists (ACGIH). However, given the uncertainties about the pathogenic effects caused by exposure to ELF EMFs, further epidemiological studies and periodic testing of personnel working in high voltage substations are of utmost importance.

Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System.

Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated.