RSSI-based LoRaWAN dataset collected in a dynamic and harsh industrial environment with high humidity.

Enabling precise device localization is a critical requirement for the future of the industry. Leveraging signal features for location determination has emerged as a leading approach and a good alternative for Global Navigation Satellite Systems (GNSS) because of their limitations (low accuracy for indoor environments, expensive chips, and high energy consumption). On this basis, to provide localization for IoT in an industry with a harsh environment, the adopted wireless networks should have a long-range coverage area. LoRaWAN (a low power and wide area networking protocol built on top of the LoRa radio modulation technique) is one of the most common communication networks that can provide coverage with low implementation cost and power consumption [1]. Among various signal features that can be used for localization, Received Signal Strength (RSS) gets more attention because of its low-cost deployment. However, RSS is highly dependent and sensitive to environmental changes, such as temperature, humidity, and background noise. This sensitivity becomes more intensive in an industrial environment with a harsh and dynamic setting. To evaluate the environmental effects on RSS in the harsh and highly dynamic industry, we present a comprehensive repository of Received Signal Strength Indicator (RSSI) measurements, collected in a harbor as a testbed featuring three LoRa gateways and one mobile end node. During the data collecting process, the mobile device obtains its location via GPS and transmits it as the LoRa message. In addition, to provide more insight into the effect of the dynamic environment on the RSSI, two end nodes are implemented in fixed locations. These end nodes transmit messages at fixed time intervals, including their unique IDs. The collected dataset includes RSSI and SNR measurements recorded by multiple gateways for each transmitted packet by fixed or mobile end nodes, along with timestamps. This dataset enables the development and evaluation of RSSI-based localization and allows researchers to explore the challenges and opportunities associated with localization in a dynamic and harsh environment.

Prevalence and risk factors of pre-hypertension and hypertension among adults in Southeastern Iran: Findings from the baseline survey of the Zahedan adult cohort study.

Hypertension (HTN) stands as the leading modifiable risk factor for cardiovascular disease(CVD) and premature death globally. Understanding its prevalence and risk factors is essential for effective prevention and management of HTN. This study aimed to investigate the prevalence of Pre-hypertension (pre-HTN), HTN, and its risk factors in adults participating in the Zahedan adult cohort study (ZACS). This cross-sectional study used the baseline data of the ZACS. Ordinal logistic regression analysis was used to estimate crude and adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for potential risk factors. Among the 10,016 participants in this study, 60.89% were women, with an average age of 50.44 ± 9.18 years. The prevalence of pre-HTN and HTN was 42.03% (men 45.44%, women 39.84%) and 18.47% (men 21.09%, women 16.79%), respectively. Being male, older age, having higher socioeconomic status (SES), being overweight and obese, having a family history of HTN, comorbidities such as diabetes and CVD, as well as abnormal blood lipid levels (triglycerides and HDL cholesterol) were the most significant predictors of pre-HTN and HTN. These findings highlight that more than half of the participants in this study exhibit pre-HTN or HTN, placing them at risk for CVD and stroke. Implementing comprehensive preventive strategies tailored to these identified risk factors is imperative to alleviate the disease burden, enhance disease management, and improve HTN treatment and control.

Eubiotic Effect of a Dietary Bio-Aqua® and Sodium Diformate (NaDF) on Salmo trutta caspius: Innate Immune System, Biochemical Indices, Antioxidant Defense, and Expression of Immunological and Growth-Related Genes.

The present study investigated the effects of combined and singular oral administration of Bio-Aqua® with different dosages of sodium diformate (NaDF) on biochemical indices, innate immune responses, antioxidant effects, and expressions of immunological related genes of Caspian brown trout (Salmo trutta caspius). Fingerlings Salmo trutta caspius (n = 1800; initial weight 15 ± 3 g) were randomly allocated into five groups (120 fish group-1 in triplicates). Control diet: without any addition, G1, G2, G3, and G4 received diets containing 0.2 g kg-1 commercial probiotic Bio-Aqua® combined with 0, 0.5, 1.0, and 1.5% NaDF to the basal diet for 60 days according to recommended dosages reported in previous studies. Results indicated that serum bactericidal activity (G3 on day 60 and G1 on day 30) and classic complement in all groups (on day 60) (G1 and G2 on day 30) were significantly elevated (P < 0.05). The serum lysozyme, glucose, globulin, and albumin levels showed no significant differences between all groups compared to the control group (P > 0.05). On days 30 and 60 of the sampling, no significant difference was observed in the amount of superoxide disotase (SOD) and catalase (CAT) between the treatments (P > 0.05) but activity of malondialdehyde (MDA) was lower in G1 than the control (P < 0.05). The expression of the immune-regulating genes IL-10, IL-1ß, GTP, FATP, and IGF was significantly improved in all probiotic + acidifier-treated groups (P < 0.05). The current findings showed that mixture of Bio-Aqua® and NaDF (1.5% + pro) is beneficial, as it effectively improves some immune parameters and expression of immunological and growth-related genes in Caspian brown trout.

A machine learning-based system for detecting leishmaniasis in microscopic images.

BACKGROUND: Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. METHODS: We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. RESULTS: A 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages. CONCLUSION: The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.

Effects of different spawning agents on serum levels of reproductive steroid hormones and cortisol level in adult female Barbus sharpeyi (Gunther, 1874).

The question of whether, as hormone therapies, spawning agents differ from each other to induce physiological pathways of gametogenesis and oocyte maturation in fish remains important, because it could modify undesirable changes, regulated by endocrine systems of individual fish. A series of experimental treatments were applied to investigate the underlying mechanism(s) in which female bunnei (Barbus sharpeyi) fish respond differently to hormone therapies. Female broodstocks were injected twice (with 12 h interval) by three different treatments namely A, B and C. The treatment A received carp pituitary extract (CPE) + luteinizing hormone-releasing hormone analogs (LHRHα2) (0.5 mg CPE kg(-1) BW for first injection and 2 mg CPE kg(-1) BW + 10 µg LHRHα2 kg(-1) for second injection), treatment B received CPE (0.5 and 3.5 mg kg(-1) BW), and treatment C received ovaprim (0.1 and 0.15 ml kg(-1) BW). Blood samples were collected at four different time intervals, including prior to injections, 6 h after first injection, 6 h after second injection and at the time of spawning, and serum steroid hormones, including testosterone, progesterone and estradiol-17ß as well as cortisol, were measured. Results showed significant increases in serum estradiol-17ß following all treatments, but the most profound response was found in treatments A and B. Testosterone was higher in larger broodfish than in small-sized broodfish (>1.5 vs. <1.5 kg) in all treatments. CPE led to higher concentration of testosterone rather than two other treatments. CPE also increases the progesterone following first injection and approximately remains unchanged till the end of experiment. Change in progesterone level was only significant after second injection of ovaprim as well as after spawning compared with previous time. Linear regression analyses indicated that cortisol had adverse effects on progesterone and testosterone levels of weight group <1.5 kg. These results suggest that among inducing agents, applied here, CPE can provide more reasonable response in reproduction of female B. sharpeyi.

Role of OpenEHR as an open source solution for the regional modelling of patient data in obstetrics.

This work investigates, whether openEHR with its reference model, archetypes and templates is suitable for the digital representation of demographic as well as clinical data. Moreover, it elaborates openEHR as a tool for modelling Hospital Information Systems on a regional level based on a national logical infrastructure. OpenEHR is a dual model approach developed for the modelling of Hospital Information Systems enabling semantic interoperability. A holistic solution to this represents the use of dual model based Electronic Healthcare Record systems. Modelling data in the field of obstetrics is a challenge, since different regions demand locally specific information for the process of treatment. Smaller health units in developing countries like Brazil or Malaysia, which until recently handled automatable processes like the storage of sensitive patient data in paper form, start organizational reconstruction processes. This archetype proof-of-concept investigation has tried out some elements of the openEHR methodology in cooperation with a health unit in Colombo, Brazil. Two legal forms provided by the Brazilian Ministry of Health have been analyzed and classified into demographic and clinical data. LinkEHR-Ed editor was used to read, edit and create archetypes. Results show that 33 clinical and demographic concepts, which are necessary to cover data demanded by the Unified National Health System, were identified. Out of the concepts 61% were reused and 39% modified to cover domain requirements. The detailed process of reuse, modification and creation of archetypes is shown. We conclude that, although a major part of demographic and clinical patient data were already represented by existing archetypes, a significant part required major modifications. In this study openEHR proved to be a highly suitable tool in the modelling of complex health data. In combination with LinkEHR-Ed software it offers user-friendly and highly applicable tools, although the complexity built by the vast specifications requires expert networks to define generally excepted clinical models. Finally, this project has pointed out main benefits enclosing high coverage of obstetrics data on the Clinical Knowledge Manager, simple modelling, and wide network and support using openEHR. Moreover, barriers described are enclosing the allocation of clinical content to respective archetypes, as well as stagnant adaption of changes on the Clinical Knowledge Manager leading to redundant efforts in data contribution that need to be addressed in future works.