Miracle-dz/UniFAR-FTUs huggingface dataset:
LoRaWAN Behaviour Analysis through Dataset Traffic Investigation

The large development of Internet of Things technologies is increasing the use of smart-devices to solve and support several real-life issues. In many cases, the aim is to move toward systems that, even if significant demands are not required in terms of quantity of exchanged data, they should be very reliable in terms of battery life and signal coverage. Networks that have these characteristics are the Low Power WAN (LPWAN). One of the most interesting LPWAN is LoRaWAN. LoRaWAN is a network with four principal components: end-devices, gateways, network servers, and application servers. It uses a LoRa physical layer to exchange messages between end-devices and gateways that forward these messages, through classic TCP/IP protocol, to the network server. In this work, we analyse LoRa and LoRaWAN by looking at its transmission characteristics and network behaviour, respectively, explaining the role of its components and showing the message exchange. This analysis is performed through the exploration of a dataset taken from the literature collecting real LoRaWAN packets. The goal of the work is twofold: (1) to investigate, under different perspectives, how a LoRaWAN works and (2) to provide software tools that can be used in several other LoraWAN datasets to measure the network behaviour. We carry out six different analyses to look at the most important features of LoRaWAN. For each analysis we present the adopted measurement strategy as well as the obtained results in the specific use case.
Fast and self-learning indoor airflow simulation based on in situ adaptive tabulation

Fast simulation for stratified indoor airflow distributions is desired for various applications, such as design of advanced indoor environments, emergency management, and coupled annual energy simulation for buildings with stratified air distributions. Reduced order models trained by pre-computed computational fluid dynamics results are fast, but their prediction may be inaccurate when applied for conditions outside the training domain. To overcome this limitation, we propose a fast and self-learning model based on an in situ adaptive tabulation (ISAT) algorithm, which is trained by a fast fluid dynamics (FFD) model as an example. The idea is that the ISAT will retrieve the solutions from an existing data set if the estimated prediction error is within a pre-defined tolerance. Otherwise, the ISAT will execute the FFD simulation, which is accelerated by running in parallel on a graphics processing unit, for a full-scale simulation. This paper systematically investigates the feasibility of the ISAT for indoor airflow simulations by presenting the ISAT-FFD implementation alongside results related to its overall performance. Using a stratified indoor airflow as an example, we evaluated how the training time of ISAT was impacted by four factors (training methods, error tolerances, number of inputs, and number of outputs). Then we demonstrated that a trained ISAT model can predict the key information for inputs both inside and outside the training domain. The ISAT was able to answer query points both inside and close to training domain using retrieve actions within a time less than 0.001 s for each query. Finally, we provided suggestions for using the ISAT for building applications.
Research on Lung Ultrasound Image Classification Based on Compressed Sensing

Pneumothorax is a common injury in disaster rescue, traffic accidents, and war trauma environments and requires early diagnosis and treatment. The commonly used X-ray, CT, and other diagnostic instruments are not suitable for rescue sites due to their large size, heavy weight, and difficulty in transportation. Ultrasound equipment is easy to carry and suitable for rescue environments. However, ultrasound images are noisy, have low resolution, and are difficult to get started, which affects the efficiency of diagnosis. This paper studies the effect of lung ultrasound image recognition and classification based on compressed sensing and BP neural network. We use ultrasound equipment to build a lung simulation model, collect five typical features of lung ultrasound images in M-mode, and build a dataset. Using compressed sensing theory, we design sparse matrix and observation matrix and perform data compression on the image data in the dataset to obtain observation values. We design a BP neural network, input the observations into the network for training, and compare it with the commonly used VGG16 network. The method proposed in this paper has higher recognition accuracy and significantly fewer parameters than VGG16, so it is suitable for use in embedded devices.
Assessment of HIV Rapid Test Kits Inventory Management Practice and Challenges in Public Health Facilities of Addis Ababa, Ethiopia

Background Many people with undiagnosed HIV live in sub-Saharan Africa and vulnerable laboratory systems undermine testing services. Methods A facility-based mixed-approach cross-sectional study was conducted from January 1 to February 1, 2020. A total of 23 health facilities were included in the study which stratified into hospitals and health centers. Six months of bin card records and request and resupply forms (RRFs) were reviewed. Data were collected through physical inventory, observation, and document review. After the data was imported into the MS Excel 2016 spreadsheet, it was analyzed using SPSS | Version 20 | Software. In addition, 12 semi-structured in-depth interviews were conducted and responses were analysed using a thematic approach. Results The entire health facility had RRF reports and bin card records while the availability of HIV rapid test kits was 75%. More than half, 38 (55.1%) of the bin card records were updated and the average data accuracy of bin cards was 84.1%. The data quality of the RRF reports was determined accurate 18 (78.3%), complete 15 (65.22%), and on-time 7 (30.3%). Sixteen (69.6%) health facilities experienced at least one stock out with an average daily stock out of 4%. The wastage rate was 0.0083%. Only 9 (39.1%) health facilities have fulfilled acceptable storage conditions. Conclusion Most of the health facilities did no longer fulfilled acceptable storage conditions. Similarly, the data quality of most bin card records and RRF reports was poor. This deprived inventory management practice was likely related to supply, staff, and documentation challenges.
Downregulation of m6A Methyltransferase in the Hippocampus of Tyrobp–/– Mice and Implications for Learning and Memory Deficits

Loss-of-function mutations in the gene that encodes TYRO protein kinase-binding protein (TYROBP) cause Nasu-Hakola disease, a heritable disease resembling Alzheimer’s disease (AD). Methylation of N6 methyl-adenosine (m6A) in mRNA plays essential roles in learning and memory. Aberrant m6A methylation has been detected in AD patients and animal models. In the present study, Tyrobp–/– mice showed learning and memory deficits in the Morris water maze, which worsened with age. Tyrobp–/– mice also showed elevated levels of total tau, Ser202/Thr205-phosphorylated tau and amyloid β in the hippocampus and cerebrocortex, which worsened with aging. The m6A methyltransferase components METTL3, METTL14, and WTAP were downregulated in Tyrobp–/– mice, while expression of demethylases that remove the m6A modification (e.g., FTO and ALKBH5) were unaltered. Methylated RNA immunoprecipitation sequencing identified 498 m6A peaks that were upregulated in Tyrobp–/– mice, and 312 m6A peaks that were downregulated. Bioinformatic analysis suggested that most of these m6A peaks occur in sequences near stop codons and 3′-untranslated regions. These findings suggest an association between m6A RNA methylation and pathological TYROBP deficiency.
Antimicrobial Resistance, Pathogenic, and Molecular Characterization of Escherichia coli from Diarrheal Patients in South Korea

Diarrheal diseases due to foodborne Escherichia coli are the leading cause of illness in humans. Here, we performed pathogenic typing, molecular typing, and antimicrobial susceptibility tests on seventy-five isolates of E. coli isolated from stool samples of patients suffering from foodborne diseases in Busan, South Korea. All the isolates were identified as E. coli by both biochemical analysis (API 20E system) and matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). The bacteria displayed entero-pathogenic E. coli (EPEC) (47.0%), entero-aggregative E. coli (EAEC) (33.3%), entero-toxigenic E. coli (ETEC) (6.6%), ETEC and EPEC (6.6%), EPEC and EAEC (4%), and ETEC and EAEC (2.7%) characteristics. The E. coli isolates were highly resistant to nalidixic acid (44.0%), tetracycline (41.3%), ampicillin (40%), ticarcillin (38.7%), and trimethoprim/sulfamethoxazole (34.7%); however, they were highly susceptible to imipenem (98.6%), cefotetan (98.6%), cefepime (94.6%), and chloramphenicol (94.6%). Although 52 strains (69.3%) showed resistance against at least 1 of the 16 antibiotics tested, 23 strains (30.7%) were susceptible to all the antibiotics. Nine different serotypes (O166, O8, O20, O25, O119, O159, O28ac, O127a, and O18), five genotypes (I to V, random-amplified polymorphic DNA), and four phenotypes (A to D, MALDI-TOF MS) were identified, showing the high level of heterogeneity between the E. coli isolates recovered from diarrheal patients in South Korea.
Numerical Study on Natural Vacuum Solar Desalination System with Varying Heat Source Temperature

A natural vacuum desalination unit with varying low grade heat source temperature is investigated numerically. The objective is to explore the effects of the variable temperature of the low grade heat source on performances and characteristics of the desalination unit. The specifications of the desalination unit are naturally vacuumed with surface area of seawater in evaporator and heating coil are 0.2 m2 and 0.188 m2, respectively. Temperature of the heating coil is simulated based on the solar radiation in the Medan city. A program to solve the governing equations in forward time step marching technique is developed. Temperature of the evaporator, fresh water production rate, and thermal efficiency of the desalination unit are analysed. Simulation is performed for 9 hours, it starts from 8.00 and finishes at 17.00 of local time. The results show that, the desalination unit with operation time of 9 hours can produce 5.705 L of freshwater and thermal efficiency is 81.8 %. This reveals that varying temperature of the heat source of natural vacuum desalination unit shows better performance in comparison with constant temperature of the heat source.
Targeting river operations to the critical thermal window of fish incubation: Model and case study on Sacramento River winter‐run Chinook salmon

Allocating reservoir flows to meet societal and ecosystem needs under increasing water demands and climatic variability presents challenges to resource managers. Often, rivers have been regulated to meet flow and temperature compliance points or mimic historical patterns. Because it is difficult to assess if this approach is efficient, process‐based models are being used to design river operations. This paper describes a model for fish incubation survival based on the premise that mortality from thermal stress occurs over a critical window (CW) of embryo development. A model for the embryo CW based on metabolic studies of development is combined with density‐dependent and background mortalities to describe salmonid survival from egg fertilization to fry detection downstream. The model is calibrated with a two‐decade dataset of Sacramento River winter‐run Chinook salmon egg‐to‐fry survival. The effects of temperature exposure over a range of CWs were explored. Based on statistical and biological support, two alternative CWs were identified for temperature control: the entire incubation period and a short duration window prior to hatching. Survival under different CW assumptions and temperature control operations were simulated with an internet‐accessible form of the model. The analysis indicated that under years of limited cold‐water resources, targeting water releases to the CW prior to hatching would yield the highest incubation survival.
Mentoring summer undergraduate researchers: the faculty members’ experience

ABSTRACT Two faculty mentors maintained weekly journals of their experiences supervising three consecutive cohorts of summer undergraduate research interns, with the objective of recording the faculty mentors’ experiences and tracking potential transitions in their experiences across time. One common theme was the importance of creating a mentoring team of other faculty members, university administrators, and departmental staff to assist with the summer interns. One of the foundational experiences during the summer internship was the week-long field trip, which defined each person’s position within the research team. One of the largest challenges for the faculty mentors was balancing the needs of interns with different levels of motivation. Overall, as the faculty mentors gained experience and benefited from working as a team, they were able to increase their satisfaction with the success of the internships and maintain their own levels of academic productivity.
Time Trends in Incidence of Reported Memory Concerns and Cognitive Decline: A Cohort Study in UK Primary Care

Purpose To investigate time trends in incidence of recorded memory concerns (MC) and cognitive decline (CD) in a UK older population presenting to primary care with no prior diagnosis of dementia. To determine the risk of developing dementia in people with recorded memory concern and cognitive decline. Patients and methods We included individuals aged 65–99 years who contributed to data within the IQVIA medical research database from 1st January 2009 to 31st December 2018. We reported crude incidence rates for MC (study population n=1,310,838) and CD (n=1,348,796). We conducted survival analysis to estimate the risk of developing dementia using fine-grey sub-distribution hazard model with competing risk of death. Results We identified 55,941 individuals (4.3%) with a record of incident MC; rates were fairly stable over the decade of study. We identified 14,869 people (1.1%) with a record of incident CD, and these rates increased from 1.29/1000 PYAR (95% CI 1.21 to 1.38) in 2009 to 3.49/1000 PYAR (95% CI 3.30 to 3.68) in 2018. Within 3 years of follow up from the first record of MC, 45.5% of individuals received a diagnosis of dementia, while of those with a record of CD, 51.7% received a dementia diagnosis. Women, people in older age groups and those living in more deprived areas were more likely to have a record of MC or CD, and their symptoms were more likely to progress to a dementia diagnosis. Conclusion Incidence rates of MC and CD estimated from routinely collected primary care data are lower than those reported in community surveys, suggesting that a minority of people who experience memory loss consult their GP and have it recorded. Our findings indicate that those who do report concerns to primary care, especially women, those in older age groups and those in more deprived areas, are at a higher risk for developing dementia.
Energy-efficient capacitor-swapping reset scheme with MSB-split DAC for SAR ADCs

An energy-efficient DAC scheme with most significant bit (MSB) split structure is proposed for successive approximation register (SAR) ADCs. The proposed scheme consumes no switching energy in the first three conversion cycles, and the switching energy for the rest cycles is further optimised using the single-side switching method. Furthermore, the proposed capacitor-swapping reset scheme eliminates the reset energy. Taking the reset energy into consideration, the proposed scheme reduces the total average energy by 99.23% compared with the conventional SAR ADC architecture and hence achieves high energy-efficiency.
Evaluation of aliphatic acid metabolism in bladder cancer with the goal of guiding therapeutic treatment

Urothelial bladder cancer (BLCA) is a common internal malignancy with a poor prognosis. The re-programming of lipid metabolism is necessary for cancer cell growth, proliferation, angiogenesis and invasion. However, the role of aliphatic acid metabolism genes in bladder cancer patients has not been explored. The samples’ gene expression and clinicopathological data were obtained from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Univariate, multivariate, and LASSO Cox regression were used to develop a BLCA prognostic model. GSVA was used to assess function, whereas pRRophetic was used to assess chemotherapeutic drug sensitivity. The twelve-gene signature may define the tumor immune milieu, according to the risk score model. We compared the expression of aliphatic acid metabolism genes in malignant and non-cancerous tissues and chose 90 with a false discovery rate of 0.05 for The Cancer Genome Atlas cohort. The prognostic risk score model can effectively predict BLCA OS. A nomogram including age, clinical T stage, gender, grade, pathological stage, and clinical M stage was developed as an independent BLCA prognostic predictor. The halfmaximal inhibitory concentration (IC50) was used to assess chemotherapeutic medication response. Sorafenib and Pyrimethamine were used to treat patients with low risk scores more sensitively than patients with high risk scores. Immunotherapy candidates with CMS1 exhibited higher risk ratings. The aliphatic acid prognostic risk score model can assess metabolic trends. Clinical stage and molecular subtype may be used to categorize individuals using the risk score.With this new paradigm, future cancer treatment and immunotherapy may be tailored to the patient’s exact requirements.
Incident atrial fibrillation and heart failure in treated hypertensive patients with left ventricular hypertrophy. The LIFE Study

Aim: The present study investigated the appearance and severity of atrial fibrillation (AF) and heart failure (HF) in 8,702 hypertensive patients with left ventricular hypertrophy (LVH) receiving antihypertensive treatment in a prospective trial. Methods: Patients who had a history of AF or HF were not included, and the participants had sinus rhythm when they were randomly allocated to blinded study medication. Endpoints were adjudicated. Results: Incident AF occurred in 679 patients (7.8%) and HF in 246 patients (2.8%) during 4.7 ± 1.1 years mean follow-up. Incident AF was associated with a > 4-fold increased risk of developing subsequent HF [hazards ratios (HRs) = 4.7; 95% confidence intervals (CIs), 3.1–7.0; P < 0.001] in multivariable Cox analyses adjusting for age, sex, race, randomized treatment, standard cardiovascular risk factors and incident myocardial infarction. The development of HF as a time-dependent variable was associated with a multivariable-adjusted 3-fold increase of the primary study endpoint (HRs = 3.11; 95% CIs, 1.52–6.39; P < 0.001) which was a composite of myocardial infarction, stroke or cardiovascular death. Incident HF was associated with a > 3-fold increased risk of developing subsequent AF (HRs = 3.3; 95% CIs, 2.3–4.9; P < 0.001). This development of AF was associated with a > 2-fold increase of the composite primary study endpoint in multivariable Cox analysis (HRs = 2.26; 95% CIs, 1.09–4.67; P = 0.028). Conclusions: Incident atrial fibrillation and heart failure are associated with increased risk of the other in treated hypertensive patients with left ventricular hypertrophy. Such high-risk hypertensive patients who subsequently develop both atrial fibrillation and heart failure have particular high risk of composite myocardial infarction, stroke or cardiovascular death (ClinicalTrials.gov identifier: NCT00338260).
Metabolic Syndrome, Gamma-Glutamyl Transferase, and Risk of Sudden Cardiac Death

Background: Metabolic syndrome is associated with a significantly increased risk of sudden cardiac death (SCD). However, whether temporal changes in the metabolic syndrome status are associated with SCD is unknown. We aimed to determine whether metabolic syndrome and gamma-glutamyl transferase (ɣ-GTP), including their temporal changes, are associated with the risk of SCD. Methods: We performed a nationwide population-based analysis using the Korean National Health Insurance Service. People who underwent a national health check-up in 2009 and 2011 were enrolled. The influence of metabolic syndrome and ɣ-GTP on SCD risk was evaluated. Results: In 2009, 4,056,423 (848,498 with metabolic syndrome) people underwent health screenings, 2,706,788 of whom underwent follow-up health screenings in 2011. Metabolic syndrome was associated with a 50.7% increased SCD risk (adjusted hazard ratio (aHR) = 1.507; p < 0.001). The SCD risk increased linearly as the metabolic syndrome diagnostic criteria increased. The ɣ-GTP significantly impacted the SCD risk; the highest quartile had a 51.9% increased risk versus the lowest quartile (aHR = 1.519; p < 0.001). A temporal change in the metabolic syndrome status and ɣ-GTP between 2009 and 2011 was significantly correlated with the SCD risk. Having metabolic syndrome in 2009 or 2011 indicated a lower SCD risk than having metabolic syndrome in 2009 and 2011 but a higher risk than having no metabolic syndrome. People with a ≥20-unit increase in ɣ-GTP between 2009 and 2011 had an 81.0% increased SCD risk versus those with a change ≤5 units (aHR = 1.810; p < 0.001). Conclusions: Metabolic syndrome and ɣ-GTP significantly correlated with an increased SCD risk. SCD was also influenced by temporal changes in the metabolic syndrome status and ɣ-GTP, suggesting that appropriate medical treatment and lifestyle modifications may reduce future SCD risk.
Opportunities and challenges in machine learning‐based newborn screening—A systematic literature review

The development and continuous optimization of newborn screening (NBS) programs remains an important and challenging task due to the low prevalence of screened diseases and high sensitivity requirements for screening methods. Recently, different machine learning (ML) methods have been applied to support NBS. However, most studies only focus on single diseases or specific ML techniques making it difficult to draw conclusions on which methods are best to implement. Therefore, we performed a systematic literature review of peer‐reviewed publications on ML‐based NBS methods. Overall, 125 related papers, published in the past two decades, were collected for the study, and 17 met the inclusion criteria. We analyzed the opportunities and challenges of ML methods for NBS including data preprocessing, classification models and pattern recognition methods based on their underlying approaches, data requirements, interpretability on a modular level, and performance. In general, ML methods have the potential to reduce the false positive rate and identify so far unknown metabolic patterns within NBS data. Our analysis revealed, that, among the presented, logistic regression analysis and support vector machines seem to be valuable candidates for NBS. However, due to the variety of diseases and methods, a general recommendation for a single method in NBS is not possible. Instead, these methods should be further investigated and compared to other approaches in comprehensive studies as they show promising results in NBS applications.