Collection System Research Articles

A robust neural network for privacy-preserving heart rate estimation in remote healthcare systems

In this study, we propose a computationally-light and robust neural network for estimating heart rate in remote healthcare systems. We develop a model that can be trained on consumer-grade graphics processing units (GPUs), and can be deployed on edge devices for swift inference. We propose a hybrid model based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) architectures for estimating heart rate from Electrocardiogram (ECG) and Photoplethysmography (PPG) signals. Considering the sensitive nature of the ECG signals, we ensure a formal privacy guarantee, differential privacy, for the model training. We perform a tight accounting of the overall privacy budget of our training algorithm using the Rényi Differential Privacy technique. We demonstrate that our model outperforms state-of-the-art networks on a benchmark dataset for both ECG and PPG signals despite having a much smaller number of trainable parameters and, consequently, much smaller training and inference times. Our CNN-BiLSTM architecture can also provide excellent heart rate estimation performance even under strict privacy constraints. We develop a prototype Arduino-based data collection system that is low-cost, efficient, and useful for providing access to modern healthcare services to people living in remote areas.

Development of a soil electrical conductivity measurement system in paddy fields

This study aims to develop a data collection system for measuring soil electrical conductivity (EC) in Paddy Fields using the Wenner measurement principle, with one electrode configuration based on the Veris 3100 measuring machine, and a second electrode configuration with fixed electrodes. The result is a system that measures soil EC using the fixed electrode configuration for more stable results compared to the coulter electrode configuration on the Veris 3100. This system comprises three main components: a 7-inch industrial touchscreen monitor used for real-time monitoring and storing collected data on external memory, a controller that provides reverse direct current (DC) power supply to the electrodes and measures voltage and current parameters on the electrodes, two mechanical configurations including six electrodes in contact with the soil. The system is developed to operate best after soil preparation and before seed sowing. The collected data is processed and compared to actual measurements using an artificial neural network (ANN), resulting in an R2=0.7449, equivalent to linear regression. The system has been successfully installed and operated on a handheld tractor and tested in Paddy Fields on the outskirts of Can Tho City, Vietnam.

Design and Development of Solar Operated Fodder Cutting and Collecting Machine

Abstract: This paper describes the design, manufacture and performance evaluation of a small modified forage cutter and harvester built with a very simple mechanism that is cheap and simple. Harvesting fodder is a necessary farming activity that requires considerable work. Labor costs and availability pose significant challenges during the cutting season. Due to shortage of labor and unpredictable weather during harvesting, farmers must use mechanized methods to ensure speed of harvesting and collection. We have used various techniques to analyze and design a very low-cost fodder cutter and harvester with solar system and battery. This machine is designed for small farmers who have less than 2 acres of agricultural land. This small machine can cut up to two stacks of fodder at once. The feed is cut by reciprocating blades that cut it like scissors. It uses a 12V electric motor for propulsion. This power is supplied to the cutter by an electric motor. After sowing, the feed is collected in a storage tank using a collection system. Because it was built using spare parts readily available in the area, this small machine is easy to maintain and operate. Small farmers can find this machine the answer to their labor and implementation cost problems

Improved drinking water planning challenges in Urban Households’ in Bamenda city, Cameroon

Access to improved drinking water remains a fundamental need and a basic human right vital for the dignity and health of all people. There is an urgent need to increase access to improved drinking water in urban households in Bamenda city where a low proportion currently appears to be having access. There also exist marked disparity in access amongst the different Sub-divisions which is still to be scientifically investigated. This study sets out to investigate the improved drinking water planning challenges in urban households in Bamenda, Cameroon. The study used systematic field observation, household surveys and administration of 520 copies of questionnaire to urban household heads as primary sources of data collection. The administration of the 520 copies of questionnaire was carried out using purposive and simple random sampling techniques within Bamenda I, II and III Sub-Divisions. Primary data were matched with secondary source information obtained from WHO, UNICEF, UN, CDC, UNDP, PAHO and The University of Bamenda Central Library. Findings revealed that standpipes (41.3%), protected dug well (51.9%), piped water connected to households (54.8%), boreholes (61.9%), rain water collection (63.3%) and spring water harnessed to households (38.6%) constitutes the main urban households improved drinking water sources. Also, natural factors (33.67%) and planning-related challenges (63.33%) work together to limit access to improved drinking water to households. Field observation and measurements revealed that less than 5 urban households share a common water source, while other 42.88% cover <50m to get to the nearest improved drinking water source point. The study highlights that improved drinking water in Bamenda city is currently a limited and non-basic service. The study recommends the expansion of improved drinking water infrastructure by the relevant stakeholders, systematic data collection on factors affecting access to improved drinking water by urban households for better decision making, seek ways to increase accessibility of improved drinking water by the government via MINEE and MINDUH and finally, households should build water towers to store water for continuous use.

Energy Efficiency Improvement for Manufacturing Companies in Latvia

Concerns about climate change and environmental issues are becoming more and more popular nowadays and are causing concerns in everyone's life and beyond. One of significant causes of climate change is energy consumption, especially in the manufacturing sector. Even though, energy efficiency (EE) topic has been extensively discussed, not all EE aims are met. For the manufacturing sector the reason is that EE is often not a top priority for the companies. Currently the companies are also faced with another big challenge – in this digital era of rapidly developing technologies they have to adjust their practices to seize the opportunities provided by digitalization and automatization. Therefore, the aim of this research is to explore how digitization can be combined with EE to promote the resilience and increase performance of manufacturing companies, and how remote data analysis, also by using machine learning tools, can help improve EE. Literature review, content analysis, empirical data analysis and cases studies from real production companies in Latvia are used to achieve the research goals. The results section shows how digital tools and real-time monitoring help assessing the current state of the business and make decisions on changes to future operations, helping to reduce consumption and the environmental impact of production. Manufacturing companies have the potential to improve EE through digitalisation, but the manufacturing processes are complex and Latvian companies are only slowly moving towards digitalisation. Therefore, there is a strong need to examine empirical cases to gather more perspective and to find a way to implement digitalization and EE improvement across all sectors. Conclusions suggest that manufacturing companies should be encouraged to move towards a more unified energy data collection system, enabling more efficient data analysis and proposals for energy efficiency improvements.

How to not Waste Glass Waste

As a highly versatile material, glass is used for numerous significant applications in our lives: from building structures to household products, to optical instruments, etc. Even more impressive than its versatility is the theoretically endless recycling opportunities of glass. While closed loop recycling is preferred due to raw materials savings and lower CO2 emissions, it is oftentimes hindered by the quality and impurities in the collected glass waste. The technical limitations to recycling may be application specific, for example, if the post-consumer glass collection systems cannot guarantee the required high quality and thus closed-loop recycling is disregarded. Moreover, there are economic and environmental limitations to glass recycling. The aim of the paper is to set a base for comparative analysis of scenarios for the potential development of glass recycling model in Latvia. The methodology algorithm of the proposed study consists of three main parts, namely, composition of database required for the analysis, building of the analysis framework, i.e., selection of technology alternatives and assessment criteria (indicators) and application on MCDA by TOPSIS method. A literature review on novel glass waste valorization techniques is performed and the summary conclusions will be used for further research development.

Quantification of Lost Resource Potential of Unsorted Textile Waste

The European Union (EU) requirements for the separate collection of textile waste came into force in Latvia in 2023. As part of the measure, containers for the management of separated textile waste were placed in locations accessible to the public, alongside other types of separately collected waste (e.g., glass, plastic and cardboard). The containers were designed to collect textile waste that met the following requirements:Shoes and clothes must be clean, dry, and free of chemicals, motor oil stains and mold.Free from damage (holes, tears, etc.);Soft toys, fabric scraps and rags, shoes that are no longer a pair or are damaged, torn or have holes, as well as home textiles, are not allowed to be stored in containers.This means that the national textile waste collection system is limited only to textiles that can be reused without additional repair or pre-treatment (e.g., washing), and the developed national textile waste collection system supplements the private initiatives existing through clothing donation points and second-hand shops. However, there is a “shadow” zone related to textile waste that do not meet the above mentioned collection requirements and are not specifically regulated and managed so far at a country level, thus have to be collected in unsorted household waste containers.The objective of the present research is to quantify the amount of textile waste collected from unsorted mixed household waste containers and to quantify the physical condition and fibers of this waste. Subsequently, the environmental impact of the lost resource potential of the unsorted textile waste based on the obtained quantitative and qualitative waste data was defined via life cycle indicators.

Privacy in a Smart City

An increasing number of cities around the globe aim to become Smart Cities through the implementation of a range of Information and Communication technologies (ICT), AI applications and cloud-based IoT solutions. While the underlying motivations vary, all such transitions require large amounts of data. In this paper, we articulate and defend two claims about privacy in a Smart City. Our first claim is that some level of systematic data collection and processing is ethically permissible. However, there is an upper limit for what is permissible: We suggest that it is never permissible to collect and process data that significantly undermine people’s autonomy. Our second claim specifies when such minor privacy infringements that do not significantly undermine people’s autonomy are permissible: We suggest that the only policies legitimized by the first claim are those that promote the collective good. Keywords: smart city, privacy, autonomy, natural right, collective good

RESCUED (REgistry for Sudden Cardiac and UnExpected Death): the German registry for families affected by sudden cardiac death in the young

Abstract Background Sudden cardiac death in the young (SCDY) refers to an abrupt and unexpected cardiac arrest at a young age due to an underlying cardiac cause. Previous studies have shown that estimated more than 10% of SCDY victims 1 to 35 years of age suffered from a – previously often undiagnosed – rare genetic cardiac disorder. Due to the inheritability of such sequence variations in disease-causing cardiac genes, blood relatives of the deceased may equally be carriers of the causative genetic variation and therefore have an increased risk for cardiac arrhythmia. Purpose Despite sudden cardiac death (SCD) increasingly gaining global awareness, systematic data collection in form of registries is sparse. The data sets collected in the RESCUED registry allow for the identification of leading causes of SCDY in Germany and offer unique opportunities for further scientific analyses with the aim of revealing unrecognized trends, risk factors and clinical warning signs of SCDY. Methods Our workflows are based on international guidelines for post mortem examination of SCDY victims and clinical assessment of their relatives. Post-mortem assessment of the index patient includes autopsy, histology, toxicology, genetic testing and review of pre-mortem clinical data. Biological relatives within the family of the index patient undergo a cardiogenetic consultation, a set of cardiological examinations and a cascade genetic screening to determine each individual´s cardiac risk profile. Data is recorded in the registry through the web-based interface of the OSSE registry software (Open Source Registry System for Rare Diseases). Results Based on our experience with complex cases of SCD (over 150 SCDY cases and more than 450 relatives) and an in-depth literature research regarding SCD registries, we have developed designated questionnaires for systematic data collection for the deceased index patient and their biological relatives, respectively. We will present insights from the design of this registry, which represents a novel, pedigree-based approach to SCDY as a consequence of a possible underlying genetic cause, and offers a new viewing angle of identifying a possible cause of SCDY. For illustrational purposes, two SCDY cases including clinical data of the family members will be shown. Conclusions RESCUED is Germany´s first registry with a holistic, pedigree-based approach to SCDY. Apart from thoroughly documenting the cardiac deaths of young individuals under the age of 50, the phenotypes of their relatives are equally recorded. Ultimately, insights gained from the RESCUED registry will lead to improved and targeted diagnostic possibilities, risk stratification and, if need be, therapy in susceptible individuals, working towards the prevention of tragic deaths in affected families. The registry will also give insights into possible preceding warning signs and symptoms of SCDY regardless of whether the underlying cause is a rare genetic disease or not.

South Tyrol Healthcare Company: A Look at a Peculiar Model of Claims Management in Italy and Analysis of Its Last 11 Years

Background: Alternative Disputes Resolution (ADR) systems are becoming increasingly important tools in recent years for the management and resolution of health responsibility cases, but their dissemination and efficiency are still poorly described. The purpose of this paper is to present an ADR system in the autonomous province of Bolzano: the Conciliation Commission. Methods: systematic collection of all claims of the South Tyrol Sanitary Service that were dealt with in the Conciliation Commission from 1 January 2012 to 31 December 2022. Results: closing times of the applications received turn out to be less than a year in 63.8% of the cases, with a number of cases managed rather stably in the time, even if minimal if compared to the number of complaints received to the South Tirol Health Service. Only 5.3% of the application continued the legal process before a civil court. Conclusions: the Conciliation Commission of South Tirol appears to be an excellent instrument for the resolution of disputes in the healthcare field, with rapid resolution times and little to zero costs for the healthcare company, a public health institution. Despite its effectiveness, it seems to be a tool that is still little-known in South Tyrol.

Management of New Automatic Ticket Vending Machine System in Urban Rail Transit

The technology of automatic fare collection system for urban rail transit is an important technology for achieving automatic fare collection for public transportation facilities such as urban subways. This article studies the relevant characteristics of the AFC system. Through introducing queuing models, simulation comparative experiments, and neural network debugging, it is found that the automatic fare collection system for urban rail transit not only effectively helps station staff to allocate tickets in a timely and reasonable manner, adjust station ticket supply, but also facilitates station passengers to query tickets, and passengers can freely choose whether to take the train according to their actual needs. The experiment shows that the AFC system can effectively help passengers avoid traffic congestion during peak hours, greatly improve management level, and reduce labor intensity.

Cavity QED systems for steady-state sources of Wigner-negative light

We present a theoretical investigation of optical cavity quantum electrodynamics (cavity QED) systems, as described by the driven, open Jaynes–Cummings model and some of its variants, as potential sources of steady-state Wigner-negative light. We consider temporal modes in the continuous output field from the cavity and demonstrate pronounced negativity in their Wigner distributions for experimentally relevant parameter regimes. We consider models of both single and collective atomic spin systems, and find a rich structure of Wigner-distribution negativity as the spin size is varied. We also demonstrate an effective realization of all of the models considered using just a single 87Rb atom and based upon combinations of laser- and laser-plus-cavity-driven Raman transitions between magnetic sublevels in a single ground hyperfine state.

#2289 Pregnancy outcomes in patients with congenital abnormality of the urinary tract: experience of a tertiary Portuguese center

Abstract Background and Aims Congenital abnormalities of the urinary tract (CAKUT) represent a broad range of disorders that occur due to abnormal kidney development. Maternal, obstetric, and perinatal outcome in pregnant woman with CAKUT have not been extensively evaluated, although small series report an increased incidence of urinary tract infections (UTI) and obstructive uropathy. Method Retrospective analysis of maternal, obstetric, and perinatal outcomes in pregnant patients with CAKUT (including genetic cystic kidney disease) that were surveilled in our tertiary center by a nephro-obstetric team between 2011 and June 2023. Information was obtained from medical records. Results We evaluated 47 gestations in 41 patients, with mean age of 31 ± 5 years [17-40], 89% were Caucasian, 11% black; 40% were nulliparous, 36% had hypertension (HT) and 42% a previous history of UTI. Regarding CAKUT specificity, 17/41 patients had genetic cystic diseases, 10/41 ureteropelvic junction obstruction (UPJ), 4/41 duplicate collecting system, 5/41 vesicourethral reflux, 2/41 ectopic kidney, 3/41 renal hypoplasia. Genetic testing was done before, during or after pregnancy in 2/4/4 patients, respectively. Teratogenic therapy exposure occurred in 25% of gestations. Most patients were CKD stage 1 (33/41), with 2/4/1/1 with patients in CKD stage 2/3/4/5, respectively. Mean baseline SCr was 0.99 ± 0.8 mg/dL [0.4-4.6] and mean proteinuria was 194 ± 357 mg/g [22-1500]. Renal function (RF) deterioration occurred in 9/47 (19%) women due to pre-eclampsia (PE), pregnancy hyperfiltration (PH) and hydronephrosis in 3/3/3 patients, respectively. Urologic intervention was needed in 2 patients due to hydronephrosis, and one of the patients required transient dialysis. All patients fully recovered RF except for the 1 CKD stage 3 patients that had partial RF recovery. De novo and worsening proteinuria occurred in 7/47 gestations due to PE (3/7) and PH (4/7). De novo or aggravation of HTN occurred in 25% gestations, UTI in 19% and PE in 6% gestations. Regarding fetal outcomes, mean duration of gestation was 38 ± 2 weeks [32-41] and mean birth weight 2910 ± 634 g [1360-4600]. There was 1 miscarriage and 1 medical termination of pregnancy at 17 weeks (neural tube defect). Cesarean was performed in (8/47, 17%) gestations and 10/47 (17%) newborns were admitted to the neonatal care unit (NICU) mainly due to prematurity, sepsis and feeding intolerance. Conclusion Our cohort of CAKUT patients was very heterogenous and only 19.5% had CKD stage 2-5. Still, we found a significant incidence of maternal, obstetric, and perinatal complications, namely HT UTI, PE, and the need for NICU. UPJ obstruction can evolve with RF deterioration and the need for intervention during pregnancy. This study underlines the importance of the management of these patients by a multidisciplinary team.

Path Optimisation of Electric Vehicles for Collection and Delivery based on Intelligent Waste Bins

This paper proposes a dynamic electric vehicle path optimization method for intelligent garbage collection and transportation systems to address the increasing uncertainty of urban household waste and the carbon emissions caused by vehicles in traditional garbage collection and transportation processes. With the goal of minimizing total cost and maximizing garbage collection and transportation, adjust the soft time window in a timely manner based on whether the amount of garbage generated by the intelligent garbage bin reaches the collection and transportation threshold. Taking into account the position of the garbage bin to be cleared, the position of the garbage collection and transportation vehicle, the amount of electricity, and the loading capacity, dynamically adjust the vehicle path. Due to the fact that genetic algorithms require a long time to converge to an acceptable solution, this paper uses greedy genetic algorithms to comprehensively utilize their advantages in local search and global search capabilities to achieve faster convergence solutions. The research results indicate that compared with traditional static garbage collection and transportation modes, dynamic garbage collection and transportation schemes can reduce total costs and increase garbage collection and transportation volume, and reasonable planning of the path of electric collection and transportation vehicles can effectively reduce carbon emissions. Finally, the effectiveness and efficiency of the algorithm were verified through comparative analysis of numerical examples.

Transcatheter or Surgical Replacement for Failed Bioprosthetic Aortic Valves

The use of valve-in-valve (ViV) transcatheter aortic valve replacement (TAVR) has been rapidly expanding as an alternative treatment to redo surgical aortic valve replacement (SAVR) for failed bioprosthetic valves despite limited long-term data. To assess mortality and morbidity in patients undergoing intervention for failed bioprosthetic SAVR. This was a retrospective population-based cohort analysis conducted between January 1, 2015, and December 31, 2020, with a median (IQR) follow-up time of 2.3 (1.1-4.0) years. A total of 1771 patients with a history of bioprosthetic SAVR who underwent ViV-TAVR or redo SAVR in California, New York, and New Jersey were included. Data were obtained from the California Department of Health Care Access and Information, the New York Statewide Planning and Research Cooperative System, and the New Jersey Discharge Data Collection System. Exclusion criteria included undergoing TAVR or redo SAVR within 5 years from initial SAVR, as well as infective endocarditis, concomitant surgical procedures, and out-of-state residency. Propensity matching yielded 375 patient pairs. Data were analyzed from January to December 2023. ViV-TAVR vs redo SAVR. The primary outcome was all-cause mortality. Secondary outcomes were stroke, heart failure hospitalization, reoperation, major bleeding, acute kidney failure, new pacemaker insertion, and infective endocarditis. From 2015 through 2020, the proportion of patients undergoing ViV-TAVR vs redo SAVR increased from 159 of 451 (35.3%) to 498 or 797 (62.5%). Of 1771 participants, 653 (36.9%) were female, and the mean (SD) age was 74.4 (11.3) years. Periprocedural mortality and stroke rates were similar between propensity-matched groups. The ViV-TAVR group had lower periprocedural rates of major bleeding (2.4% vs 5.1%; P = .05), acute kidney failure (1.3% vs 7.2%; P < .001), and new pacemaker implantations (3.5% vs 10.9%; P < .001). The 5-year all-cause mortality rate was 23.4% (95% CI, 15.7-34.1) in the ViV-TAVR group and 13.3% (95% CI, 9.2-18.9) in the redo SAVR group. In a landmark analysis, no difference in mortality was observed up to 2 years (hazard ratio, 1.03; 95% CI, 0.59-1.78), but after 2 years, ViV-TAVR was associated with higher mortality (hazard ratio, 2.97; 95% CI, 1.18-7.47) as well as with a higher incidence of heart failure hospitalization (hazard ratio, 3.81; 95% CI, 1.57-9.22). There were no differences in 5-year incidence of stroke, reoperation, major bleeding, or infective endocarditis. Compared with redo SAVR, ViV-TAVR was associated with a lower incidence of periprocedural complications and a similar incidence of all-cause mortality through 2 years' follow-up. However, ViV-TAVR was associated with higher rates of late mortality and heart failure hospitalization. These findings may be influenced by residual confounding and require adjudication in a randomized clinical trial.

Case Study for Predicting Failures in Water Supply Networks Using Neural Networks

This study deals with the prediction of recurring failures in water supply networks, a complex and costly task, but essential for the effective maintenance of these vital infrastructures. Using historical failure data provided by Companhia de Água e Esgotos da Paraíba (CAGEPA), the research focuses on predicting the time until the next failure at specific points in the network. The authors divided the failures into two categories: Occurrences of New Faults (ONFs) and Recurrences of Faults (RFs). To perform the predictions, they used predictive models based on machine learning, more specifically on MLP (Multi-Layer Perceptron) neural networks. The investigation unveiled that through the analysis of historical failure data and the consideration of variables including altitude, number of failures on the same street, and days between failures, it is possible to achieve an accuracy greater than 80% in predicting failures within a 90-day interval. This demonstrates the feasibility of using fault history to predict future water supply outages with significant accuracy. These forecasts allow water utilities to plan and optimize their maintenance, minimizing inconvenience and losses. The article contributes significantly to the field of water infrastructure management by proposing the applicability of a data-driven approach in diverse urban settings and across various types of infrastructure networks, including those pertaining to energy or communication. These conclusions underscore the paramount importance of systematic data collection and analysis in both averting failures and optimizing the allocation of resources within water utilities.

IMPLEMENTATION OF A BASE OF RULES FOR DIFFERENTIAL DIAGNOSIS OF CLINICAL AND HEMATOLOGICAL SYNDROMES BASED ON MORPHOLOGICAL CLASSIFICATION ALGORITHM

The evolving landscape of modern medicine underscores the growing importance of automating diagnostic processes. This advancement is not merely a convenience but a necessity to harness the full potential of technological progress, aiming to elevate research and clinical outcomes to new heights. Among the innovative strides in this field, the development of diagnostic systems based on morphological classification algorithms stands out. Such systems, rooted in comprehensive rule bases for differential diagnosis, promise to revolutionize the way we approach complex medical conditions. This paper introduces a cutting-edge system that epitomizes this evolution. Designed to harness the power of data analysis, it paves the way for groundbreaking research opportunities. At the heart of this system is a sophisticated set of rules derived from a morphological classification algorithm. This foundation enables the system to perform automated diagnoses of a wide array of clinical and hematological syndromes with unprecedented accuracy. A notable application of this technology is its ability to diagnose anemia by analyzing six distinct blood parameters and further categorize the anemia type based on biochemical criteria. The implications of such diagnostic capabilities are profound. By enabling the systematic collection and analysis of statistical data, the system facilitates in-depth research into the prevalence of diseases across different demographic groups. It aids in identifying disease patterns and supports preventive medicine efforts, potentially shifting the paradigm from treatment to prevention. This study not only highlights the system's capacity for enhancing diagnostic precision but also emphasizes its role as a catalyst for medical research and the improvement of healthcare delivery. The integration of such technologies into the medical field promises to enhance the quality of care, streamline diagnostic processes, and open new avenues for medical research, ultimately contributing to the advancement of global health standards.

Current evidence on the relationships among five polymorphisms in the matrix metalloproteinases genes and prostate cancer risk.

Matrix metalloproteinases (MMPs) had a variety of subtypes, which may be related to tumor invasion and angiogenesis, and the polymorphisms from MMPs have been also associated with the susceptibility to a variety of tumors, including prostate cancer (PCa). However, previous studies have not systematically analyzed the association between MMP and prostate cancer, so we conducted systematic data collection and analyzed to evaluate the relationship among polymorphisms in MMPs and PCa susceptibility. We searched PubMed, Web of Science, Embase and Google Scholar for all papers published up to Apr 3rd, 2023, and systematically analyzed the relationship among MMP1-1607 2G/1G, MMP2-1306T/C, MMP2-735T/C, MMP7-181 G/A, MMP9-1562T/C and PCa susceptibility using multiple comparative models and subgroup analyses. We found that MMP2-1306T/C polymorphism showed associations with PCa susceptibility, with the Ethnicity subgroup (Asian) being more pronounced. Similarly, MMP9-1562T/C has also had associations with PCa susceptibility. Our current study found that the polymorphisms of, MMP2-1306T/C, and MMP9-1562T/C had strong associations with PCa risk.

Connecting digital twins to control collections systems and water resource recovery facilities: From siloed to integrated urban (waste)water management

ABSTRACT The use of digital twins is a rapidly emerging field for improved real-time control (RTC) and decision support for the operation of collection systems and water resource recovery facilities (WRRFs). Digital twins for collection systems can help minimize the impacts of flow variation due to extreme weather events, attenuate flows to the WRRF, and reduce sewer overflows and the associated effects. Similarly, digital twins for WRRFs can help improve process, energy, and cost efficiency, fully utilise plant volumes, reduce carbon footprint, and support operator training. The current study provides an overview of two digital twin applications for collection systems (Future City Flow) and WRRFs (TwinPlant) and presents a first example of digital twin integration for proactive collection system-WRRF operation under wet-weather conditions. Current applications of the integrated digital twin are described, including (i) proactive implementation of wet-weather operation mode in WRRF based on inflow forecast and (ii) evaluation of the impacts of RTC in collection systems on WRRF performance. Other potential application examples are described together with the challenges related to the use of this solution. Overall, this new approach has a wide potential to support the cooperation within water utilities towards the adoption of integrated wastewater management.

Discrimination of New and Aged Seeds Based on On-Line Near-Infrared Spectroscopy Technology Combined with Machine Learning

The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky–Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1 derivative (SG-D1), Savitzky–Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed.