Local Control Strategies Research Articles

Enhancing PV Hosting Capacity of Electricity Distribution Networks Using Deep Reinforcement Learning-Based Coordinated Voltage Control

Coordinated voltage control enables the active management of voltage levels throughout electricity distribution networks by leveraging the voltage support capabilities of existing grid-connected PV inverters. The efficient management of power flows and precise voltage regulation through coordinated voltage control schemes facilitate the increased adoption of rooftop PV systems and enhance the hosting capacity of electricity distribution networks. The research work presented in this paper proposes a coordinated voltage control scheme and evaluates the enhanced hosting capacity utilizing a deep reinforcement learning-based approach. A comparative analysis of the proposed algorithm is presented, and the performance is benchmarked against existing local voltage control schemes. The proposed coordinated voltage control scheme in this paper is evaluated using simulations on a real-world low-voltage electricity distribution network. The evaluation involves quasi-static time series power flow simulations for assessing performance. Furthermore, a discussion is presented that reflects on the strengths and limitations of the proposed scheme based on the results observed from the case study.

Deviation control strategy for electric vehicle virtual energy storage dispatching instruction execution

Abstract As flexible resources, electric vehicles (EVs) participate in the dispatching of power grid regulation. Due to the randomness of entering and exiting the station, each EV cluster cannot fully respond to the guiding power instructions issued by the operator, resulting in biased values. This paper establishes a deviation management and control model, and proposes a local deviation control strategy and a cross-cluster collaborative deviation control strategy based on the model, so as to fully utilize the deviation control capabilities of each EV’s virtual energy storage and minimize the power deviation. The effectiveness of the proposed deviation control strategy for scheduling instructions is verified by numerical examples.

Anti-seepage measures for the upper reservoir of one pumped-storage power station

Abstract Seepage control plays an important role in the design of pumped storage power station. In this paper, the seepage characteristics and geological conditions of the upper reservoir of a pumped storage power station are analyzed, and the natural seepage of the upper reservoir is calculated. According to the characteristics and amount of leakage, the upper reservoir can be controlled by local seepage control scheme, and the anti-seepage treatment scheme of the south reservoir bank is compared and analyzed. After the local anti-seepage treatment of upper reservoir, the daily seepage volume decreased from 5585m3/d to 2304m3/d, which indicates that the selected anti-seepage scheme is suitable. This paper can provide useful reference for similar projects in the future.

Local Light Field Control Enables Efficient Quantum Dot Color Conversion Films for Mini‐LED Backlit Displays

AbstractQuantum dot (QD) color conversion films (QD‐CCFs) are used in ultrahigh definition (UHD) Mini‐LED backlit displays due to their narrow band emission and high color purity. However, their poor light conversion efficiency (LCE) leads to greater film thickness and thus high cost. Herein, this study proposes a local light field control strategy to enhance the LCE of QD‐CCFs using high‐refractive BN nanosheets. The LCEs of the green and red QD‐CCFs can increase from 29.9% and 34.8% to 67.4% and 91.0% after adding BN nanosheets, respectively. Combining the experimental data with the finite‐difference time domain (FDTD) simulations, the enhanced LCEs can be attributed to observably increased blue light absorption and improved light extraction efficiency of the light emitted from QDs. The white Mini‐LEDs, prepared by integrating the optimized QD‐CCFs containing BN nanosheets with blue Mini‐LED chips, show a high luminous efficiency of 70.2 lm W−1, a high external quantum efficiency of 36.2%, and a wide color gamut of 96.3% Rec. 2020 standards. This work provides an interesting idea for designing high‐efficiency QD‐CCFs toward low‐cost UHD Mini‐LED backlit displays.

Elucidating transport dynamics and regional division of PM2.5 and O3 in China using an advanced network model

Air pollution exhibits significant spatial spillover effects, complicating and challenging regional governance models. This study innovatively applied and optimized a statistics-based complex network method in atmospheric environmental field. The methodology was enhanced through improvements in edge weighting and threshold calculations, leading to the development of an advanced pollutant transport network model. This model integrates pollution, meteorological, and geographical data, thereby comprehensively revealing the dynamic characteristics of PM2.5 and O3 transport among various cities in China. Research findings indicated that, throughout the year, the O3 transport network surpassed the PM2.5 network in edge count, average degree, and average weighted degree, showcasing a higher network density, broader city connections, and greater transmission strength. Particularly during the warm period, these characteristics of the O3 network were more pronounced, showcasing significant transport potential. Furthermore, the model successfully identified key influential cities in different periods; it also provided detailed descriptions of the interprovincial spillover flux and pathways of PM2.5 and O3 across various time scales. It pinpointed major pollution spillover and receiving provinces, with primary spillover pathways concentrated in crucial areas such as the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas, the Yangtze River Delta, and the Fen-Wei Plain. Building on this, the model divided the O3, PM2.5, and synergistic pollution transmission regions in China into 6, 7, and 8 zones, respectively, based on network weights and the Girvan Newman (GN) algorithm. Such division offers novel perspectives and strategies for regional joint prevention and control. The validity of the model was further corroborated by source analysis results from the WRF-CAMx model in the BTH area. Overall, this research provides valuable insights for local and regional atmospheric pollution control strategies. Additionally, it offers a robust analytical tool for research in the field of atmospheric pollution.

Long-Term Results of Stereotactic Radiotherapy in Patients with at Least 10 Brain Metastases at Diagnosis.

to evaluate an SRT approach in patients with at least 10 lesions at the time of BM initial diagnosis. This is a monocentric prospective cohort of patients treated by SRT, followed by a brain MRI every two months. Subsequent SRT could be delivered in cases of new BMs during follow-up. The main endpoints were local control rate (LCR), overall survival (OS), and strategy success rate (SSR). Acute and late toxicity were evaluated. Seventy patients were included from October 2014 to January 2019, and the most frequent primary diagnosis was non-small-cell lung cancer (N = 36, 51.4%). A total of 1174 BMs were treated at first treatment, corresponding to a median number of 14 BMs per patient. Most of the patients (N = 51, 72.6%) received a single fraction of 20-24 Gy. At 1 year, OS was 62.3%, with a median OS of 19.2 months, and SSR was 77.8%. A cumulative number of 1537 BM were treated over time, corresponding to a median cumulative number of 16 BM per patient. At 1-year, the LCR was 97.3%, with a cumulative incidence of radio-necrosis of 2.1% per lesion. Three patients (4.3%) presented Grade 2 toxicity, and there was no Grade ≥ 3 toxicity. The number of treated BMs and the treatment volume did not influence OS or SSR (p > 0.05). SRT was highly efficient in controlling the BM, with minimal side effects. In this setting, an SRT treatment should be proposed even in patients with ≥10 BMs at diagnosis.

A frequency cooperative control strategy for multimicrogrids with EVs based on improved evolutionary-deep reinforcement learning

The construction of microgrid has fully promoted the large-scale access of distributed power and the rapid development of electric vehicles (EVs). And microgrid system faces operational issues such as strong random disturbances from distributed power sources and loads, as well as unexpected events during operation. These issues can lead to unstable microgrid frequency, excessive discharge of EVs, and increased control costs. To solve the issues, a frequency cooperative control strategy for multimicrogrids with EVs based on improved evolutionary-deep reinforcement learning (EDRL) is proposed in this paper. First, consider the effect of the vehicle-to-grid (V2G) process on the minimum time required to fully charge an EV, and the impact of the output distribution of micro turbines (MTs) on the regulation cost, and based on the coupling relationship between generator terminal voltage regulation and system frequency control, a multimicrogrid comprehensive control model is constructed. Second, in order to deal with engineering tasks with deceptive rewards and sparse rewards such as integrated frequency control, evolutionary algorithms and deep reinforcement learning algorithms are combined to effectively assist the training process to jump out of local optimal solutions and approach optimal control strategies. Meanwhile, the algorithm is improved based on the novelty search and intelligent partition strategy, so that the strategy has better convergence characteristics and can reduce the cost of information transmission and computational complexity under the premise of ensuring the control effect. Furthermore, the state space, action space and reward function of the controller are defined. Finally, the simulation results show that the proposed controller has the ability of coordinated control, can effectively reduce the adjustment cost of MT unit and the unnecessary discharging of EVs while ensuring the frequency regulation requirements of each submicrogrid, which is far superior to PID control, fuzzy control, and traditional deep reinforcement learning control.

Mobile measurement background quantification methods for determining local traffic emissions and background source contributions to ammonia

On-road ammonia (NH3) could be critical for public health in high-population areas due to its contribution to fine particulate matter (PM2.5) formation. Therefore, quantifying the impact of vehicular NH3 emissions on pollution levels is vital from both policy and exposure perspectives. In this study, we conducted night- and daytime mobile measurements during a pollution episode in March 2021, explored common data processing strategies for background concentration estimation and calculated the vehicle contribution in urban Beijing. Percentile and time–frequency methods were evaluated and applied to separate local vehicle and background concentrations. The spatial distributions differed between the night- and daytime periods. However, vehicular emissions were an important source of NH3 both at night and during the day. In the pollution accumulation process, the vehicle contribution increased to 27.4%. Even though local traffic emissions notably impacted NH3 levels, the background concentration, including regional transport, was the largest contributor to NH3 at the city scale. This study has implications for local and regional NH3 control strategies aimed at reducing pollution in densely populated urban areas.

Seroprevalence and risk factors of Toxoplasma gondii infection in goats from South Punjab Province, Pakistan

Layyah District in South Punjab Province of Pakistan offers the most intensive caprine economy in the country; its Indus riverine and desert environment makes the area peculiar and worthy of specific investigations. This study aimed to investigate the prevalence of anti-Toxoplasma gondii (T. gondii) IgG-antibody in goats in serum samples and the potential risk factors. The prevalence of T. gondii infection was estimated using a two-stage sample design. All caprine farms in the study area were stratified by size, and from these 110 were randomly selected. Twelve goats (>1-year-old) were selected from each farm and a total of 1320 serum samples were collected and tested by ELISA. A questionnaire on the conditions and management practices of each farm was administered to 110 farmers. Four hundred and sixteen out of 1320 sera samples (31.5%) were found positive and 89% of the flock had at least one seropositive goat. The proportion of seropositive goats tested within each flock ranged from 8.3% to 83.3%. with several factors contributing to this heterogeneity. Goat age played a significant role in the presence of cats. Significant interactions were related to goat farms having floor of dirt and kitten presence. Moreover, age class, abortion history and water source supply were modulated by owner education levels. This is the first study to determine the prevalence of anti-T. gondii antibodies in goats sera in Layyah district and the largest carried out so far in Pakistan. The remarkable presence of T. gondii among goats in areas where goat farming plays a significant economic role may pose a production threat to the small-stock industry, as well as to public health and food safety. Therefore, investigations to identify high-risk goat populations are highly recommended in order to facilitate the implementation of local control strategies.

Exploration on the Mildew Conditions of Cladosporium asperulatum CY-H1, a Pathogenic Fungus on Tobacco Leaves

Aims: Mildew poses a significant threat to tobacco industry. This study focuses on a strain of Cladosporium asperulatum isolated from moldy tobacco leaves to investigate its growth under different temperature and humidity conditions, aiming to provide a theoretical basis for local prevention and control strategies against this fungus.
 Methodology: Potential fungal strains were obtained from moldy tobacco leaves utilizing isolation techniques and individual colonies were cultured on PDA medium subsequently. Microscopic examination and molecular identification were employed to determine the species of the isolated strains. Isolated strain was initially inoculated with varying spore powder concentrations at water activities of 0.99, 0.95, 0.90, 0.87, 0.83, and 0.77, combined with diverse culture temperatures of 15°C, 20°C, 25°C, 30°C, and 35°C on PDA. The growth of colony area was daily measured by ImageJ software, and spore powder yield was observed after a 7-day incubation period under these specified culture conditions.
 Results: (1) colony area of isolated C. asperulatum CY-H1 has a rising trend with increasing culture time at consistent temperatures. Sporulation exhibited an initial increase followed by a decrease, yet overall demonstrated an upward trajectory. The maximum value was attained at a water activity of 0.95. Under identical water activity, escalating temperatures initially augmented colony area until reaching a maximum at 25°C. Similarly, spore production displayed an initial increase followed by a decrease, with the highest production occurring at 25°C. (2) Varied concentrations of initial spore suspension influenced colony growth differently. A high concentration (initially 106 spores/mL) exhibited an earlier growth trend compared to medium (105 spores/mL) and low concentrations (104 spores/mL). (3) C. asperulatum CY-H1 did not thrive under the culture conditions of 35°C, irrespective of the initial inoculation concentration and water activity.
 Conclusion: C. asperulatum assumes a crucial role as a predominant mold in tobacco storage environments. Effective prevention of fungal mildew in stored tobacco leaves is achieved by maintaining temperatures exceeding 35°C and sustaining water activity below 0.77. These findings highlight the significance of environmental conditions in controlling mold growth during tobacco storage.

Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment: An Overview of the Special Issue

This paper summarizes the results of a Special Issue focusing on the practical applications of model predictive control and other advanced control methods in the built environment. This Special Issue contains eleven publications and deals with various topics such as the virtual sensing of indoor air pollutants and prediction models for indoor air temperature and building heating and cooling loads, as well as local and supervisory control strategies. The last three publications tackle the predictive maintenance of chilled water systems. Most of these publications are field demonstrations of advanced control solutions or promising methodologies to facilitate the adoption of such control strategies, and they deal with existing buildings. The Special Issue also contains two review papers that provide a comprehensive overview of practical challenges, opportunities, and solutions to improve building operations. This article concludes with a discussion of the perspectives of advanced controls in the built environment and the increasing importance of data-driven solutions.

A cross-sectional pilot household study of Schistosoma mansoni burden and associated morbidities in Lake Albert, Uganda.

Schistosomiasis is persistent in Lake Albert, Uganda, but local data are limited. This study aims to describe the local burden of moderate-to-heavy infection and associated morbidity in all ages and identify factors associated with these outcomes to guide further research. This cross-sectional pilot study was conducted in July-August, 2022 in four village sites (Walukuba, Rwentale, Kyabarangwa and Runga) of the Praziquantel in Preschoolers (PIP) trial. Residents (approximately four per household) of any age of households of PIP participants were eligible, but individuals <10 years were only enrolled if no older individuals were available. Socio-demographic information, household location, single stool Kato-Katz and hepatic ultrasound results were obtained for a convenience sampled subset of trial households. The primary outcome, moderate-to-heavy infection (≥100 eggs per gram of faeces), was analysed using mixed-effects logistic regression, with a household random effect. Univariate analyses were used for the secondary outcome, periportal fibrosis (Niamey protocol ultrasound image pattern C-F). Of 243 participants with a median age of 22 (interquartile range 12-33) years from 66 households, 49.8% (103/207 with a Kato-Katz result) had moderate-to-heavy infection and 11.2% (25/224 with ultrasound data) had periportal fibrosis. Moderate-to-heavy infection clustered by household (intraclass correlation coefficient = 0.11) and, in multivariable analysis, varied by village (Walukuba vs. Kyabarangwa adjusted odds ratio [aOR] 0.11, 95% CI 0.02-0.71), was highest in participants aged 10-15 years (vs. 5-9 years aOR 6.14, 95% CI 1.61-23.38) and lower in those reporting praziquantel treatment in the past year (aOR 0.39, 95% CI 0.18-0.88). In this setting, schistosome infection and morbidity are pervasive in all age groups. More intensive interventions are needed, for example more frequent praziquantel treatment, under investigation in the PIP trial and improved water and sanitation. More research is needed to understand local treatment barriers and optimal control strategies.

Mathematical Modelling and Computational Dynamics of the impact of Quasi-Lockdown Policy in Control of COVID-19 in Akwa Ibom State, Nigeria

Determining the impact of a local control strategy, implemented to curtail the spread of COVID-19 has become obligatory to enable policy makers to combat the so-called long covid. This research used a deterministic SEIAHR-model, incorporating a logistic function to investigate the effect of quasi-lockdown policy during reemergence of corona virus pandemic. The notion of uniform convergence and fixed point theory were used to establish positive invariant region, ultimate boundedness, and existence of unique solution of the model. Nonlinear dynamical behaviours of the model such as stability and oscillating flows occurred at the disease free, and endemic equilibrium points. Using centre manifold theory, a trans-critical bifurcation with hysteresis effect were established, when quasi-lockdown policy was used as a control parameter in the model. A forward sensitivity analysis was conducted to unfold parameters that contributed significantly towards the spread and control of the infection. These parameters were estimated and fitted using least square technique, comparative to the observed real datasets adapted from the Nigeria Centre for Disease Control (NCDC). The polynomial regression model fitted the observed datasets with an average coefficient of determination given as R2 = 0.94. Simulations in form of phase space diagrams were used to validate the theoretic results. Epidemiologically, the model demonstrated that quasi-lockdown policy was necessarily a strategic policy to curb the spread of the infection, but not sufficient to eradicate the disease as the disease persisted endemically. It was shown that the basic reproduction number of the infection was within the critical threshold (R0 \(\le\) 1), when the lockdown policy was enforced strictly, but otherwise on relaxation of the lockdown policy (R0 \(\ge\) 1) Thus a relaxation of the quasi-lockdown policy leads to increase in susceptibility of the populace as the infection risk ratio increases in the model. Similarly, the model predicted that a pre-mature lifting of the lockdown policy could have led to high infectivity on the susceptible class and disastrous to healthy living of the citizens.

Performance analysis of the comprehensive energy system based on active energy storage-discharge technology under time-sharing electricity price operation strategy

Multi-energy complementary comprehensive energy systems based on renewable energy can reliably meet the energy needs of communities and significantly mitigate carbon emissions. This is of paramount significance in the establishment of low-carbon sustainable communities. The effective operation of an integrated energy system necessitates a thorough examination of demand-side load, the dynamic performance of the energy supply system, the configuration of energy storage systems, local electricity pricing policies, and operational or control strategies for the system. It confronts substantial challenges in practical application. Considering the noteworthy performance variations of comprehensive energy systems under diverse demand-side load, fluctuating electricity prices, and various operating modes of energy storage equipment, this paper initially conducts a practical analysis of demand-side load. A comprehensive energy system with multi-energy complementary based on source-load-storage coordination (SLS-CES) model was constructed. From the perspective of system operation strategy, we identify the current electricity consumption pattern in the region, characterized by peak and valley periods. We integrate this information with the time-of-use electricity pricing policy and the equipment within the target area. An operation strategy based on local time-of-use electricity price policy was proposed. An in-depth analysis of the thermal performance, environmental performance, and economic performance of the system in the full-year dimension and typical daily dimension under this operating strategy was conducted. The results show that under this operation strategy, the system's annual net income is 415.90 thousand yuan, of which the system's energy storage battery power supply revenue during peak period reaches 254.40 thousand yuan. The initial investment is 5.03 million yuan, and the static investment payback period is 12.09 years. The annual primary energy saving rate of the system is 36.00%, and the maximum CO2 emission reduction rate is 72.23%. The maximum primary energy saving rates on typical days in winter and typical days in summer are 74.74% and 97.14% respectively. The total annual CO2 emissions were reduced by 5.47 tons and 4.54 tons respectively. Through this study, we found that a system incorporating energy storage equipment, combined with an operation strategy based on electricity price policy, can yield additional economic benefits. This approach helps alleviate peak power supply pressure on the grid and mitigates the challenges associated with the high initial investment in renewable energy systems, all while maintaining good thermal performance and environmental benefits.

Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation

This paper develops a data-driven framework to synthesize local Volt/Var control strategies for distributed energy resources (DERs) in power distribution grids (DGs). Aiming to improve DG operational efficiency, as quantified by a generic optimal reactive power flow (ORPF) problem, we propose a two-stage approach. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> stage involves learning the manifold of optimal operating points determined by an ORPF instance. To synthesize local Volt/Var controllers, the learning task is partitioned into learning local surrogates (one per DER) of the optimal manifold with voltage input and reactive power output. Since these surrogates characterize efficient DG operating points, in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">second</i> stage, we develop local control schemes that steer the DG to these operating points. We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DG operating point agreeing with the local surrogates. We use neural networks to model the surrogates and enforce the identified conditions in the training phase. AC power flow simulations on the IEEE 37-bus network empirically bolster the theoretical stability guarantees obtained under linearized power flow assumptions. The tests further highlight the optimality improvement compared to prevalent benchmark methods.

ОЦЕНКА ПРОПУСКНОЙ СПОСОБНОСТИ УЧАСТКА УЛИЧНО-ДОРОЖНОЙ СЕТИ ПУТЕМ ПРИМЕНЕНИЯ МОДЕЛИ МАКСИМИЗАЦИИ ЭНТРОПИИОЦЕНКА ПРОПУСКНОЙ СПОСОБНОСТИ УЧАСТКА УЛИЧНО-ДОРОЖНОЙ СЕТИ ПУТЕМ ПРИМЕНЕНИЯ МОДЕЛИ МАКСИМИЗАЦИИ ЭНТРОПИИОЦЕНКА ПРОПУСКНОЙ СПОСОБНОСТИ УЧАСТКА УЛИЧНО-ДОРОЖНОЙ СЕТИ ПУТЕМ ПРИМЕНЕНИЯ МОДЕЛИ МАКСИМИЗАЦИИ ЭНТРОПИИ

The article assesses the capacity of a section of the road network using an entropy maximization model. The purpose of this study is to evaluate the correspondence matrix using the entropy model using the example of a specific section of the road network (Moskovskaya St. and Glinki St., Angarsk). The initial data of the model is processed in accordance with the encoding of the directions of movement at the intersection, which is used to simplify the input of field measure-ments of traffic flow intensity. As part of the research, the correspondence matrix is used in the development and changes of the local traffic intensity control scheme, the introduction of bans and for prompt response to the redistribution of traffic flow in within the urban space

Leveraging the Multidimensional Poverty Index to estimate Helicobacter pylori prevalence in districts in Karnataka, India.

The prevalence of Helicobacter pylori (Hp) in India is approximately 64%. However, substantial regional variations necessitate high-fidelity, locality-specific studies to characterise its population distribution in India. We conducted a cross-sectional study including 2998 dyspeptic patients at a tertiary care centre in Karnataka and investigated the relationship between Hp prevalence and the Multidimensional Poverty Index (MPI) by district. Helicobacter pylori prevalence in our population was 43.2% with a significant correlation between district-specific Hp prevalence and MPI. This data can be leveraged for evaluating local Hp infection control strategies, planning interventions for Hp hotspots in Karnataka and predicting a dyspeptic patient's risk of Hp infection.

Patterns of Failure in Pediatric and Young Adult Rhabdomyosarcoma

To characterize patterns of failure in pediatric and young adult patients with rhabdomyosarcoma (RMS) from a single institution with over 20 years of experience. Patients diagnosed with RMS from 2000 to 2022 were identified retrospectively. Time to failure was calculated from diagnosis. Local only failure was defined as first failure at the primary site without distant failure. Distant failure was defined as first failure outside of the primary site with or without local failure. Cumulative incidence (CI) of failure was calculated using death as a competing risk. Fine-Gray regression was used to evaluate impact of prognostic factors. Ninety-five patients were eligible. Median age was 7.28 years (range 0 - 35 years), 41% of patients were >10 years old. Median follow up was 33.3 months. Approximately half (n = 47, 49.5%) of the tumors demonstrated alveolar histology. FOXO1 fusion status was available in 76 (80%) patients, of which 7 out of 37 alveolar tumors (18.9%) were FOXO1 fusion negative. The majority of tumors presented with unfavorable primary site (n = 72, 75.8%) and advanced stage (Stage III and IV, n = 72, 75.8%). The 5-yr CI of local only failure and distant failure for the entire cohort was 19.0% (95% CI 11.3, 28.3) and 34.6% (24.0, 45.5%), respectively. The predominant pattern of failure by Group was: Groups 1&2: Local only (5yr CI 14.8%), Group 3: Distant (5yr CI: 25.9%), Group 4: Distant (5yr CI: 67.6%). CI of distant failure by primary site was higher in perianal/gluteal (n = 2/5, 5yr CI 60.0%) and extremity (n = 8/19, 5yr CI 45.9%) sites. Of the 28 distant failures, 10 (36%) also had a local failure component. CI of local only failure by primary site was higher in parameningeal head and neck (n = 6/25, 5yr CI 30%) and bladder/prostate (n = 2/12, 5yr CI 23%) sites. The following were associated with an increased CI of distant failures: increasing age (HR 1.08, p<0.01), alveolar vs. embryonal histology (HR 3.01, p = 0.0095), FOXO1 fusion positive vs. negative (HR 2.8, p = 0.02) and Group IV vs. Groups I/II (HR 7.7, p = 0.0007). FOXO1 fusion and alveolar histology were associated with older age and Group IV, both of which were independently associated with increased distant failure on multivariate analysis. Failures were predominantly distant in older patients and patients with Group IV RMS, both of which were associated with FOXO1 fusion and alveolar histology, highlighting the need to improve therapies in this population. Local only failures were highest in parameningeal head and neck and bladder/prostate primaries, highlighting the need to improve local control strategies at these sites.

A low-complexity multi-channel active noise control system using local secondary path estimation and clustered control strategy for vehicle interior engine noise

Conventional multi-channel active noise control (ANC) system based on the adaptive notch filtered-x least mean square algorithm is typically used for active control of vehicle interior engine noise with prominent harmonic characteristics. However, due to the large estimated secondary path length and the centralized control strategy, the system has high computational complexity and fragile stability. To alleviate this problem, an efficient multi-channel ANC system using the local secondary path (LSP) estimation and clustered control strategy is proposed in this paper. Based on the modified LSP estimation method, the system may utilize a set of low-order but accurate LSP models to simplify the convolution operations of reference filtering. Besides, the proposed clustered control strategy combines the advantages of centralized and decentralized control strategies, and thus the system not only enjoys good noise attenuation performance and stability, but also its computational burden is further greatly reduced. Numerical simulations are performed to evaluate the noise attenuation performance and frequency mismatch effect of the proposed multi-channel ANC system. In addition, a series of real vehicle ANC experiments are conducted. The results show that, under stationary work conditions, the interior overall, 2nd order, 4th order, 6th order engine noises may be attenuated by 12.91 dB(A), 32.98 dB(A), 15.34 dB(A) and 8.74 dB(A), respectively, and under nonstationary work conditions, the maximum overall noise reductions at the four error microphones may reach 6.40 dB(A), 6.14 dB(A), 12.48 dB(A) and 11.07 dB(A), respectively. This fully confirms the effectiveness of the proposed multi-channel ANC system in real-world applications.

Active surveillance of carbapenemase-producing Enterobacterales using genomic sequencing for hospital-based infection control interventions.

Whole-genome sequencing (WGS) is increasingly used to characterize hospital outbreaks of carbapenemase-producing Enterobacterales (CPE). However, access to WGS is variable and testing is often centralized, leading to delays in reporting of results. We describe the utility of a local sequencing service to promptly respond to facility needs over an 8-year period. The study was conducted at Royal Prince Alfred Hospital in Sydney, Australia. All CPE isolated from patient (screening and clinical) and environmental samples from 2015 onward underwent prospective WGS. Results were notified to the infection control unit in real time. When outbreaks were identified, WGS reports were also provided to senior clinicians and the hospital executive administration. Enhanced infection control interventions were refined based on the genomic data. In total, 141 CPE isolates were detected from 123 patients and 5 environmental samples. We identified 9 outbreaks, 4 of which occurred in high-risk wards (intensive care unit and/or solid-organ transplant ward). The largest outbreak involved Enterobacterales containing an NDM gene. WGS detected unexpected links among patients, which led to further investigation of epidemiological data that uncovered the outpatient setting and contaminated equipment as reservoirs for ongoing transmission. Targeted interventions as part of outbreak management halted further transmission. WGS has transitioned from an emerging technology to an integral part of local CPE control strategies. Our results show the value of embedding this technology in routine surveillance, with timely reports generated in clinically relevant timeframes to inform and optimize local control measures for greatest impact.