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The advancement in water treatment technology has revolutionized the progress of membrane bioreactor (MBR) technology in the modern era. The large space requirement, low efficiency, and high cost of the traditional activated sludge process have given the necessary space for the MBR system to come into action. The conventional activated sludge (CAS) process and tertiary filtration can be replaced by immersed and side-stream MBR. This article outlines the historical advancement of the MBR process in the treatment of industrial and municipal wastewaters. The structural features and design parameters of MBR, e.g., membrane surface properties, permeate flux, retention time, pH, alkalinity, temperature, cleaning frequency, etc., highly influence the efficiency of the MBR process. The submerged MBR can handle lower permeate flux (requires less power), whereas the side-stream MBR can handle higher permeate flux (requires more power). However, MBR has some operational issues with conventional water treatment technologies. The quality of sludge, equipment requirements, and fouling are major drawbacks of the MBR process. This review paper also deals with the approach to address these constraints. However, given the energy limitations, climatic changes, and resource depletion, conventional wastewater treatment systems face significant obstacles. When compared with CAS, MBR has better permeate quality, simpler operational management, and a reduced footprint requirement. Thus, for sustainable water treatment, MBR can be an efficient tool.

ROV operators often encounter challenges with orientation awareness while operating underwater, primarily due to relying solely on 2D camera feeds to manually control the ROV robot arm. This limitation in underwater visibility and orientation awareness, as observed among Malaysian ROV operators, can compromise the accuracy of arm placement, and pose a risk of tool damage if not handle with care. To address this, a 3D orientation monitoring system for ROVs has been developed, leveraging measurement sensors with nine degrees of freedom (DOF). These sensors capture crucial parameters such as roll, pitch, yaw, and heading, providing real-time data on the ROV’s position along the X, Y, and Z axes to ensure precise orientation. These data are then utilized to generate and process 3D imaging and develop a corresponding 3D model of the operational ROV underwater, accurately reflecting its orientation in a visual representation by using an open-source platform. Due to constraints set by an agreement with the working class ROV operators, only short-term tests (up to 1 min) could be performed at the dockyard. A video demonstration of a working class ROV replica moving and reflecting in a 3D simulation in real-time was also presented. Despite these limitations, our findings demonstrate the feasibility and potential of a cost-effective 3D orientation visualization system for working class ROVs. With mean absolute error (MAE) error less than 2%, the results align with the performance expectations of the actual working ROV.

Dear Readers, Welcome to this Special Edition of the IMH Magazine, dedicated to the complex and sometimes unnoticed topic of medical repatriation and maritime Telemedical Assistance Services (TMAS). As you will learn in the pages that follow, in maritime medicine, the decision to send a seafarer home is rarely a simple logistical matter. It is frequently a medical judgment that has been shaped by limited onboard resources, operational realities of the vessel while at sea, and the physician's responsibility to maintain a balance between clinical care and the safety and wellbeing of the individual seafarer. Additionally, the regulatory framework provided by the Maritime Labour Convention (MLC 2006) emphasizes the importance of ensuring appropriate medical care and repatriation when illness or injury occurs far from shore. This issue of the Magazine brings together several perspectives from experienced TMAS practitioners who share insights drawn from their daily clinical work supporting ships around the world. Their contributions highlight the complexity of medical decision-making in maritime environments and the collaborative nature of telemedical assistance. We hope that this special edition will not only be an engaging read but can also serve as a useful reference for maritime health professionals who may one day face the difficult question of when going home becomes, above all, a medical decision. On behalf of the editorial team, thank you for joining us in exploring this important aspect of maritime medicine. Warm regards, James A. Denham, MD Editor, IMH Magazine.

Due to environmental concerns, the energy-saving train regulation is necessary for urban metro transportation, which can improve the service quality and energy efficiency of metro lines. In contrast to most of the existing research of train regulation based on centralized control, this paper studies the energy-saving train regulation problem by utilizing distributed model predictive control (DMPC), which is motivated by the breakthrough of vehicle-based train control (VBTC) technology and the pressing real-time control demand. Firstly, we establish a distributed control framework for train regulation process assuming each train is self-organized and capable to communicate with its preceding train. Then we propose a DMPC algorithm for solving the energy-saving train regulation problem, where each train determines its control input by minimizing a constrained local cost function mainly composed of schedule deviation, headway deviation, and energy consumption. Finally, simulations on train regulation for the Beijing Yizhuang metro line are carried out to demonstrate the effectiveness of the proposed DMPC algorithm, and the results reveal that the proposed algorithm exhibits significantly improved real-time performance without deteriorating the service quality or energy efficiency compared with the centralized MPC method.

Enhancing the efficiency of Rankine cycles is crucial for improving the performance of thermal power plants, as it directly impacts operational costs and emissions in light of energy transition goals. This study sets itself apart from existing research by applying a novel optimization technique to a basic ideal Rankine cycle, focusing on a specific power plant that has not been previously analyzed. Currently, this cycle operates at 41% efficiency and a steam quality of 76%, constrained by fixed operational parameters. The primary objectives are to increase thermal efficiency beyond 46% and raise steam quality above 85%, while adhering to operational limits: a boiler pressure not exceeding 15 MPa, condenser pressure not dropping below 10 kPa, and turbine temperature not surpassing 500°C. This study utilizes numerical simulations to model the effects of varying boiler pressure (Pb) and condenser pressure (Pc) within the ranges of 12 MPa < Pb < 15 MPa and 5 kPa < Pc < 10 kPa. By systematically adjusting these parameters, the proposed aim to identify optimal conditions that maximize efficiency and performance within specified constraints. The findings will provide valuable insights for power plant operators seeking to optimize performance under real-world conditions, contributing to more efficient and sustainable power generation.

Background Many countries are facing overlapping epidemics of tuberculosis (TB) and diabetes mellitus (DM). Diabetes increases the overall risk of developing Tuberculosis (TB) and contributes to adverse treatment outcomes. Active screening for both diseases can reduce TB transmission and prevent the development of complications of DM. We investigated bi-directional TB-DM screening in Karachi, Pakistan, a country that ranks fifth among high TB burden countries, and has the seventh highest country burden for DM. Methods Between February to November 2014, community-based screeners identified presumptive TB and DM through verbal screening at private health clinics. Individuals with presumptive TB were referred for a chest X-ray and Xpert MTB/RIF. Presumptive DM cases had random blood glucose (RBS) tested. All individuals with bacteriologically positive TB were referred for diabetes testing (RBS). All pre-diabetics and diabetics were referred for a chest X-ray and Xpert MTB/RIF test. The primary outcomes of this study were uptake of TB and DM testing. Results A total of 450,385 individuals were screened, of whom 18,109 had presumptive DM and 90,137 had presumptive TB. 14,550 of these individuals were presumptive for both DM and TB. The uptake of DM testing among those with presumptive diabetes was 26.1% while the uptake of TB testing among presumptive TB cases was 5.9%. Despite efforts to promote bi-directional screening of TB and DM, the uptake of TB testing among pre-diabetes and diabetes cases was only 4.7%, while the uptake of DM testing among MTB positive cases was 21.8%. Conclusion While a high yield for TB was identified among pre-diabetics and diabetics along with a high yield of DM among individuals diagnosed with TB, there was a low uptake of TB testing amongst presumptive TB patients who were recorded as pre-diabetic or diabetic. Bi-directional screening for TB and DM which includes the integration of TB diagnostics, DM screening and TB-DM treatment within existing health care programs will need to address the operational challenges identified before implementing this as a strategy in public health programs.

The manufacturing systems’ success directly relates to their accurate, reliable and flexible schedules, including how production is planned and scheduled and which constraints are considered in generating the schedules. The study's objective arises from the need to generate an optimal production scheduling system in a connecting plates manufacturing company that works on a Make-To-Stock basis. This research investigates the impact of demand and operational constraints on production schedules, including the facility capacity, operators and machines availability, raw materials availability, inventory level and warehouse capacity. A multi-agent-based optimisation model is developed to face the complexity of considering demand and operational constraints and reflects their impact on generating a reliable production schedule. This model involves a proposed heuristic algorithm that considers demand and operations constraints in such a manufacturing environment and optimises the production schedule based on these restrictions/requirements. A real-life case study based on a connecting plates manufacturer company is used as a test bench of the proposed agent-based heuristic optimisation model. The proposed algorithm is compared with other related approaches to check its superiority based on key criteria, including inventory levels, missed/unsatisfied orders and total production time. Results show that the proposed heuristics algorithm reduced the number of missed orders by 34% compared with similar approaches.

The increasing integration of variable renewable energy sources (VRESs) into modern power systems presents significant challenges in ensuring operational flexibility, highlighting the need for robust methodologies to evaluate and ensure system reliability. The Insufficient Ramping Resource Expectation (IRRE) has emerged as a critical metric for quantifying the probability of ramping deficiencies in power systems. However, its traditional application, designed primarily for capacity-constrained systems, may not fully capture the operational dynamics of energy-constrained systems, such as those dominated by hydropower generation. This study analyzes the IRRE methodology and proposes enhancements to incorporate additional constraints, including seasonal and monthly hydrological variability and operational reserve requirements, to better reflect the flexibility limitations in energy-constrained systems. A case study of the Brazilian electricity system evaluates these modifications by comparing traditional and enhanced IRRE results across varying scenarios, including higher VRES penetration. Results reveal that, under stricter constraints, IRRE values increased by over 11 times for monthly hydrological limits in the Northeast subsystem, compared to the traditional IRRE. Additionally, combining these constraints with a 5% operational reserve requirement led to ramping deficits in up to 5% of the hours in a year for the same subsystem, highlighting the critical impact of operational constraints. Furthermore, scenarios with 30% and 100% VRES growth resulted in deficits increasing by 56 times and 418 occurrences, respectively, in certain subsystems. These findings demonstrate the enhanced IRRE’s effectiveness in evaluating flexibility challenges and its relevance for supporting planning and operational strategies in systems undergoing rapid renewable energy expansion.

This work presents an implementation of a Nested Decomposition Algorithm (NDA) applied to co-optimizing generation and transmission capacity expansion planning problems in power systems, including operational flexibility constraints. The proposed methodology has been gaining relevance in recent years, as it can efficiently solve large mixed-integer problems faster than the conventional extensive formulation (mixed-integer linear programming). Three case studies are conducted on two IEEE test power systems to evaluate the algorithm’s performance and cut configuration. The first case study compares the performance between the NDA and the extensive formulation. The second case study compares the performance of each cut type, analyzing differences in simulation times and algorithm convergence. The third case study proposes a set of cut patterns based on the prior outcomes, whose performance and convergence are tested. Based on the simulation results, conclusions are drawn about the capability and performance of the NDA applied to the capacity expansion planning problem. The study shows that obtaining results with reasonable convergence in less simulation time is possible using a particular pattern.

This paper develops simplifying entropic models of irreversible closed cycles. The entropic models involve the irreversible connections between external and internal main operational parameters with finite physical dimensions. The external parameters are the mean temperatures of external heat reservoirs, the heat transfers thermal conductance, and the heat transfer mean log temperatures differences. The internal involved parameters are the reference entropy of the cycle and the internal irreversibility number. The cycle’s design might use four possible operational constraints in order to find out the reference entropy. The internal irreversibility number allows the evaluation of the reversible heat output function of the reversible heat input. Thus the cycle entropy balance equation to design the trigeneration cycles only through external operational parameters might be involved. In designing trigeneration systems, they must know the requirements of all consumers of the useful energies delivered by the trigeneration system. The conclusions emphasize the complexity in designing and/or optimizing the irreversible trigeneration systems.