Cost-effective Control Strategies Research Articles

Modelling and experimental validation of an adaptive interconnected suspension with adjustable roll stiffness (AIS-ARS)

Vehicle suspensions play a critical role in improving vehicle stability and ride quality, especially in heavy vehicles that usually have high centre-of-gravity and carry tons of cargo. Interconnected suspension systems, in which the suspension struts are connected through hydraulic hoses and flow control devices, have great potential to enhance vehicle handling stability and attenuate vibrations. This study introduces an adaptive interconnected suspension with adjustable roll stiffness (AIS-ARS) to address the shortcomings of existing designs. It not only eliminates the conventional anti-roll bars but also enables adjustment of roll stiffness depending on the driving and vehicle load conditions. The AIS-ARS system uses a unique design that avoids using expensive electro-proportional flow control valves, which has been a significant barrier to widespread implementation. Instead, the system utilises a cost-effective control strategy that uses only two solenoid valves to achieve adaptive damping. The mathematical modelling of the AIS-ARS is also straightforward, requiring less computational power and making it more practical for real-time implementations. Overall, the AIS-ARS system represents a significant advancement in the design of interconnected suspension systems, offering a more cost-effective, practical, and versatile solution. The above features are validated through laboratory experiments and co-simulation between MATLAB/Simulink and ADAMS/Car.

Small dams drive Anopheles abundance during the dry season in a high malaria burden area of Malawi.

This study explores the influence of small dams on the exposure to malaria vectors during the dry season in Kasungu district, Malawi, an area recently identified as high priority for malaria interventions by the National Malaria Control Programme. Small dam impoundments provide communities with a continuous supply of water for domestic and agricultural activities across sub-Saharan Africa and are considered vital to food security and climate change resilience. However, these permanent water bodies also create ideal breeding sites for mosquitoes in typically arid landscapes. The study focuses on a specific dam impoundment and its vicinity, aiming to assess its spatial and temporal influence on indoor vector densities. From May to August 2021, CDC light traps were used to measure indoor mosquito densities for two consecutive nights per month in three communities located at increasing distances from the dam (0, ~1 and ~2 km). Simultaneously, drone imagery was captured for each community, enabling the identification of additional standing water within approximately 400 m of selected households. Larval sampling was carried out within the impoundment periphery and in additional water bodies identified in the drone imagery. Generalised linear mixed models (GLMMs) were employed to analyse the indoor Anopheles abundance data, estimating the effects of household structure (open/closed eaves), month, temperature and water proximity on malaria vector exposure. Throughout 685 trapping nights, a total of 1256 mosquitoes were captured, with 33% (412) being female Anopheles. Among these, 91% were morphologically identified as Anopheles funestus s.l., and 5% as Anopheles gambiae s.l. Catches progressively decline in each consecutive trapping month as the environment became drier. This decline was much slower in Malangano, the community next to the dam, with abundance being notably higher in June and July. Further, the majority of An. gambiae s.l. were caught in May, with none identified in July and August. Anopheles larvae were found both in the impoundment and other smaller water bodies such as irrigation wells in each survey month; however, the presence of these smaller water bodies did not have a significant impact on adult female mosquito catches in the GLMM. The study concludes that proximity to the dam impoundment was the primary driver of differences between survey communities with the abundance in Chikhombwe (~1 km away) and Chiponde (~2 km away) being 0.35 (95% confidence interval [CI], 0.19-0.66) and 0.28 (95% CI, 0.16-0.47) lower than Malangano, respectively, after adjusting for other factors. These findings underscore the importance of targeted interventions, such as larval source management or housing improvements, near small dams to mitigate malaria transmission risks during the dry season. Further research is needed to develop cost-effective strategies for vector control within and around these impoundments.

Cost-Effective Control of Hybrid Ground Source Heat Pump (GSHP) System Coupled with District Heating

Hybrid ground source heat pump systems (GSHP) offer energy flexibility in operation. For hybrid GSHP systems coupled with district heating, limited studies investigated control strategies for reducing system energy costs from the perspective of building owners. This study proposed a cost-effective control strategy for a hybrid GSHP system integrated with district heating, investigating how power limits of district heating/GSHP, COP value for control (COPctrl), and control time horizon impact the system annual energy cost, CO2 emissions, and long-term borehole heat exchanger system performance. The simulations were performed using the dynamic building simulation tool IDA ICE 4.8. The results indicate that to realize both the energy cost savings and the long-term operation safety, it is essential to limit the heating power of district heating/GSHP and select an appropriate COPctrl. The control time horizon insignificantly affected the annual energy cost and long-term borehole heat exchanger system performance. The recommended COPctrl was 3.6, which is near the GSHP seasonal performance factor. Eventually, the cost-effective control reduced the system’s annual energy cost by 2.2% compared to the GSHP-prioritized control. However, the proposed control increased the CO2 emissions of the hybrid GSHP system due to the higher CO2 emissions from district heating.

Simulation-based economic evaluation of the Wolbachia method in Brazil: a cost-effective strategy for dengue control

Dengue virus (DENV) is an arbovirus transmitted by Aedes aegypti mosquitoes, which can cause severe conditions such as hemorrhagic fever and dengue shock syndrome. These conditions are associated with adverse social, clinical, and economic consequences in Brazil. Herein, the Wolbachia mosquito replacement method is a promising dengue control strategy. We estimated the economic impact of implementing the Wolbachia mosquito replacement method in seven Brazilian cities. A mathematical microsimulation model tracked nearly 23 million inhabitants over a 20-year period, considering the transitions between five different health states (susceptible, inapparent, outpatient, hospitalised and death). Direct costs included local dengue control programs, Wolbachia implementation and dengue care. Indirect costs related to death and productivity loss, as well as disability-adjusted life-years (DALY) averted were also considered. Without Wolbachia, the model projected 1,762,688 reported dengue cases over 20 years. Implementing the Wolbachia method would avert at least 1,295,566 dengue cases, resulting in lower costs and greater effectiveness in all simulated cities. On average, for every 1000 inhabitants followed for 20 years, the Wolbachia method yielded a cost difference of USD 538,233.68 (BRL 2,691,168.40) and averted 5.56 DALYs. Net monetary benefits (NMB) were positive in all seven cities, ranging from USD 110.72 (BRL 553.59) to USD 1399.19 (BRL 6995.95) per inhabitant. Alternative scenarios have also shown a favourable return on investment with a positive benefit-cost ratio (BCR). Wolbachia is likely a cost-effective strategy in the Brazilian context, consistent with international studies. Sensitivity analysis and alternative scenarios confirmed the robustness of the results. This study was funded by the Wellcome Trust under a grant (224459/Z/21/Z).

Improving tuberculosis control: assessing the value of medical masks and case detection-a multi-country study with cost-effectiveness analysis.

Tuberculosis (TB) remains a significant global health concern, necessitating effective control strategies. This article presents a mathematical model to evaluate the comparative effectiveness of medical mask usage and case detection in TB control. The model is constructed as a system of ordinary differential equations and incorporates crucial aspects of TB dynamics, including slow-fast progression, medical mask use, case detection, treatment interventions and differentiation between symptomatic and asymptomatic cases. A key objective of TB control is to ensure that the reproduction number, , remains below unity to achieve TB elimination or persistence if exceeds 1. Our mathematical analysis reveals the presence of a transcritical bifurcation when the signifies a critical juncture in TB control strategies. These results confirm that the effectiveness of case detection in diminishing the endemic population of symptomatic individuals within a TB-endemic equilibrium depends on exceeding a critical threshold value. Furthermore, our model is calibrated using TB yearly case incidence data per 100 000 population from Indonesia, India, Lesotho and Angola. We employed the bootstrap resampling residual approach to assess the uncertainty inherent in our parameter estimates which provides a comprehensive distribution of the parameter values. Despite a declining trend in new incidence, these four countries exhibit a reproduction number greater than 1, indicating persistent TB cases in the presence of ongoing TB control programmes. We employ the partial rank correlation coefficient in conjunction with the Latin hypercube sampling method to conduct a global sensitivity analysis of the parameter for each fitted parameter in every country. We find that the medical mask use is more sensitive to reduce compared with the case detection implementation. To further gain insight into the necessary control strategy, we formulated an optimal control and studied the cost-effectiveness analysis of our model to investigate the impact of case detection and medical mask use as control measures in TB spread. Cost-effectiveness analysis demonstrates that combining these interventions emerges as the most cost-effective strategy for TB control. Our findings highlight the critical importance of medical masks and their efficacy coupled with case detection in shaping TB control dynamics, elucidating the primary parameter of concern for managing the control reproduction number. We envisage our findings to have implications and be vital for TB control if implemented by policymakers and healthcare practitioners involved in TB control efforts.

Modeling the Regional Distribution of International Travelers in Spain to Estimate Imported Cases of Dengue and Malaria: Statistical Inference and Validation Study.

Understanding the patterns of disease importation through international travel is paramount for effective public health interventions and global disease surveillance. While global airline network data have been used to assist in outbreak prevention and effective preparedness, accurately estimating how these imported cases disseminate locally in receiving countries remains a challenge. This study aimed to describe and understand the regional distribution of imported cases of dengue and malaria upon arrival in Spain via air travel. We have proposed a method to describe the regional distribution of imported cases of dengue and malaria based on the computation of the "travelers' index" from readily available socioeconomic data. We combined indicators representing the main drivers for international travel, including tourism, economy, and visits to friends and relatives, to measure the relative appeal of each region in the importing country for travelers. We validated the resulting estimates by comparing them with the reported cases of malaria and dengue in Spain from 2015 to 2019. We also assessed which motivation provided more accurate estimates for imported cases of both diseases. The estimates provided by the best fitted model showed high correlation with notified cases of malaria (0.94) and dengue (0.87), with economic motivation being the most relevant for imported cases of malaria and visits to friends and relatives being the most relevant for imported cases of dengue. Factual descriptions of the local movement of international travelers may substantially enhance the design of cost-effective prevention policies and control strategies, and essentially contribute to decision-support systems. Our approach contributes in this direction by providing a reliable estimate of the number of imported cases of nonendemic diseases, which could be generalized to other applications. Realistic risk assessments will be obtained by combining this regional predictor with the observed local distribution of vectors.

Advanced intelligent optimization for electric vehicle charging stations integrated with photovoltaic system and battery storage in commercial buildings

This paper presents a sophisticated four-stage optimization and intelligent control algorithm tailored for two-way electric vehicle charging (EVC) stations integrated with advanced photovoltaic systems and fixed battery energy storage in commercial buildings. The primary objective is to minimize operating costs while prioritizing customer satisfaction within a dynamic and uncertain energy landscape. Our algorithm optimizes the scheduled charging and discharging of electric vehicles (EVs), local battery storage (BS) units, grid power supply, and deferred loads to balance instantaneous supply and demand. The first stage focuses on developing optimal energy management plans for the day ahead, considering factors such as projected energy production, anticipated EVC demand, and building energy consumption patterns. Building on this foundation, the second stage introduces multilayer EV charging price structures and optimizes participation rewards for discharging, dynamically addressing EV charging patterns and price sensitivities. Approaching the commissioning timeline, the third stage refines energy management plans for the upcoming hours using real-time data and forecasts, adapting to evolving conditions for optimal resource allocation. The final stage involves real-time control and the implementation of optimized programs, dynamically adjusting charge/discharge processes, grid interactions, and load deferral to maintain supply-demand balance and minimize operating costs. Our algorithm enhances system resilience in unpredictable conditions, providing compelling incentives for active EV user participation. Coordinating the integrated system efficiently, including the commercial building’s energy load, ensures reliable service to customers while reducing costs. Extensive case studies and a comparative analysis validate the algorithm’s efficiency in significantly reducing operating costs and enhancing resilience to uncertainty. The paper concludes by highlighting the algorithm’s pioneering role in intelligent EV charging station (CHS) management, offering a cost-effective, customer-oriented, and dynamic energy control strategy for advancing global energy practices.

Adaptive Intelligent Minimum Parameter Singularity Free Sliding Mode Controller Design for Quadrotor

This paper presents a singularity free fast terminal sliding mode control (SFFTSMC) for position and attitude tracking of a quadrotor with unknown dynamics. The contribution of this work is threefold, namely: devising an SFFTSMC strategy using an auxiliary function to eliminate the singularity in the control law, a cost-saving minimum parameter update scheme using a fully connected recurrent neural network (FCRNN) to handle the unknown dynamics of quadrotor and development of an adaptive control law to compensate for the loss of effectiveness of the actuator and saturation effects, considered explicitly, without the requirement of any fault detection and diagnosis (FDD) unit. The proposed approach offers a direct singularity free control action in the presence of unknown dynamics, actuator faults, and saturation. Overall closed-loop stability is guaranteed in finite-time using Lyapunov stability theory and update laws for minimum parameters of FCRNN and fault compensator are derived using the same. Extensive simulations are presented for the trajectory tracking tasks using the proposed method and compared with the existing piece-wise fast terminal sliding mode controller (PFTSMC) and Radial basis function network (RBFN) based fault compensation approach. The proposed method is also validated in the Gazebo simulator via Pixhawk autopilot to demonstrate the feasibility in real-time. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This research is motivated by a cost-effective control scheme for quadrotors in cases of unknown/uncertain dynamics, external disturbances, actuator faults, and saturation. Precise quadrotor dynamics are difficult to obtain in real time. Also, there may be parametric uncertainties in quadrotor parameters like mass, inertia, and aerodynamic coefficients. External disturbances like wind gusts are always present in outdoor environments, and it may be the case that quadrotor motors lose their effectiveness due to defective motors or propeller damage while flying. To address these issues, first, it requires a precise control scheme to control the quadrotor while guaranteeing stability to ensure the safety of the quadrotor itself and people in the surrounding environment. Second, the approach should not be computationally heavy, which may sluggish the overall response of the quadrotor. Thus, we propose a control scheme to address these two issues simultaneously using a new fast terminal sliding mode control scheme augmented with FCRNN by updating minimum parameters instead of all the weight parameters of FCRNN. A compensator is introduced to mitigate the effect of unexpected actuator fault and saturation. The proposed approach can be helpful in various quadrotor applications, like pesticide spraying in agriculture, payload transportation, search and rescue, construction, etc., where unknown quadrotor dynamics and unknown payload situations occur.

A nonlinear mathematical model for exploring the optimal cost-effective therapeutic strategies and within-host viral infections spread dynamics

This study presents a nonlinear mathematical model to capture the constant rates of three different target cells class-specific drug therapeutic measures (namely, drug therapy for blocking new infections, drug therapy for actively infected cells, and drug therapy inhibiting viral production) for the dynamics of within-host viral infections with multiple classes of target cells. The threshold quantity of the control reproduction number of the model is calculated. The global asymptotic behaviours of the model around the steady states are investigated in terms of the control reproduction number. Moreover, the model is extended to an optimal control problem by considering the three constant parameters for drug therapeutic measures as time-dependent control variables. Qualitative analysis of the proposed model is conducted using optimal control theory. Numerical solutions of the derived optimality system are sought to illustrate the efficacies of different combination strategies consisting of using at least any of the three target cells’ class-specific optimal controls in reducing the burden of within-host virus transmission and spread at a minimum cost. Cost-effectiveness analysis is further carried out to determine the least costly and most effective intervention strategy. The cost analysis reveals that the use of only target cells class-specific drug therapy control for blocking new infections is the most cost-effective control strategy.

Demographic and socio-economic factors affecting bed net ownership, usage, and malaria transmission among adult patients seeking healthcare in two Ghanaian urban cities

BackgroundThe most cost-effective malaria prevention and control strategy is the use of a bed net. However, several factors affect the ownership and usage of bed nets among the adult population. Hence, this study aimed to examine socio-demographic factors affecting bed net ownership, usage and malaria transmission among adult patients seeking healthcare in two Ghanaian urban cities.MethodsThis hospital-based cross-sectional study was conducted, between January and September 2021, at Bremang Seventh-Day Adventist Hospital, Suame Municipal, Ashanti Region and Sunyani Municipal Hospital, Sunyani, Bono Region, Ghana. Structured questionnaires were administered to a total of 550 participants to ascertain their ownership and usage of the bed nets. Afterwards, finger prick blood samples were collected for malaria microscopy. Crude and adjusted prevalence ratios (PR) and their respective 95% CIs were calculated, using Poisson regression with robust standard errors, to show associated variables in bivariate and multivariate analyses respectively. R software (version 4.1.1) was used to perform all statistical analyses.ResultsAbout 53.3% (n = 293) of participants owned at least one-bed net but only 21.5% (n = 118) slept under it the previous night. Those married were 2.0 (95% CI: 1.6 – 2.5) and 2.4 (95% CI: 1.6 – 3.5) times more likely to own and use a bed net respectively than those who never married. Also, pregnant women were 1.3 (95% CI: 1.1 – 1.6) and 1.8 (95% CI: 1.3 – 2.5) times more likely to own and use a bed net respectively than non-pregnant. Even though income levels were not associated with bed net ownership and usage, students were 0.4 (95% CI: 0.2 – 0.6) and 0.2 (95% CI: 0.1 – 0.5) times less likely to own and use bed net respectively compared to formally employed persons. The overall malaria prevalence rate was 7.8%. Malaria-negative patients were 1.6 (95% CI: 1.2 – 2.0) and 2.4 (95% CI: 1.4 – 4.1) times more likely to own and use bed nets respectively than malaria positive. Patients with tertiary education recorded the lowest malaria prevalence (3.5%, n = 4). None of those with a monthly income > $300 recorded a case of malaria. On the contrary, majority 83%, n/N = 25/30) of the malaria-positive patients earned ≤ $150.ConclusionThe National Malaria Control Program should conduct comprehensive mapping of all urban population segments before launching mass bed net distribution campaigns, taking into account demographic and socioeconomic factors to enhance bed net utilization and reduce malaria prevalence.

Dynamics and optimal control of tuberculosis model with the combined effects of vaccination, treatment and contaminated environments.

Tuberculosis has affected human beings for thousands of years, and until today, tuberculosis still ranks third among 29 infectious diseases in China. However, most of the existing mathematical models consider a single factor, which is not conducive to the study of tuberculosis transmission dynamics. Therefore, this study considers the combined effects of vaccination, treatment, and contaminated environments on tuberculosis, and builds a new model with seven compartments of $ SVEITRW $ based on China's tuberculosis data. The study shows that when the basic reproduction number $ R_{0} $ is less than 1, the disease will eventually disappear, but when $ R_{0} $ is greater than 1, the disease may persist. In the numerical analysis part, we use Markov-chain Monte-Carlo method to obtain the optimal parameters of the model. Through the next generation matrix theory, we calculate that the $ R_{0} $ value of tuberculosis in China is $ 2.1102 $, that is, if not controlled, tuberculosis in China will not disappear over time. At the same time, through partial rank correlation coefficients, we find the most sensitive parameter to the basic reproduction number $ R_{0} $. On this basis, we combine the actual prevalence of tuberculosis in China, apply Pontryagin's maximum principle, and perform cost-effectiveness analysis to obtain the conditions required for optimal control. The analysis shows that four control strategies could effectively reduce the prevalence of TB, and simultaneously controlling $ u_{2}, u_{3}, u_{4} $ is the most cost-effective control strategy.

Impact of operation parameters and lambda input signal during lambda-dithering of three-way catalysts for low-temperature performance enhancement

A synthetic exhaust gas bench was dynamically operated to investigate the impact of temperature, amplitude, split cycle, mean lambda, gas hourly space velocity, and oxygen storage capacity on average pollutant conversion and product selectivity of three-way catalysts in periodic operation. As temperature and amplitude increase and oxygen storage capacity decreases, the optimal frequency for maximum pollutant conversion increases. This is consistent with faster desorption of CO and O2 from the catalyst, yielding free surface sites. Regarding the formation of secondary products, the optimal frequency for maximum pollutant conversion does not always correspond to minimal N2O and NH3 emissions. The split cycle variation reveals the enhancement of C3H8 and NO conversion after both lean-rich and rich-lean switches and C3H6 and CO conversion after rich-lean switches at the optimal frequency. As periodic operation does not affect existing engine settings or operating conditions, it is a cost-effective control strategy for meeting future emission limits.

Field investigation combined with modeling uncovers the ecological heterogeneity of Aedes albopictus habitats for strategically improving systematic management during urbanization

BackgroundAedes albopictus is an invasive vector of serious Aedes-borne diseases of global concern. Habitat management remains a critical factor for establishing a cost-effective systematic strategy for sustainable vector control. However, the community-based characteristics of Ae. albopictus habitats in complex urbanization ecosystems are still not well understood.MethodsA large-scale investigation of aquatic habitats, involving 12 sites selected as representative of four land use categories at three urbanization levels, was performed in Guangzhou, China during 2015–2017. The characteristics and dynamics of these Ae. albopictus habitats were assessed using habitat-type composition, habitat preference, diversity indexes and the Route index (RI), and the temporal patterns of these indexes were evaluated by locally weighted scatterplot smoothing models. The associations of RI with urbanization levels, land use categories and climatic variables were inferred using generalized additive mixed models.ResultsA total of 1994 potential habitats and 474 Ae. albopictus-positive habitats were inspected. The majority of these habitats were container-type habitats, with Ae. albopictus showing a particularly higher habitat preference for plastic containers, metal containers and ceramic vessels. Unexpectedly, some non-container-type habitats, especially ornamental ponds and surface water, were found to have fairly high Ae. albopictus positivity rates. Regarding habitats, the land use category residential and rural in Jiangpu (Conghua District, Guangzhou) had the highest number of Ae. albopictus habitats with the highest positive rates. The type diversity of total habitats (H-total) showed a quick increase from February to April and peaked in April, while the H-total of positive habitats (H-positive) and RIs peaked in May. RIs mainly increased with the monthly average daily mean temperature and monthly cumulative rainfall. We also observed the accumulation of diapause eggs in the winter and diapause termination in the following March.ConclusionsEcological heterogeneity of habitat preferences of Ae. albopictus was demonstrated in four land use categories at three urbanization levels. The results reveal diversified habitat-type compositions and significant seasonal variations, indicating an ongoing adaptation of Ae. albopictus to the urbanization ecosystem. H-positivity and RIs were inferred as affected by climatic variables and diapause behavior of Ae. albopictus, suggesting that an effective control of overwintering diapause eggs is crucial. Our findings lay a foundation for establishing a stratified systematic management strategy of Ae. albopictus habitats in cities that is expected to complement and improve community-based interventions and sustainable vector management.Graphical

Optimal control and cost effectiveness analysis of contamination associated with aflatoxins in maize kernels, livestock and humans

Aflatoxin contamination poses a significant challenge in food safety and security as it affects both health of consumers and supply chains. Due to health impacts associated with aflatoxin contamination, countries have set standards and restrictions for importing food crops and animal feed, resulting in greater economic losses to farmers, transporters, and crop processors. Three controls, namely good farming practices, biological control and public education and awareness campaigns, have been mostly used in countries where aflatoxin contamination has occurred. Since resources are scarce, there is a need to find the optimal and cost-effective strategy to reduce the burden on farmers. This study aimed to find optimal and cost-effective control strategy to mitigated aflatoxin contamination in maize kernels, livestock and humans. A deterministic model was developed and analyzed for studying the impact of implementing three time dependent controls on the dynamics and control of aflatoxin contamination in maize kernels, livestock and humans. We use optimal control theory to find the necessary conditions for existence of the optimal controls and to determine the optimal strategy for controlling the aflatoxin contamination. We also carry out cost-effectiveness analysis through Incremental Cost Effective Ratio (ICER) to obtain the most effective strategy. Simulation results for the optimal control problem suggest that strategy which involve implementation of all controls performs well than other strategies in controlling the aflatoxin contamination in maize kernels, livestock and humans. Therefore, to control aflatoxin contamination initiatives should focus on good farming practices, biological control and public education and awareness campaigns.

A Novel Model-Free Deep Reinforcement Learning Framework for Energy Management of a PV Integrated Energy Hub

This paper utilizes a fully model-free and data-driven deep reinforcement learning (DRL) framework to develop an intelligent controller that can exploit information to optimally schedule the energy hub with the aim of minimizing energy costs and emissions. By posing the energy hub scheduling problem as a multi-dimensional continuous state and action space, the proposed deep deterministic policy gradient (DDPG) method enables more cost-effective control strategies. The method can lead to a more efficient operation by considering nonlinear physical characteristics of the energy hub components like nonconvex feasible operating regions of combined heat and power (CHP) units, valve-point effects of power-only units, and fuel cell dynamic efficiency. Moreover, to provide great potential for the DDPG agent to learn an optimal policy in an efficient way, a hybrid forecasting model based on convolutional neural networks (CNNs) and bidirectional long short-term memories (BLSTMs) is developed to overcome the risk associated with PV power generation that can be highly intermittent, particularly on cloudy days. The effectiveness and applicability of the proposed scheduling framework in reducing energy costs and emissions while coping with uncertainties are demonstrated by comparing it against conventional robust optimization and stochastic programming approaches as well as state-of-the-art DRL methods in different case studies.

Evaluation of breast cancer awareness in female patients diagnosed with schizophrenia.

In this study, it was aimed to investigate the awareness of female patients diagnosed with schizophrenia about breast cancer and to evaluate whether there was a difference in this awareness between the control group and individuals diagnosed with schizophrenia. Secondly, the frequency of breast cancer screenings of patients diagnosed with schizophrenia and the control group was compared. Individuals between 18 and 65 years of age who were literate and voluntarily gave informed consent to participate after being informed about the study were included. The research study group comprised of 82 individuals, 35 patients with schizophrenia and 47 healthy individuals. Patients with schizophrenia were required to have no clinically severe disease picture (CGI-S score of 3 or below). Individuals were given the Breast Cancer Awareness Scale (B-CAS) to fill in. The patient group had less awareness of breast cancer than the control group; conversely, they faced more barriers in breast cancer screening. The number of those who stated that they did not know about breast cancer early diagnosis methods was higher in the patient group than in the control group. In the evaluation of health attitudes toward breast cancer, it was found that the healthy control group was better than the patient group in performing regular breast self-exam. Educating individuals with schizophrenia about the signs and symptoms of cancer and adapting healthcare systems to facilitate rapid and early cancer diagnosis may result in cost-effective and applicable cancer control strategies for curable cancers.

Rule-Based Control Studies of LNG–Battery Hybrid Tugboat

The use of hybrid energy systems in ships has increased in recent years due to environmental concerns and rising fuel prices. This paper focuses on the development and study of a hybrid energy system using liquefied natural gas (LNG) and batteries for a tugboat. The hybrid system model is created in MATLAB/Simulink® and uses fuel data obtained from an operational diesel-powered tugboat. The LNG–hybrid system is then subjected to testing in four distinct configurations: fixed speed, variable speed, and with and without a battery. The different configurations are compared by computing the daily fuel cost, CO2 emissions, energy efficiency operation indicator (EEOI) and carbon intensity indicator (CII) ratings in three distinct operation cases. The analysis reveals that the use of an LNG–battery hybrid tugboat results in an average reduction of 67.2% in CO2 emissions and an average decrease of 64.0% in daily fuel cost compared to a diesel system. An energy management system using rule-based (RB) control is incorporated to compare the daily cost and CO2 emissions for one of the case studies. The rule-based control that requires the battery to be used and the LNG engine to be switched off at the lowest allowable minimum power based on the specific gas consumption produces the most cost-effective control strategy out of all the different control strategies tested. The result demonstrates that an additional reduction of CO2 and daily fuel cost for LNG–battery hybrid tugboats by 23.8% and 22.3%, respectively, could be achieved with the implementation of the cost-effective strategy as compared to not having a control strategy.

Optimal R&D investment in the management of invasive species

Invasive alien species (IAS) threaten world biodiversity, ecosystem services, and economic welfare. While existing literature has characterized the optimal control of an established IAS, it has not considered how research and development (R&D) into new removal methods or technologies can affect management decisions and costs over time. R&D can lower the costs of control in a management plan and creates an intertemporal trade-off between quick but costly control and gradual but cheaper removal over time. In this paper, we develop and solve a continuous time dynamic optimization model to study how investment in R&D influences the optimal control of an established invasive species. After characterizing the dynamic model solution, we solve the model numerically to study the benefits from R&D in the management of the brown tree snake (Boiga irregularis), and explore how optimal solutions vary across economic and biological conditions. We find that the introduction of R&D significantly reduces overall costs of IAS and management and that the cost reductions substantially outweigh research expenditure. These results imply that policymakers seeking to control IAS should consider R&D as a vital component of cost effective control strategies.

Discard the dross and select the essence: Purified orange peel with coupled alkali-acid hydrolysis as potential Cd(II)-capturing adsorbent

Turning agricultural wastes into green adsorbents is a compatible and cost-effective strategy for solid waste disposal and environmental pollution control. Herein, the discarded orange peel (OP) sample was purified via coupled alkali-acid hydrolysis and the obtained adsorbents were applied for eliminating toxic cadmium (Cd(II)) from the liquid phase. Based on the single factor experiments, the optimal Cd(II)-capturing adsorbent (i.e., OP-OH-H-25) was generated under the purification condition of soaking raw OP in 2.0 M NaOH solution at 25 °C followed by an acid hydrolysis treatment with 2.5 M HCl solution at 25 °C. Spectral analyses reflected the efficient elution of hemicellulose/pectin and lignin impurities, the transformation of cellulose type I to cellulose type II, and the exposure of sulfhydryl functional groups. The kinetics removal process of Cd(II) by OP-OH-H-25 reached equilibrium after 0.5 h with a removal efficiency of ∼88%. The maximum Cd(II) sorption capacity of OP-OH-H-25 (i.e., 124.90 mg/g) was higher than a series of previously reported adsorbents. The sorption behavior of Cd(II) onto the prepared OP-OH-H-25 material was mostly interfered with by the coexisting Mg2+, Ca2+, and heavy metals. The desorption data and spectroscopic analysis jointly pointed to the inner-sphere binding of Cd(II) with OP-OH-H-25 functional groups. Overall, the extracted OP-OH-H-25 sample could act as a promising decontaminant for the remediation of Cd(II)-polluted water environment.

Session IX:

Turning agricultural wastes into green adsorbents is a compatible and cost-effective strategy for solid waste disposal and environmental pollution control. Herein, the discarded orange peel (OP) sample was purified via coupled alkali-acid hydrolysis and the obtained adsorbents were applied for eliminating toxic cadmium (Cd(II)) from the liquid phase. Based on the single factor experiments, the optimal Cd(II)-capturing adsorbent (i.e., OP-OH-H-25) was generated under the purification condition of soaking raw OP in 2.0 M NaOH solution at 25 °C followed by an acid hydrolysis treatment with 2.5 M HCl solution at 25 °C. Spectral analyses reflected the efficient elution of hemicellulose/pectin and lignin impurities, the transformation of cellulose type I to cellulose type II, and the exposure of sulfhydryl functional groups. The kinetics removal process of Cd(II) by OP-OH-H-25 reached equilibrium after 0.5 h with a removal efficiency of ∼88%. The maximum Cd(II) sorption capacity of OP-OH-H-25 (i.e., 124.90 mg/g) was higher than a series of previously reported adsorbents. The sorption behavior of Cd(II) onto the prepared OP-OH-H-25 material was mostly interfered with by the coexisting Mg2+, Ca2+, and heavy metals. The desorption data and spectroscopic analysis jointly pointed to the inner-sphere binding of Cd(II) with OP-OH-H-25 functional groups. Overall, the extracted OP-OH-H-25 sample could act as a promising decontaminant for the remediation of Cd(II)-polluted water environment.