Assist Decision Makers Research Articles

The Implementation of the Supported Decision-Making System in Chinese Law

Abstract: This study explores the introduction and realization path of assisted decision system in Chinese law. As the UN Convention on the Rights of Persons with Disabilities and other international conventions pay more and more attention to the decision on assistance, China has gradually introduced the system and is facing many challenges, including legal framework and social resources. This paper compares assisted decision with traditional alternative decision, and analyzes its advantages and limitations. Then, it focuses on the practice path of the assisted decision system in China, including how to deal with legal defects, cultural barriers and UN recommendations to China. Finally, this paper looks forward to the possible development direction of the system in the future, and emphasizes the importance of improving legal protection and social support.

Extreme Rainfall Analysis in Pernambuco, Northeast Brazil, Using a High‐Resolution Gridded Dataset

ABSTRACTThis paper presents a detailed spatio‐temporal analysis of the rainfall in the state of Pernambuco, Northeast Brazil. It is based on climate indices for extreme precipitation recommended by the Expert Team on Climate Change Detection, Monitoring and Indices. To accomplish this, daily rainfall 1data (1961–2019) were extracted from 809 high‐resolution grid points (0.1° × 0.1°) using the Brazilian Daily Weather Gridded Data (BR‐DWGD). The significance and magnitude of index trends were assessed using the modified Mann–Kendall and Sen's slope tests. This study also examined whether there existed a significant difference in climate indices among the three regions (Sertão, Agreste and Zona da Mata) within the state. The findings revealed notable significant negative trends in the PRCPTOT, R10mm, R20mm, Rx1day, Rx5day and CWD indices across all regions of Pernambuco, exhibiting a gradient from the coast to the state's interior. Reduction values of up to 15 mm year−1 for PRCPTOT, 0.7 day year−1 for R10mm, 0.2 day year−1 for R20mm, 0.01 mm year−1 for Rx1day, 0.03 mm year−1 for Rx5day, 0.4 day year−1 for CWD were observed. Furthermore, an alarming pattern was also noted for CDD, displaying a higher concentration of significant positive trends in all regions of the state, with estimated increases of up to 1.4 day year−1. Conversely, a balance of trends—both positive and negative—was observed across the entire state for R95p and R99p, with a majority of trends proving non‐significant. SDII exhibited a higher frequency of grid points showing a significant positive trend, particularly notable in the Sertão and Zona da Mata regions, where significant differences in the index values were absent. However, the remaining indices showcased notable regional differences, with values decreasing from the east to the west of the state, except for CDD. This study will assist decision makers, providing detailed long‐term information essential for preventing natural disasters and supporting socioeconomic and environmental policies in the state.

Planning and Evaluation of Water-Dropping Strategy for Fixed-Wing Fire Extinguisher Based on Multi-Resolution Modeling

The deployment of fixed-wing aircraft in fire-extinguishing operations represents a significant advancement in the domain of aviation emergency rescue. Addressing the challenge of enhancing firefighting efficacy, this study delves into the water-dropping strategies of fixed-wing extinguishers and provides a methodological framework for the strategic planning and assessment of water-dropping tactics, employing multi-resolution modeling. The formulation of the planning algorithm and the structure of the effectiveness evaluation index system are explained accordingly. The corresponding prototype system was designed, comprising four subsystems that utilized distinct resolution models: fire environment simulation, water-dropping point scheme planning, approaching path planning, and mission evaluation simulation. Case studies validate the system’s capability to forecast fire and smoke propagation, plan a water-dropping trajectory based on the fire line, optimize flight paths based on the trajectory, and simulate as well as evaluate the whole firefighting mission process. The above research comprehensively constructs the model, finishes the iterative optimization, and evaluates the water-dropping strategy by simulation. The technical path and methodological framework of studying water-dropping strategies are established. The outcomes of this study provide invaluable support for the parameter inversion design of the fixed-wing extinguisher, offering decision-making assistance to commanders and supplying training scenarios for new aviation crews.

Dynamic Data-Driven Deterioration Model for Sugarcane Shredder Hammers Oriented to Lifetime Extension

Several sugar mills operate as waste-to-energy plants. The shredder is the initial high-energy machine in the production chain and prepares sugarcane. Its hammers, essential spare parts, require continuous replacement. Then, the search for intelligent strategies to extend the lifetime of these hammers is fundamental. This paper presents (a) a dynamic data-driven model for estimating the deterioration and predicting remaining life of the sugarcane shredder hammers during operation, for which the real data of the entering sugarcane flow and the power required to prepare the sugarcane are analyzed, and (b) a management architecture intended for online decision-making assistance to extend the hammers’ life by making a trade-off between the desired lifetime, along with a nominal shredder work satisfaction criterion. The deterioration model is validated with real data achieving an accuracy of 84.41%. The remaining life prognostic is within a confidence zone calculated from the historical sugarcane flow, with a probability close to 99%, fitting a lognormal probability distribution. A numerical example is also provided to illustrate a closed loop control, where the proposed architecture is used to extend the useful life of the hammers during operation, adjusting the incoming sugarcane flow while maintaining the nominal work satisfaction of the shredder.

Coaching as a tool for the development of leadership qualities women-entrepreneurs in Ukraine

In 2023-2024, women’s entrepreneurship turned into a driving force of the Ukrainian economy. At the same time, women who had never thought of themselves as a leader came into entrepreneurship. Such women often have low self-confidence, are afraid of responsibility for independently made decisions, and find it difficult to establish communication with the team, clients and partners. At the same time, Ukrainian womenmostly independently try to combine the roles of mother, wife, specialist, entrepreneur, daughter, etc. In such conditions, it is coaching that can turn a noself-confident woman who combines the maximum number of functions in various fields into a real leader. Coaching, as a tool for leadership development, is devoted to the work of DZ. Whitmore, A. Chernenko-Dyba, S. Kovi, O. Nezhynska, M. Landsberg and others. The purpose of the article is to study coaching as a tool for developing leadership qualities of women entrepreneurs of Ukraine. The article describes the key factors of the influence of coaching on the development of leadership qualities of domestic female entrepreneurs, namely: help in increasing self-confidence; development of emotional intelligence; assistance in decision-making; establishment of effective communication with partners, clients, state authorities; ensuring a balance between work and personal life; assistance in strategic planning and development of the contact base of a female entrepreneur. Also given are the main problems that can accompany domestic female entrepreneurs who seek to develop their leadership qualities with the help of a professional coach. At the same time, the author firmly believes that for a Ukrainian woman entrepreneur who really wants to develop her leadership qualities, the priority of her new self — a self-confident woman capable of building her business and leading people should come to the fore. It is under this condition that the given problems are quite easily solved. Based on this, practical and effective steps are proposed to overcome these problems.

Dam Siltation in the Mediterranean Region Under Climate Change: A Case Study of Ahmed El Hansali Dam, Morocco

Dams are vital for irrigation, power generation, and domestic water needs, but siltation poses a significant challenge, especially in areas prone to water erosion, potentially shortening a dam’s lifespan. The Ahmed El Hansali Dam in Morocco faces heightened siltation due to its upstream region being susceptible to erosion-prone rocks and high runoff. This study estimates the siltation at the dam from its construction up to 2014 using bathymetric data and the Brown model, which is a widely-used empirical model that calculates reservoir trap efficiency. Additionally, the study evaluates the impact of Land Use and Land Cover (LULC) changes and projected future rainfall until around 2076 based on siltation rates. The results indicate that changes in LULC, particularly temporal variations in precipitation, have a significant impact on the siltation of the Ahmed El Hansali dam. Notably, rainfall is strongly correlated with the siltation rate, with an R2 of 0.92. The efficiency of sediment trapping (TE) is 97.64%, meaning that 97.64% of the sediment in the catchment area is trapped or deposited at the bottom of the dam. The estimated annual specific sediment yield is about 32,345.79 tons/km2/yr, and the sediment accumulation rate is approximately 4.75 Mm3/yr. The dam’s half-life is estimated to be around 2076, but future precipitation projections may extend this timeframe due to the strong correlation between siltation and precipitation. Additionally, soil erosion driven by land management practices plays a crucial role in future siltation dynamics. Hence, this study offers a comprehensive assessment of the siltation dynamics at the Ahmed El Hansali dam, providing essential information on the long-term effects of erosion, land use changes, and climate projections. These findings may assist decision makers in managing dam reservoir sedimentation more effectively, ensuring the durability of the dam and extending the reservoir life.

Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM

Supply chain management is changing rapidly due to increasing complexity, uncertain demand, and the requirement for sustainable methods. Advanced technologies like Bidirectional Long Short-Term Memory networks (BiLSTM) and Convolutional Neural Networks (CNNs) can enhance supply chain processes. This paper proposes integrating CNNs and BiLSTM models to improve supply chain efficiency and sustainability. The proposed model employs CNNs to optimize resource allocation, uncover trends, and evaluate supply chain spatial linkages. Using BiLSTM models to capture temporal correlations allows accurate demand forecasting and proactive decision-making. Combining these models explains supply chain dynamics. CNNs and BiLSTM models' adaptive learning and real-time monitoring boost efficiency by responding quickly to changing situations. Predictive analytics optimizes inventory, lowers stock outs, and cuts lead times. Sustainability factors include transportation route optimization, carbon footprint minimization, and intelligent green-sourcing decision assistance. The proposed Hybrid Model achieved 94.65 % Specificity, 96.57 % Accuracy, 95.67 % Sensitivity and 0.85 % MCC. The result analysis demonstrates that the proposed model significantly improved the accuracy level. This research sheds light on supply chain difficulties from all sides. CNNs and BiLSTM models can boost operational efficiency and link supply chain practices with sustainability goals to produce a more sustainable global supply network.

Development of a prognostic model for predicting long-term visual acuity after cataract surgery in children with bilateral congenital cataracts: a single centre retrospective, observational study

BackgroundTo evaluate factors influencing best corrected visual acuity (BCVA) in paediatric patients with bilateral congenital cataracts (CC) after cataract extraction and intraocular lens (IOL) implantation, as well as develop a robust model for predicting long-term visual acuity.MethodsThis retrospective study followed 194 paediatric patients with bilateral CC from January 2008 to December 2021. The endpoint event was defined as a final BCVA < 0.22 Log MAR at the last follow-up, which indicated good outcome. The probability of reaching this endpoint event was modelled using Cox proportional hazards regression analysis and internally validated through 200 iteration of 5-fold cross-validation.ResultsA prognostic model for long-term visual acuity in bilateral CC after surgical treatment was established as follows: ln h(t) = −0.009 × “age at cataract extraction” − 0.015 × “age at IOL implantation” − 2.934 × “without nystagmus at last follow − up” + ln h0(0), in which h0(t) represents the baseline risk equation that can be any non-negative equation for time (t); h(t) represents the probability of the endpoint event occurring at time (t) without any endpoint event occurring before it. The model was visualized using a nomogram and contour plot to facilitate clinical practice. The model demonstrated reasonably accurate discrimination with an area under the receiver operating characteristic curve of 0.712 (95% confidence interval [CI]: 0.589–0.835) and a C-index of 0.797 (95% CI: 0.683–0.911). According to the model, children with bilateral CC had a higher likelihood of achieving a good outcome (BCVA < 0.22 Log MAR) if they underwent cataract extraction before the age of six months (hazard ratio [HR] 1.80, 95% CI: 0.92–3.70), received IOL implantation before the age of thirty-one months (HR 3.70, 95% CI: 1.77–7.80), and presented without nystagmus during their last follow-up visit (HR 11.20, 95% CI: 3.96–31.80).ConclusionsThis long-term visual acuity prognostic model demonstrates adequate performance for individualized prediction and assists in clinical decision-making. The risk stratification index guides optimal timing for surgery.

Consumer Behavior in the Digital Marketplace: A Fuzzy Evaluation Approach

With the growing prevalence of the internet and the development of logistics, consumers face increasingly complex choices when shopping online. Therefore, this article introduces fuzzy comprehensive evaluation to address these shopping issues. In analyzing consumers' purchase decision-making problems for online shopping, it is essential to subjectively evaluate products, such as price, quality, and reputation. By constructing fuzzy relationships and assigning weights, this method effectively handles the ambiguity and uncertainty of evaluation information, reflecting consumers' subjective preferences in actual shopping decisions. This study concludes that quality is the dominant factor among numerous commodity factors,and it also established a shopping decision model, which aids consumers in making the best decisions during their shopping, thereby enhancing shopping efficiency and satisfaction. The results indicate that compared to traditional methods, this method can more accurately focus on consumer purchase intention and improve the understanding of shopping decisions. The paper summarizes the fuzzy comprehensive evaluation of its application value in shopping problems, pointing out that it can better simulate human fuzzy decision-making processes and provide a more practical shopping decision assistance tool. This study not only enriches the application of fuzzy comprehensive evaluation theory in consumer behavior research, but also provides consumers with a scientific and practical shopping decision support method.

Urban Habitat Quality Enhancement and Optimization under Ecological Network Constraints

The process of urbanization leads to the rapid expansion of construction land and brings a series of ecological and environmental problems. The ecological network, as a linear landscape element, is of great significance to improve the quality of the regional ecological environment. In this study, the Morphological Spatial Pattern Analysis (MSPA) and the model of Minimum Cumulative Resistance (MCR) were used to construct the ecological corridors in the central city of Fuzhou, and the land use pattern under the constraints of the ecological network was simulated and quantified by the patch-level land use simulation (PLUS) tool with the results of the identification of ecological corridors. Meanwhile, with the help of InVEST habitat quality model, the regional habitat quality under different development scenarios was compared. The results show that (1) 19 ecological sources and 35 ecological corridors were identified; (2) under the constraints of ecological corridors, the area of forested land in the study area in 2027 was increased by 1.57% and the area of built-up land was reduced by 0.55% compared with that in 2022; (3) and under the constraints of ecological corridors, the mean value of habitat quality in Fuzhou City improved by 0.0055 and 0.0254 compared with 2022 and 2027 natural evolution scenarios, respectively. The study provides decision-making assistance for the construction of ecological corridors from the perspective of land use planning.

Early prediction of intraoperative hypothermia in patients undergoing gynecological laparoscopic surgery: A retrospective cohort study.

Intraoperative hypothermia is one of the most common adverse events related to surgery, and clinical practice has been severely underestimated. In view of this, this study aims to build a practical intraoperative hypothermia prediction model for clinical decision-making assistance. We retrospectively collected clinical data of patients who underwent gynecological laparoscopic surgery from June 2018 to May 2023, and constructed a multimodal algorithm prediction model based on this data. For the construction of the prediction model, all data are randomly divided into a training queue (70%) and a testing queue (30%), and then 3 types of machine learning algorithms are used, namely: random forest, artificial neural network, and generalized linear regression. The effectiveness evaluation of all predictive models relies on the comprehensive evaluation of the net benefit method using the area under the receiver operating characteristic curve, calibration curve, and decision curve analysis. Finally, 1517 screened patients were filtered and 1429 participants were included for the construction of the predictive model. Among these, anesthesia time, pneumoperitoneum time, pneumoperitoneum flow rate, surgical time, intraoperative infusion, and room temperature were independent risk factors for intraoperative hypothermia and were listed as predictive variables. The random forest model algorithm combines 7 candidate variables to achieve optimal predictive performance in 2 queues, with an area under the curve of 0.893 and 0.887 and a 95% confidence interval of 0.835 to 0.951 and 0.829 to 0.945, respectively. The prediction efficiency of other prediction models is 0.783 and 0.821, with a 95% confidence interval of 0.725 to 0.841 and 0.763 to 0.879, respectively. The intraoperative hypothermia prediction model based on machine learning has satisfactory predictive performance, especially in random forests. This interpretable prediction model helps doctors evaluate the risk of intraoperative hypothermia, optimize clinical decision-making, and improve patient prognosis.

The mediating effects of parental hope and psychological resilience on social support and decision conflict in children with hypospadias

IntroductionThe most common congenital genital abnormality of the urinary system in infants is hypospadias; its frequency is rising to 0.33 percent globally. Surgical reconstruction is the most effective treatment for hypospadias. Parents, as surrogate decision-makers for children, frequently experience decision-making conflicts when making decisions. Stress is neither a straightforward stimulus nor a simple response. However, a stressor (stimulus) caused by life events and influenced by numerous intermediate factors including personality, social support, and psychosomatic symptoms or diseases is a multi-factor process of stress response (response) . The surgical decision conflict of parents of children with hypospadias is a type of stress response, and social support may have an effect on parents' hope, psychological resilience, and decision conflict, with hope and psychological resilience serving as mediators between social support and decision conflict. AimTo investigate the structural relationship between parental social support, hope, psychological resilience, and parents decision conflict in children with hypospadias. Study DesignA cross-sectional design was used to collect the data of 210 parents of children with hypospadias from Department of Urology, Shenzhen Children's Hospital from April 2022 to March 2023. ResultsSocial support indirectly affects decision conflict through hope and psychological resilience, with a total effect of -0.224 (P<0.05), DiscussionDecision conflicts and regret can be reduced if medical professionals provide adequate and clear information, decision assistance, and meet support requirements during the decision-making process. Improving the level of psychological resilience, hope, and social support among parents of children with hypospadias is a crucial strategy for reducing the level of decision conflict they experience. In the process of making surgical decisions for children with hypospadias, the more sufficient social support from relatives, friends, and medical staff can stimulate their hope, boost their belief in the success of surgery, and the greater their psychological resilience, the more effectively decision conflict can be reduced. Some potential limitations of this study should be considered. Firstly, this study was a cross-sectional survey design. It may have some limitations in providing causal relationships between selected variables. Second, the survey investigated only one hospital, which reduces the generalizability of the findings. ConclusionThe data support the proposed model and confirm the structural relationship among the four study variables. This study provides new information on the mediating role of hope and psychological resilience between social support and decision-making conflict.

Cost Analysis of a Finite Capacity Queue with Server Failures, Balking, and Threshold-Driven Recovery Policy

The study is on the cost dynamics which is concerned with finite queues in particular to susceptibility to server outages, threshold-driven recovery and Catastrophic events, but main focus is on balking behaviour of the customers. A thorough investigation of the financial implications of interruptions and efficacy to recovery strategies is done and to alleviate the impact of breakdowns, disasters and customer refusals the queue system designs through empirical research is conducted. Various recovery policies have been compared with the existing thresholds, and the study further endeavors to categorize the cost-effective approach to mitigate the economic concerns of server failures and catastrophic incidents. The study brings discernments on the expected development in the management of the queuing system along with the assistance in complete decision-making in cost efficiency and enhancement of the complete system performance.

Development and Application of Drilling Intelligent Assistant Decision System

Abstract In order to systematically harness the substantial volume of data generated during the drilling process and achieve intelligent analysis and auxiliary decision-making for real-time risk monitoring and warnings during drilling, an intelligent drilling auxiliary decision-making system has been developed. This system integrates various technologies, including physical models, intelligent algorithms, and trend analysis. By analyzing the characteristics of logging data under different operating conditions, an intelligent algorithm capable of automatically distinguishing six types of drilling operating conditions has been established. Taking into account the impact of rock cuttings on the stress on the pipe string and annular pressure loss during the drilling process, coupled frictional torque and cyclic pressure loss calculation models were developed under coupled conditions. Building upon this foundation and considering the trend of deviation between predicted and measured parameters, a monitoring algorithm for detecting stuck drilling, wellbore leakage, and overflow incidents was formulated. The development of the drilling intelligent decision-making system was completed based on a C/S three-layer architecture and a data center. This system has been successfully applied in more than 50 exploration wells of various types, including shale oil and gas horizontal wells and deep wells. The static analysis results demonstrated a compliance rate of 91.5% with on-site monitoring, playing a crucial role in drilling parameter optimization and risk monitoring. The practicality and feasibility of the system have been verified, providing essential support for efficient and safe drilling production.

The Assisted Decision Making (Capacity) Act 2015 in Action: Ireland East Hospital Group Marks 1 Year Milestone

Abstract Background In Ireland, the Assisted Decision Making (ADM) (Capacity) Act 2015 was enacted in April 2023. The Act supports a new progressive rights-based system of supported decision making that requires all healthcare staff to make some changes in their clinical practice. The Health Service Executive (HSE) developed an implementation plan to support organisations to meet the legislative requirements of the Act. Methods Assessing implementation progress across ten acute hospital sites using the HSE Implementation Framework (HSE, 2015). Progress was evaluated across seven thematic areas: Operational Implementation, Communication Strategy, Learning Strategy, Alignment of Current Operational Approaches, Quality and Compliance, Risk Consideration and Resources. Results Significant progress has been made across all seven domains with strong engagement from all sites. Acknowledging variation is important given the breadth of services provided across these hospitals. Implementation is an ongoing process given the cultural changes needed, the resource implications and the interplay with external stakeholders. Conclusion Further development in key areas includes tailoring education to suit the specific needs of individual units within hospitals and embedding this education into existing education programmes. It is crucial to monitor resource requirements and adapt to regulatory requirements to ensure compliance. Moving forward sustained efforts will be necessary to embed the principles of supported decision making firmly within healthcare delivery, ultimately promoting dignity and autonomy for all patients.

A fuzzy multi-criteria decision-making for optimizing supply chain aggregate production planning based on cost reduction and risk mitigation

In an increasingly complex and uncertain business environment, decision makers require robust strategies for supply chain aggregate production planning that account for the interdependencies and coordination across multiple echelons of the supply chain network. Traditional approaches often inadequately address the uncertainties and risks inherent in supply chain operations, leading to suboptimal performance and increased costs. This study introduces a business model incorporating open innovation dynamics to achieve cost-effectiveness and resilient supply chain performance in the face of uncertainties. A Multi-Objective Fuzzy Linear Programming model was proposed by integrating the Chance-Constraint Programming and Zimmermann’s approach designed to assist decision makers in optimizing the production plan, material flows and resource allocation across the entire supply chain network. The model focuses on both cost and risk minimization based on unsymmetrical triangular fuzzy numbers. Specifically, it targets the downside risk of uncertainty, aiming to reduce the likelihood of negative outcomes or financial losses due to fluctuations, unpredictability, and unforeseen circumstances, which can drive-up supply chain operation costs. The efficacy of the model is demonstrated through a case study. It highlights various imprecise factors such as costs, customer demands, and machine operating hours. The findings underscore the model's capability to provide decision makers with a comprehensive supply chain aggregate production plan that not only optimizes the supply chain network and enhances operational efficiency and reliability but also significantly reduces costs and mitigates unsymmetrical skewness of risks associated with operational uncertainties.

Modeling nitrogen behavior in Tigris River using system dynamics approach

Nitrogen behavior across multimedia environment like Tigris River is complex process resulting in a number of interactions of the biogeochemical processes. A solid understanding of the interacted processes is critical to evaluate river system performance. Inorganic nitrogen including ammonium ion (NH4+), free ammonia (NH3), total ammoniacal nitrogen (NHx), nitrite (NO2−), nitrate (NO3−), total oxidized nitrogen (NOx), and total inorganic nitrogen (TIN) in Tigris River were examined from April 2018 to March 2020. A simple system model was implemented to simulate and to help interpret the trend of inorganic N forms based on datasets collected from the river system. It is indicated that the obtained results revealed that the dynamic model was able to represent and predict NHx and NOx behavior in Tigris River. Root mean squared errors between actual and predicted concentrations for NHx and NOx were 0.053 and 0.312 g m−3, respectively. Modeling analysis confirmed the steady-state dynamics of NHx and NOx over the course of the study. Average concentration for NHx was 0.37 ± 0.05 g m−3, while mean value of NOx levels was 2.18 ± 0.33 g m−3 in the Tigris river water. The results from this study also supported by the changes of the water budget, chemical oxygen demand, pH, dissolved oxygen, electrical conductivity, and water temperature in the river system. The results suggest that the microbial mechanisms are the principal N transformation processes in the river system. Findings presented in this research could help to improve conceptual understanding of N behavior in Tigris River, and therefore could assist decision makers to select best management practices and to develop mitigation strategies to sustain water quality in Tigris River.

A multi-point leakage prediction statistical method using Bayesian inference in water distribution networks

ABSTRACT Leakage in water distribution networks (WDNs) not only leads to serious water loss and pipe contamination but also affects residents’ daily water. Accurate localization of leaks in WDN is significant to conserve water resources and reduce economic losses. However, in traditional optimization and verification methods for leak detection, factors such as modeling and pressure monitoring point errors are not taken into account, resulting in the deviation from the actual location of leaks. To address the mentioned issues, this article presents a WDN leakage probability prediction method based on Bayesian inference. The method converts the prior probability of leakage events into posterior probability. Then, by utilizing the posterior probability density function, the uncertainty of modeling and measurement errors in the hydraulic simulation model are quantified. The probability distribution of leakage pipes and leakage quantity within the leakage area is calculated, allowing for the location prediction and corresponding magnitude of leakage. The experimental results indicate that the prediction model can detect unknown quantities of leakage events. By collecting multiple sets of leakage data, it is possible to accurately predict the location and quantity of leaks, enhancing the efficiency of leakage detection in large-scale water supply networks and providing decision-making assistance for water utilities.

成年监护范式的反思与进路：从替代决定到协助决定

Substitution decision and assistance decision are the two classic paradigms of adult guardianship. Article 22 of the Civil Code points out the position of applying partial guardianship to adult wards based on the integration of Article 47 of the former Contract Law. Article 35, paragraph 3, of the same law expresses the new concept of guardianship in a textual sense, which is “based on independence, supplemented by safeguards and assistance”. Both articles make it clear that adult guardianship is not full guardianship, but there is a dispute as to whether it should be primarily independent and assisted guardianship or non-independent and substituted guardianship. The understanding of the adult guardianship paradigm should be based on the systematic interpretation of Chapter 2 of the General Provisions of the Civil Code, and the direct interpretation of Article 35(3) as assisted guardianship lacks the value support of other articles. This paper will start from the argumentation of the Civil Code’s legislative technology for the guardianship model and the paradigm it adopts, analyze the problems of the adult guardianship system under the substitute decision, such as the ill-defined scope, the re-marginalization of the elderly, and the emphasis on property rather than human beings, etc., and then finally, through the expansive interpretation of Articles 22 and 33, achieve the unity of the value concepts and scope of exercise with Article 35, paragraph 3, and construct the space of the law for the transformation of the assisted decision.

A fuzzy multi-objective model for aggregate production planning considering energy consumption, carbon emission, chemical, solid waste, and wastewater

With a growing focus on environmental conservation, enterprises continuously reevaluate their conventional profit-driven business models. This shift has led to the development of green production planning models, attracting considerable attention in recent years. Previously, energy consumption or carbon emission were the most considered green parameters in the production planning problems. Nevertheless, several other essential green aspects such as reductions in chemical, solid waste, and wastewater are usually neglected. Therefore, this study proposes a multi-objective model for environmentally conscious aggregate production planning, simultaneously considering the above-mentioned green objectives. Additionally, the uncertain parameters of the model, including the green parameters, were modelled through fuzzy programming. Furthermore, this study incorporated a contradicting relationship between energy consumption cost and associated carbon emission, which was previously presented as non-contradicting. This relationship was illustrated by utilizing the time-of-use electricity tariff scheme. To solve the model, the initial step involved the defuzzification of uncertain attributes, followed by the transformation of the multi-objective model into a single-objective model. Finally, the IBM ILOG CPLEX Optimization Studio solver was utilized to solve the model based on the data collected from a precision engineering company. The generated production plan achieved an overall satisfaction level of 71%, which is reasonably acceptable to the decision maker. Moreover, sensitivity analysis has been conducted for the degree of optimism of the objective functions and the feasibility degree of the constraints. A general overview of the results associated with the performed sensitivity analysis indicated that the optimum values of the objective functions deteriorate as the parameters increase, though all remain within an acceptable range. The proposed study will assist decision makers in generating production plans that are both economically and environmentally feasible.