Optimal Strategy Research Articles

Independence or cooperation? Evaluating emission reduction strategies in non-competitive and competitive environments

This study combines a dynamic game and the Nash bargaining model to discuss the choice of manufacturers’ emission reduction strategies independently or in cooperation with cleantech firms under the cap-and-trade mechanism. The models comprise one manufacturer and one cleantech firm in a non-competitive environment, and two manufacturers with different emission reduction capacities in a competitive environment. The results show that competition will produce significant substitution effects, and some sensitivity analyses of the disadvantaged manufacturer tend to be opposite to those under a non-competitive environment. On the contrary, cooperation will bring significant positive impacts, carbon reduction rates will be improved after cooperating with cleantech firms. However, when profits are concerned, the Nash equilibrium model (C, C) may fall into a prisoner’s dilemma choice, while cleantech firms with low technical improvement capacities can drive it to become a Pareto optimality strategy, which will cause false prosperity in the cleantech industry. Given this, the objective of carbon neutrality and the cooperation model with the same cleantech firm is further extended, showing effective cooperation and improvement to provide new perspectives into emission reduction strategies.

Artificial Intelligence in Modernization of Pharmaceutical and Healthcare Industry: A Review

: Artificial intelligence (AI) falls under the purview of computer technology, which analyzes complex data and helps solve problems in different segments. Big Data, Machine Learning, and AI are currently being used by the major pharmaceutical industries to minimize time and costs and increase possibilities. Artificial intelligence is used in the pharmaceutical industry in diverse ways, such as drug discovery and development, clinical trials, disease diagnosis, and different stages in pharmaceutical manufacturing, data analysis, and supply management. Most of the cost and time are involved in drug discovery and clinical trials. Artificial intelligence can minimize human error in data processing, documentation, data integrity issues, and data selection throughout the journey. It works in descriptive, diagnostic, predictive, and prescriptive mode. Major pharmaceutical conglomerates like Pfizer, Roche, Novartis, and Johnson & Johnson have already applied Artificial Intelligence in different segments of pharmaceutical and medicinal science. Tech companies like IBM Watson, Catalia Health, Intel, Microsoft, and Google, in collaboration with pharmaceutical companies, are working in the different areas of drug discovery, early diagnosis, and personalized medicine. Further, AI finds application in the health sector for data management, scanning and evaluation of medical history reports, and finding optimum treatment strategies for chronic care patients. Though lots of research and development are being done on the utilization of artificial intelligence in the pharmaceutical industry, it is still in the nascent stage. This article is our endeavor to study, in detail, the present and future opportunities of machine learning and AI in the pharmaceutical industry as a whole.

Optimum Surgical Strategies for Medial Sphenoid Wing Meningiomas: A Comprehensive Evaluation of Resection Extent, Visual Outcomes, and Vascular Injury

IntroductionVisual impairment affects 55-80% of medial sphenoid wing meningiomas (mSWMs) patients, making optic nerve (ON) decompression a critical surgical goal. 1–3 Complete resection often leads to better visual outcomes.4 However, involvement of critical neurovascular structures increases postoperative morbidity and mortality, with vascular injury reported in 18-20% of cases.3,5–7 This study aims to evaluate the relationship between the extent of resection (EOR), visual outcomes, and the incidence of vascular injury, seeking to identify the optimal surgical approach for mSWMs. Materials and MethodsWe retrospectively analyzed data from patients undergoing surgery for mSWM at our tertiary care center from January 2001 to December 2021. Inclusion criteria included histopathologically confirmed globoid mSWMs(N=89). Patients with recurrent tumors(n=14) or lost to follow-up (n=9) were excluded. We classified patients into two groups based on EOR using Simpson’s grade: Group 1(good-resection,Simpson Grade-I/II,n=51) and Group 2 (poor-resection,Simpson Grade III/IV,n=15). ResultsAmong 66(=N) patients, visual impairment was the most common symptom (81.8%), followed by headaches(77.3%) and seizures(27%). T2-hyperintensity on MRI[(OR:5.4, 95%CI:1.5- 18.6)(p-value<0.01)] and CS-extension[(OR:3.9, 95%CI:1.1-13.1)(p-value-0.02)] were independent significant predictors of poor resection. Visual status was preserved in 90.3% of Group-1 and 86.6% of Group-2, with no significant difference based on EOR. Vascular involvement was noted in 87.9%, higher than the vessel encasement(>1800)(57.6%,p=0.04). Vessel injury occurred in 7.8% of Group-1 and 6.6% of Group-2, with no significant impact on EOR. ConclusionCS-extension and T2-hyperintensity predict poor resection rates in mSWMs. While visual outcomes are not directly affected by EOR, long-term visual status may decline due to tumor recurrence and radiotherapy. Vascular injury incidence is not associated with EOR. Thus, the "maximal safe resection" of mSWMs involves a surgical strategy balancing targeted aggressive and conservative resection for maximal cytoreduction and functional preservation.

Optimal Arrangements of Renewable Energy Systems for Promoting the Decarbonization of Desalination Plants

In this research, a renewable energy hybrid system (PV-Wind) is modeled to compare different design options based on their economic and technical features. The energy requirements of a Reversible Osmosis desalination plant located on the island of Tenerife with a water production capacity of up to 20,000 m3/day was considered. The system is connected to the electricity grid. The HOMER software, version 2.75 was used to produce optimum strategies for renewable energy. The assumptions input into the model were: the technical specifications of the devices, electricity demand of the desalination plant, as well as the solar radiation and the wind speed potentials. Numerous arrangements were considered by the software, version 2.75. The optimal results were obtained based on the use of renewable energy. The data used in the study were recorded in Tenerife in the Canary Islands. The experience of this research could be transferred to other Atlantic islands with similar renewable energy sources (specifically the wind) and water scarce conditions.

Terpolymers of alkyl methacrylate-trans anethole-1,2,3,6-tetrahydrophthalic anhydride copolymers: a low dosage and high-efficiency cold flow improver for diesel fuel

The addition of cold flow improvers (CFIs) is considered as the optimum strategy to improve the cold flow properties (CFPs) of diesel fuels, but this strategy is always limited by the required large dosage. To obtain low-dosage and high-efficiency CFIs for diesel, 1,2,3,6-tetrahydrophthalic anhydride (THPA) was introduced as a third and polar monomer to enhance the depressive effects of alkyl methacrylate-trans anethole copolymers (C14MC-TA). The terpolymers of alkyl methacrylate-trans anethole-1,2,3,6-tetrahydrophthalic anhydride (C14MC-TA-THPA) were synthesized and compared with the binary copolymers of C14MC-TA and alkyl methacrylate-1,2,3,6-tetrahydrophthalic anhydride (C14MC-THPA). Results showed that C14MC -THPA achieved the best depressive effects on the cold filter plugging point (CFPP) and solid point (SP) by 11°C and 16°C at a dosage of 1250 mg/L and monomer ratio of 6:1, while 1500 mg/L C14MC-TA (1:1) reached the optimal depressive effects on the CFPP and SP by12°C and 18°C. THPA introduction significantly improved the depressive effects of C14MC-TA. Lower dosages of C14MC-TA-THPA in diesel exerted better improvement effects on the CFPP and SP than that of C14MC-TA and C14MC-THPA. When the monomer ratio and dosage were 6:0.6:0.4 and 1000 mg/L, the improvement effect of C14MC-TA-THPA on diesel reached the optimum level, and the CFPP and SP were reduced by 13°C and 19°C, respectively. A 3D nonlinear surface diagram fitted by a mathematical model was also used for the first time to better understand the relationships of monomer ratios, dosages, and depressive effects of CFIs in diesel. Surface analysis results showed that C14MC-TA-THPA achieved the optimum depressive effects at a monomer ratio of 6:0.66:0.34 and dosage of 1000 mg/L, and the CFPP and SP decreased by 14°C and 19°C, respectively. The predicted results were consistent with the actual ones. Additionally, the improvement mechanism of these copolymers in diesel was also explored.

Стратегии выхода российских организаций на международные рынки в изменившихся внешнеэкономических условиях

The change in the system of world economic communications as a result of sanction policy towards Russia and its allied countries forces enterprises and organizations to prove strategies of entry into foreign markets for the purpose of income generation and inclusion in new value chains. Therefore, the purpose of this research is the comparative analysis of factors and assessment of the application of international market entry strategies of Russian IT companies. Qualitative and quantitative methods of economic research were applied to achieving this goal. Qualitative methods included PESTEL analysis, situation and comparative analysis. Quantitative methods included the cost-benefit method, statistical techniques and economic analysis methods. Tabular and graphic approaches are used for text visualization. In this research the factors influencing organizations activity in the international sphere and also the main foreign market entry strategies of IT companies were analyzed. Comparative analysis revealed benefits and shortcomings of each strategy, taking into account modern forms of business and currency risks. For the Russian IT company producing software three strategies were evaluated: export, franchising and direct investments. The choice of the IT industry for considering international market entry strategies of Russian organizations is proven. It is necessary to separate the development of new technologies and infrastructure, existence of financial resources and pressure of economic sanctions from the factors influencing this process. Of the three strategies analyzed, the lowest-cost and lowest-profit strategy was the franchising strategy; it can be used when there is a shortage of financial resources. For “growth hacking” the expensive, but highly profitable strategy of direct investment taking into account currency hedging is applicable. Proceeding from the analysis of the influencing factors and legal conditions of the Russian Federation it is expedient to follow the strategy of average profitable export for ensuring company development. The practical application of strategies for organizations to enter foreign markets is a multifaceted object of research that requires a comprehensive analysis of various aspects of business expansion. The received results will help companies make weighed decisions when choosing the optimum strategy for expanding their business in the international market.

Epidemic dynamics edge caching strategy for 6G networks

By caching popular content on edge servers closer to users to respond to users’ content requests in 6G networks, the transmission load of backhaul links can be reduced. However, the time-varying characteristics of content prevalence leads to the issue that the cache content may not match the user’s needs, resulting in a decrease in cache success ratio. To solve these issues, we proposed a cache distribution strategy based on epidemic dynamics (CDSED) for 6G edge network. First, a 6G edge caching content model (6G ECCM) is constructed to establish the process of cache content propagation among users as an infectious disease propagation process, analyze the distribution of users’ interest in cache content and obtain the cache content state probability prediction equation, and use the cache content state probability prediction equation to predict the cache content prevalence. Second, based on the predicted prevalence results, a prevalence predictive genetic-annealing cache content algorithm (PGAC) is proposed with the optimization objective of maximizing the cache success ratio. The algorithm designs the selection function of the traditional genetic algorithm as a simulated annealing selection function based on the cache content success ratio, which avoids the defect of the genetic algorithm that converges to the locally optimum cache strategy too early and enhances the cache success ratio. Finally, the optimum cache content decision is solved by iterative alternation. Simulation results demonstrate that CDSED strategy can enhance cache success ratio than the LRU strategy, the LFU strategy, and the MPC strategy.

A Review of Central Bank Digital Currency: Current Status and Changing Trends

Since the emergence of the Central Bank Digital Currency (CBDC), which digitally represents the legal currency of the country in which it is created, significant developments have been experienced in the global financial environment as a result of the changes brought about by the innovations. Although CBDCs offer various advantages, such as cost reduction, financial inclusion, and increasing the efficiency of payment systems, they may create some disadvantages, such as the risk of dysfunctionalization of the banking sector or decreasing the level of security. At this point, it is necessary to be aware of the possible risks it may create and to develop an optimum strategy in case such risks arise to establish a successful CBDC system and benefit from its advantages. In this context, the aim of this study is to analyze all aspects (emergence process, positive/negative sides, projects, use cases, and technical infrastructure to be used) of this digital currency and to make a comprehensive evaluation of CBDC applications around the world. The evaluations show that many countries are not indifferent to this new digital currency, which has a very high development potential, and continue their research intensively in order not to fall behind the steps taken in CBDC.

From data to decision: integrated approach to pavement preservation in Iowa through treatment effectiveness analysis

ABSTRACT This investigation seeks to enhance ‘Iowa's Pavement Preservation Guide’ with a data-informed decision-making framework. Iowa DOT's databases were used as the data source for evaluating pavement performances under different preservation treatment regimens and determining the effectiveness of those strategies. Five preservation treatment methods for flexible and composite pavements, namely, crack sealing-and-filling, microsurfacing, slurry sealing, patching, and thin hot mix asphalt (HMA) overlays were evaluated using historic data from about 7000 road segments between 1998 and 2020. The data included various attributes describing pavement, road, and traffic characteristics required for analysing condition-based performance and treatment impacts. Two key indicators, service life extension (LE) and index benefit (IB), were defined and used to quantify treatment effectiveness in terms of pavement condition improvement and impact on deterioration rate. The treatment-effectiveness indicators were then factored into the cost analyses to assess the cost-effectiveness of preservation strategies. The results of the treatment effectiveness analysis were used to develop decision matrices that provide insights for selecting preservation treatments based on benefit–cost analysis considering both the overall condition and the dominant distress. This approach to treatment effectiveness highlighted the distinction between an optimum pavement preservation strategy and one that targets maximum service life extension.

Maternal mental health and breastfeeding duration: the role of optimism and coping strategies

Background/Aims Mental health can influence the likelihood that a woman will initiate and continue breastfeeding behaviour that is associated with improved infant and maternal health. Mental health outcomes are associated with variables such as optimism and coping. The aim of this study was to explore the relationship between coping, optimism and mental health, and their impact on breastfeeding duration. Methods A total of 612 women who had breastfed completed an online questionnaire comprising the hospital anxiety and depression scale, cognitive emotional regulation questionnaire, brief coping orientation to problems experienced inventory and life orientation test. Data were analysed using correlations, ordinal linear regression and mediation analysis. Results There was a direct relationship between breastfeeding duration and depression but no direct relationship between duration and anxiety. A mediation analysis showed no mediating between breastfeeding duration and depression. Optimism did mediate the relationship between anxiety and duration, with optimism being associated with reduced anxiety and increased duration. Conclusions The findings highlight the importance of support for mothers while breastfeeding to improve mental health outcomes and foster coping strategies, such as optimism.

Distributed Optimal Attitude Synchronization Control of Multiple QUAVs via Adaptive Dynamic Programming.

This article proposes a distributed optimal attitude synchronization control strategy for multiple quadrotor unmanned aerial vehicles (QUAVs) through the adaptive dynamic programming (ADP) algorithm. The attitude systems of QUAVs are modeled as affine nominal systems subject to parameter uncertainties and external disturbances. Considering attitude constraints in complex flying environments, a one-to-one mapping technique is utilized to transform the constrained systems into equivalent unconstrained systems. An improved nonquadratic cost function is constructed for each QUAV, which reflects the requirements of robustness and the constraints of control input simultaneously. To overcome the issue that the persistence of excitation (PE) condition is difficult to meet, a novel tuning rule of critic neural network (NN) weights is developed via the concurrent learning (CL) technique. In terms of the Lyapunov stability theorem, the stability of the closed-loop system and the convergence of critic NN weights are proved. Finally, simulation results on multiple QUAVs show the effectiveness of the proposed control strategy.

Optimal Maintenance Service Strategy of Service-Oriented Aviation Manufacturers for Two-Stage Leased System Under Capacity Limits

Optimal Maintenance Service Strategy of Service-Oriented Aviation Manufacturers for Two-Stage Leased System Under Capacity Limits

Data-Driven Optimal Bipartite Consensus Control for Second-Order Multiagent Systems via Policy Gradient Reinforcement Learning.

This article investigates the optimal bipartite consensus control (OBCC) problem for unknown second-order discrete-time multiagent systems (MASs). First, the coopetition network is constructed to describe the cooperative and competitive relationships between agents, and the OBCC problem is proposed by the tracking error and related performance index function. Based on the distributed policy gradient reinforcement learning (RL) theory, a data-driven distributed optimal control strategy is obtained to guarantee the bipartite consensus of all agents' position and velocity states. In addition, the offline data sets ensure the learning efficiency of the system. These data sets are generated by running the system in real time. Besides, the designed algorithm is an asynchronous version, which is essential to solve the challenge caused by the computational ability difference between nodes in MASs. Then, by means of the functional analysis and Lyapunov theory, the stability of the proposed MASs and the convergence of the learning process are analyzed. Furthermore, an actor-critic structure containing two neural networks is used to implement the proposed methods. Finally, a numerical simulation shows the effectiveness and validity of the results.

Re-watering solution facilitates seed germination and seedling growth of Carex schmidtii: Implication for species re-introduction in degraded semi-arid wetlands

Water deficiency threatens the health and function of wetlands in semi-arid areas. Optimum re-watering is an effective method for close-to-natural restoration to mitigate wetland degradation. Although the ecological importance of optimal re-watering as a nature-based solution for promoting wetland plant growth has been widely recognized, the response mechanisms of seed germination and seedling growth to re-watering are still poorly understood despite their decisive impact on plant life history. To fill this gap, this study compared the characteristics of seed germination and seedling growth in Carex schmidtii under initial water content with three levels (30%, 50%, and 70%) and five re-watering treatments (maintained at constant water content and re-watering to 100% on 7th, 14th, 21st, and 28th day). Moreover, the degree of reserve mobilization during four germination stages (seed suckering, sprouting, 20% germination, and seedling growth) was investigated. The results showed that water deficiency and re-watering treatments significantly affected C. schmidtii seed germination, seedling growth, and reserve mobilization. Compared with the other treatments, 50% moisture content and re-watering to 100% on the 14th day (50%-RT3) treatment significantly improved germination traits (germination rate, daily germination rate, germination index, and vigor index) and seedling growth characteristics (shoot length, root length, shoot biomass, root biomass, and total biomass). Furthermore, the degree of mobilization of starch, soluble protein, fat, and soluble sugar accumulation in C. schmidtii seeds under 50%-RT3 was higher than that in the other treatments. The structural equation model showed that the characteristics of seed germination and seedling growth of C. schmidtii were directly related to water deficiency and re-watering treatments, whereas reserve mobilization indirectly affected seed germination and seedling growth. These findings demonstrated that water deficiency and re-watering treatments have a crucial regulatory effect on seed germination and seedling growth of wetland plant species through a dual mechanism. This study provides information for the formulation of an optimum re-watering strategy for wetland vegetation restoration in semi-arid areas of the world.

Toward Rapid and Optimal Strategy for Swarm Conflict: A Computational Game Approach

Toward Rapid and Optimal Strategy for Swarm Conflict: A Computational Game Approach

Comparison of multi-criteria decision-making methods for selection of optimum passive design strategy

In the pursuit of achieving high-performance building design, the selection of the most suitable passive design strategies often involves the use of multi-criteria decision-making (MCDM) methods to address multiple conflicting criteria simultaneously. However, identifying the appropriate MCDM method for a specific building design context poses a challenge, as methods commonly effective in other contexts may not yield equivalent results. This study evaluates five MCDM methods (AHP, COPRAS, TOPSIS, VIKOR, and WSM) to understand their sensitivity in recommending the best solution. The considered criteria are energy demand, thermal comfort and daylight availability. The sensitivity analysis involves the impact of the variability of assigned weights on the rank shifting given by the considered MCDM method and the sensitivity of each criterion to weights variability. The findings reveal that implementing a fair-weight allocation leads to similar top 5 solutions among all MCDM methods. However, when a negative shift is applied to each criterion weight, AHP demonstrates greater robustness to weight variability compared to the other methods evaluated, while VIKOR is the most sensitive to weight variation.

A review of the current treatment methods for retroperitoneal fibrosis with obstructive uropathy.

Retroperitoneal fibrosis (RPF) is a fibroinflammatory disease in which patients may suffer obstructive uropathy (OU). The optimum treatment strategy for RPF with secondary OU is currently unclear, and the aim of this literature review is to assess the methods used to treat this patient cohort. Medline, Embase, Cinahl, the Cochrane Library and PubMed were systematically searched to find studies assessing treatment outcomes in this patient cohort. After reviewing the studies' titles, abstracts and full texts, 12 were found that matched our search aims. Data from these publications were analysed and reported. The demographic and symptomatic features of patients across the 12 studies were representative of the general RPF population. No randomised control trials (RCTs) were found, and just one study formally compared outcomes between patients who underwent different treatment strategies. Many of the studies concluded that using medical and surgical methods in combination led to positive outcomes; whereas, others found positive outcomes following a variety of regimens. Many studies also highlighted, however, that significant minorities required further treatment after initial therapy. Conclusions regarding optimum treatment methods were limited as most publications did not formally compare outcomes following different strategies and had an observational study design. Although positive outcomes were commonly seen following medical, surgical and a combination of treatments, the literature currently lacks research formally comparing outcomes after assigning specific treatment protocols to groups of RPF patients. More research is therefore required to determine how to best manage RPF leading to secondary OU.

Determining the Economically Optimum Metaphylactic Strategy for Cattle Cohorts of Varied Demographic Characteristics.

Metaphylactic antibiotic use in feeder cattle is a common practice to control respiratory disease. Antimicrobial stewardship is important to ensure continued efficacy and to protect animal welfare. The objective of this study is to identify characteristics of cohorts of cattle that had not received metaphylaxis that would have benefited economically from the use of metaphylaxis. Cohorts (n = 12,785; 2,206,338 head) from 13 feedlots that did not receive metaphylaxis were modeled using an economic model to estimate net returns for three metaphylactic options. Logistic regression models with covariates for entry weight, sex, average daily weight gain, number of animals per cohort, and days on feed, with feedlot as a random effect, were used to determine the model-adjusted probability of cohorts benefiting economically from metaphylaxis. Most (72%) cohorts in this data set that had not received metaphylaxis at arrival would not economically benefit from metaphylaxis. Sex, entry weight category, number of cattle in the cohort, and average daily weight gain were associated with the likelihood of benefitting economically from metaphylaxis. The results illustrated that cattle cohort demographics influenced the probability that cohorts would benefit economically from metaphylaxis and the type of metaphylaxis utilized, and integrating this information has the potential to influence the metaphylaxis decision.

Substratifying the risk of covert malignancy in significant rectal polyps: Outcomes from a specialist multidisciplinary team (MDT).

A treatment strategy for patients with a significant polyp or early colon cancer (SPECC) of the rectum presents a challenge due to the significant rate of covert malignancy and lack of standardized assessment. For this reason, NICE recommends multidisciplinary meetings to improve outcomes. The primary aim of the present study was to report the performance of our specialist early rectal cancer (SERC) multidisciplinary team (MDT) in correctly substratifying the risk of cancer and to discuss the limitations of staging investigations in those patients with "poor outcomes". This was a retrospective review of patients referred to our SERC MDT from 2014 to 2019. Lesions were assigned by the MDT to three pre-resection categories (low, intermediate, high) according to the risk of covert malignancy. Resection method and final histology were compared to the pre-resection categories. Of 350 SPECC lesions, 174 were assessed as low-risk, 108 intermediate-risk and 68 high-risk. The cancer incidence was 4.8%, 8.3% and 53%, respectively (15.5% overall). Eight lesions were categorized as low-risk but following piecemeal resection were found to be malignant. Five lesions, three of which were categorized as high-risk, were ultimately benign following conventional surgery. One pT1sm1 cancer, removed by anterior resection, may have been treated by local excision. A total of 83% of malignant polyps were triaged to an en bloc resection technique and surgical resection avoided for nearly all benign lesions. However, 12 patients from this cohort were deemed to have a poor outcome because of miscategorization. Further comparative research is needed to establish the optimum strategy for rectal SPECC lesion assessment. There is currently no consensus for staging significant polyps of the rectum. This paper reports the effectiveness of a specialist early rectal cancer MDT to correctly risk-stratify significant rectal polyps. It underscores the importance of accurate categorization for treatment decision-making, while acknowledging the limitations of current staging modalities.

Carbon Neutrality Computational Cost Optimization for Economic Dispatch With Carbon Capture Power Plants in Smart Grid

To achieve carbon neutrality, reducing carbon emissions is crucial in dispatching problems in smart grid. Though renewable energy such as wind power has low carbon emissions, it suffers from random generation, which makes the thermal power necessary for a stable supply power system. To reduce carbon emissions, the thermal power plants are transformed into carbon capture power plants, which brings new challenges to economic dispatch algorithms. Besides, there are usually many constraints to keep the security operation of power systems, which incurs a large problem scale and high computational cost. Most existing methods either do not consider reducing carbon emissions, or suffer from high computational costs. In this paper, a framework for the carbon capture plants with wind power to reduce both running costs and carbon emissions is designed to support carbon neutrality. To reduce computational cost, initial-training and fine-tuning are used. A deep neural network is employed to describe the relationship between users' load and the constraints, which provides guides for finding the active constraints. Therefore, the problem scale can be significantly decreased, making the optimal dispatching strategy obtained quickly. The experimental results on real-world data show that the proposed framework can obtain the optimal strategy efficiently.