Fourth Strategy Research Articles

Выбор критериев для сокращения неизвестных в методе Ньютона–Рафсона в задаче нахождения равновесного состава железной руды и топлива

Finding the equilibrium composition of a multicomponent system is required in various industries associated with chemical reactions. Reduction of the unknowns in the original nonlinear equation is possible if most of the unknowns receive values equal to zero as a result of the solution. This situation is typical to solve the problem of finding an equilibrium composition of iron ore and fuel, where the unknowns are the number of moles of all possible reaction products from given simple substances. The dependence of algorithmic complexity on the number of unknowns to solve a SLAEs is expressed through the function O(N3). Therefore, reduction of the unknowns will speed up the computational process. Thus, the solution to the problem of selecting criteria to reduce the number of the unknowns at each iteration of the Newton-Raphson method as applied to the problem of finding the equilibrium composition of iron ore and fuel is relevant. The matrix dimensionality reduction has been achieved using four strategies. The first three strategies involve finding a minimum threshold (removing values that most quickly tend to zero), after which the number of unknowns will decrease. The fourth strategy is associated with reducing the dimension of the original matrix. The results of numerical experiments to reduce the unknowns and speed up calculations are presented. Criteria have been found under which the calculation speed increases by 2–4 times and variables whose value is not equal to zero are not reduced. The criteria to reduce the unknowns implemented in the T-Energу software package make it possible to solve the problem of finding the equilibrium composition of iron ore and fuel 2–4,2 times faster. In this case, the obtained solution for the components of the equilibrium composition with the reduction of the unknowns corresponds to the complete solution with an accuracy of 10–3. Due to acceleration at the same time, it is possible to construct equilibrium compositions over a temperature range with a temperature step that is 4 times smaller.

Design and analysis of zero-energy and carbon buildings with renewable energy supply and recycled materials

Given the notable consumption of fossil fuels, buildings prioritize their energy utilization. This paper’s aim is to explore the primary strategies in attaining zero-energy and zero-carbon buildings. The investigation employs two specialized software programs: Design Builder, which analyzes energy usage, and PV Syst, which evaluates renewable energy requirements. The study is conducted in Fort Myers, USA, and Dubai, UAE, leveraging diverse regional and climate discrepancies, and real-world data on zero-energy and carbon constructions for comparison and validation. Within the Design Builder software, the research examines four strategies, with the first serving as the realistic baseline. By implementing the second strategy, utilizing internal shading devices such as Shade Roll, a reduction of 376 kWh in Fort Myers and 591 kWh in Dubai is achieved. The third strategy encompasses the deployment of blind-type internal shading systems, resulting in reductions of 212 kWh in Fort Myers and 348 kWh in Dubai. The fourth strategy involves integrating renewable energy via photovoltaic panels, injecting 323.5 kWh in Fort Myers and 2237 kWh in Dubai into the power grid for lighting purposes. Furthermore, 3200.5 kWh and 4722 kWh of energy are respectively injected into the grid for heating in Fort Myers and Dubai. Cooling, however, necessitates additional energy from the grid, amounting to 2243.94 kWh in Fort Myers and 9211.64 kWh in Dubai. In the pursuit of zero-carbon goals, the implementation of suitable low-carbon recycled materials, in place of primary building materials, reduces carbon dioxide emissions from 79 tons to 7.315 tons in Fort Myers and 7.038 tons in Dubai. By employing appropriate materials, the zero-carbon objective is attained.

Integration of the wind and solar power for the dynamic economic emission dispatch with the charging and discharging of plug-in electric vehicles

Wind and solar power are incorporated into the dynamic economic emission dispatch with the plug-in electric vehicles (DEED-PEV) in this paper, seeking to reduce the generation cost and pollutant emission significantly. A Laplacian non-dominated sorting genetic algorithm-II (LNSGA-II) and a constraint handling method (CHM) are proposed for the DEED-PEV. The LNSGA-II applies the Laplace distribution to both the crossover and mutation. The Laplace-based crossover makes each individual carry out unidirectional search towards its partner and enhances the information exchange between each pair of individuals. The Laplace-based mutation makes each individual carry out bi-directional search surrounding itself and adjust the searching range adaptively. Therefore, the LNSGA-II is able to explore and exploit the decision space of the DEED-PEV sufficiently, contributing to the reductions of the generation cost and pollutant emission in the objective space. For the variables of every individual, the CHM limits them within the feasible operating zones. The CHM also limits them within the dynamic boundaries. It further adjusts them according to the power balances. The power generation constraints and up/down ramp rate constraints can be always satisfied while the elimination of the total violation of the other constraints can be expedited. Therefore, the CHM can transform infeasible individuals to feasible ones rapidly. Four strategies are investigated for six PEV charging/discharging scenarios. The first three strategies consider no more than one kind of renewable energy generation while the fourth strategy involves both the wind power and solar power. Experimental results suggest that the fourth strategy is more economical and environment-friendly than the other three strategies. Furthermore, the LNSGA-II outperforms the other three competitor algorithms for six DEED-PEV problems in the light of hypervolume, coverage rate and spacing.

A corpus-based study of translating idioms from English into Arabic using audio-visual translation

PurposeAudio-visual translation (AVT) is recognized as the most vibrant type of translation. While AVT plays a vital function in the field of translation, its significance within cultural studies hasn’t been thoroughly investigated. This research aims to uncover the predominant techniques employed in translating idiomatic expressions found in subtitled movies from English to Arabic.Design/methodology/approachThe corpus utilized in the current study consisted of five movies. The dialog in the chosen English films was examined to identify idiomatic expressions. The occurrence and proportion of each approach employed in translating English idioms into Arabic were calculated.FindingsThe findings of this investigation unveiled that the most common technique employed by translators to render idioms was retaining both similar meaning and similar form. The second approach involved maintaining a similar meaning while altering the form. The third method involved paraphrasing for translation. The fourth strategy was compensation-based translation. When dealing with verb and object idioms as well as similes, the predominant techniques were retaining both similar meaning and similar form, followed by a similar meaning but different form approach. Compound idioms were translated using paraphrasing, similar meaning but different form and similar meaning but different form strategies.Originality/valueThe research was constrained to examining the utilization of subtitles for the translation of five films. The films and corresponding Arabic subtitles were obtained and saved in plain text formats. Solely idiomatic expressions were scrutinized to determine the translation approaches employed through the use of subtitles.

Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this paper, a comprehensive study is reported regarding day-ahead electricity load forecasting. For this purpose, three sequence-to-sequence (Seq2seq) deep learning (DL) models are used, namely the multilayer perceptron (MLP), the convolutional neural network (CNN) and the ensemble learning model (ELM), which consists of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL, emphasizing the way the datasets are trained and fine-tuned for higher forecasting accuracy. In order to implement the forecasting strategies using deep learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1–24 h) for each month of the year for the Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. Four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only the Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for the Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) involves the training of the DL models using the Lesvos dataset, and the testing period is performed directly on the Chios dataset without fine-tuning. The fourth strategy is a multi-task deep learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. The results obtained demonstrate that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, DTL Case 1 and 2 outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it is concluded that the MLP model presents the best monthly forecasts with MAPE values of 6.24% and 6.01% for the first two cases, the CNN model presents the best monthly forecasts with MAPE values of 5.57% and 5.60%, respectively, and the ELM model achieves the best monthly forecasts with MAPE values of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

Fostering community participation in sustainable municipal solid waste management at multiple scales in Tehran, Iran

This article delves into the intricacies of waste management in Tehran, offering insights into the structured framework governing the control of solid waste materials. As collected, the average daily generation of municipal solid waste (MSW) in the region is 0.645 kg per inhabitant. Unexpectedly, the impact of socio-economic factors on processes such as garbage sorting, creating products from recyclable waste, and composting was shown to be comparatively weaker than that of supporting elements. The initial approach entails providing environmental cadres and the community with more comprehensive MSW reduction training, which will cultivate a nuanced comprehension of waste management. The second strategy focuses on amplifying information dissemination through mass media and targeted campaigns, emphasizing effective MSW practices. The objective of the third endeavor is to augment the quantity of environmental cadres, which will be comprised of community and local leaders, in order to inspire and direct endeavors to reduce MSW. The fourth and final strategy underscores the expansion and diversification of waste storages, positioning them as customer-centric institutions dedicated to environmental campaigns and training. Ultimately, the collaborative implementation of these strategies is poised to instigate a positive shift towards heightened community participation in MSW reduction practices.

Mixing approach for 3D printable concrete: method of optimisation of superplasticiser dosage

This study is a systematic investigation of superplasticiser (SP) addition methods and optimum SP dosage with various water-to-binder ratios (w/b) for mixing three-dimensional printable concrete. In this study, five distinct strategies were adopted to arrive at the optimum method of addition. In the first strategy, a random mixing approach was applied to identify, through visual observation, methods for adding SP with varying time patterns. Secondly, a constant time pattern was adopted from the random approach for mixing and the optimal SP dosage was determined. In the third strategy, different methods of adding the optimum SP dosage were examined. In the fourth strategy, the batching effect of the material with the optimum SP dosage was assessed. Finally, the printable regions with different values of w/b and SP dosages were correlated. It was observed that the optimum SP dosage, in the range of 0.10–0.21% of binder, with w/b = 0.21–0.25 achieved adequate printability parameters by full addition of SP following a constant wet mixing time as the optimal application. However, after applying the optimum SP dosage, a supplemental SP dosage is required at appropriate intervals to retain the workability for higher batches.

Russian industrial companies under the “second wave” of sanctions: Response strategies

The article presents initial findings of a research project aimed at analyzing the impact of the “second wave” of sanctions in 2022 on the behavior of Russian companies, assesses their actions to adapt to the new conditions. This paper is based on survey data collected from managers of 1860 Russian manufacturing companies between August and November 2022. Despite the severity and scope of the sanctions, one third of the companies did not take adaptation measures, particularly local, technologically backward firms without innovative activity. On the whole, firms integrated into the global economy reacted more actively to new threats than those confined to the domestic market. We found significant variability in companies’ responses to the sanctions shock. Four basic quasistrategies of firms’ response are identified. The first one involved reducing current activities, employment, and investments. These actions were most common in industries integrated into global value chains, notably automotive industry and wood processing. The second strategy included simplifying products to reduce dependence on imports, finding new markets, and changing investment directions — typically seen in pharmaceutical firms. The third one consisted of altering supply channels for materials and components, and it was frequently adopted by manufacturers of rubber and plastic products. The fourth strategy focused on digitalization, developing new products, and increasing interaction with authorities. This was characteristic of industries whose markets have carved out niches due to the exits of major foreign players, in particular, manufacture of furniture. We highlight the following recommendations for the state anti-crisis policy: In 2022, the relative success of companies’ adaptation was associated with the implementation of “easy” import substitution. However, in the future there will be a need to prioritize support for the development of domestic technologies and increased access to knowledge. In conditions of high uncertainty, it is crucial to assist companies in building their own value chains, and predictability of government actions appears to be a significant factor in enhancing private initiative.

Design optimization and power management of a fuel cell-battery fixed-wing electric UAV

This study aims for endurance improvement and energy saving by addressing, on the one hand, the design optimization (DO) of a hybrid electric source (HES) consisting of a fuel cell (FC) and a battery, intended for a fixed-wing electric UAV (EUAV) and, on the other hand, the power management (PM) of the sources. The DO employed the particle swarm optimization (PSO) algorithm, which determines the FC power and the battery capacity that offer the sources the least weight; the findings resulted in a weight reduction of 8%; this gain saves a total energy of 90.28 kJ that can extend the “endurance stage” by more than 26 min. Three power management strategies (PMS) are examined: frequency separation (FS), which gives good performance in PM and protects the FC from dehydration by smoothing its response; power limitation (PL) which protects the FC from exceeding its maximum power, and fuzzy logic (FL), which allows for more flexible power distribution between the sources and has the main advantage of controlling the battery state of charge (SOC). A fourth strategy was developed by combining the benefits of these three strategies. This hybrid PMS (HPMS) saves 2% of FC energy compared to the FL strategy. FS and PL save, respectively, 41% and 44% of FC energy, but the SOC can drop significantly, especially for longer missions. The experimental findings, achieved using a laboratory test bench, validate the simulated results obtained from MATLAB/Simulink software, confirming the validity and performance of the proposed HPMS.

Comparison of different inversion strategies for electrical impedance tomography (EIT) measurements

SUMMARY Electrical impedance tomography (EIT) is a promising method to image the frequency-dependent complex electrical conductivity distribution of the subsurface in the mHz to kHz frequency range. In contrast to the well-developed electrical resistivity tomography (ERT) method, the inversion approach for EIT data is less established. Different inversion strategies have been proposed, but the implications of the differences between these methods have not been investigated yet. In this study, we aim to compare four different inversion strategies for EIT measurements. The first strategy (CVI) formulates the inverse problem in the complex number domain and is mathematically the most elegant method. The second strategy (RVI) is the established real-valued inversion method, which decouples the inversion of the real and imaginary parts and completely ignores the complex nature. The third strategy (ALT) is very similar to the RVI strategy in case of small phase angles, but it considers the complex coupling in the forward operator and alternately updates the real and imaginary parts of the model in the case of large phase angles. The fourth and final strategy (CVI+) was newly formulated in this study. It fully considers the complex nature of EIT measurements but separates the treatment of the real and imaginary part in terms of the data weighting and regularization. The different inversion strategies were tested with two synthetic models. The first model has a small phase contrast and the second model has a large phase contrast. In the case of a small phase contrast, the CVI strategy was able to resolve the distribution of electrical conductivity amplitude, but the inversion result for the phase angle was less reliable. The other three strategies presented similar results and the models were well resolved within the expected data misfit. In the case of a model with large phase contrast, only the newly formulated CVI + strategy was able to produce reliable results. It was found that the extremely large phase angle can have a significant influence on the modelled amplitude of data. The cross-sensitivity (i.e. the imaginary part of the sensitivity) that describes the influence on the real part of data due to a change in the imaginary part of model, or that on the imaginary part of data due to a change in the real part of model, provided unique information during the inversion. It was concluded that the CVI + strategy is theoretically the most comprehensive and correct approach for EIT inversion, but that in the case of small phase angles the RVI strategy has the practical advantage that no complex calculations are required, which substantially reduces the required computational effort.

Vehicle power supply management strategies

Introduction. To optimize the performance of the vehicle electrical system, it is necessary to improve the control principles and strategies. The control principles may be based on voltage constancy, ensuring battery charge, reducing fuel consumption, etc.Materials and methods. A distinctive feature of the first level control strategy is the absence of additional sensors in the power supply system. Their occurence led to the emergence of the second level control strategy aimed at ensuring the starter battery charge, which is achieved by organizing the interaction between the electronic engine control unit and the generator. In the control strategy of the third level the first place is given to the reduction of the vehicle fuel consumption. The target functions of the listed control strategies are considered.Results. The fourth level management strategy, the purpose of which is to reduce the costs of power supply system operation at all stages of the life cycle, is proposed. The structure of power supply system operation costs is considered, which includes the costs of fuel overconsumption caused by malfunctions in the operation of units, as well as the costs of maintenance, diagnostics, repair (replacement) and utilization.Discussion and conclusions. Practical application of the proposed strategy will make it possible to take into account all types of costs and minimize the cost of operating the power supply system for both vehicles with internal combustion engines and hybrid and electric vehicles, in which electricity is even more important.

Participatory Mathematical Modeling Approach for Policymaking during the First Year of the COVID-19 Crisis, Jordan.

We engaged in a participatory modeling approach with health sector stakeholders in Jordan to support government decision-making regarding implementing public health measures to mitigate COVID-19 disease burden. We considered the effect of 4 physical distancing strategies on reducing COVID-19 transmission and mortality in Jordan during March 2020-January 2021: no physical distancing; intermittent physical distancing where all but essential services are closed once a week; intermittent physical distancing where all but essential services are closed twice a week; and a permanent physical distancing intervention. Modeling showed that the fourth strategy would be most effective in reducing cases and deaths; however, this approach was only marginally beneficial to reducing COVID-19 disease compared with an intermittently enforced physical distancing intervention. Scenario-based model influenced policy-making and the evolution of the pandemic in Jordan confirmed the forecasting provided by the modeling exercise and helped confirm the effectiveness of the policy adopted by the government of Jordan.

Strategies on Learning Speaking used by International Accounting Students

This research aimed to investigate the strategies that are used by the international accounting students at universitas Tanjungpura and to explain the most dominant learning speaking strategies used by international accounting students in learning speaking. The total numbers of participants in this research are 34 students. This research used a descriptive quantitative design, and the data were collected from questionnaires. The findings of this study revealed that international accounting students in the universitas Tanjungpura class used nearly all of the strategies for learning English. The highest average was Metacognitive strategy, with 52,0% total average. The second strategy is Cognitive strategies with the average 44,1%; the third strategy was effective strategy with the average 38,3%, the fourth strategy was the social strategy with the average 38,2%, fifth strategy was compensation with the average 33,8%, and the last strategy was memory strategy with average 11,0%.

Proposed Marketing Strategy to Increase the Awareness of Bulan Imunisasi Anak Nasional (BIAN) in Bandung City

Bulan Imunisasi Anak Nasional or BIAN is an Health Ministry of Republic Indonesia program that aims to enhance the children’s health by immunize them with a mass Complete Immunization and Follow-Up Immunization in Indonesia especially for the children that did not have immunize yet since the pandemic in 2020. Bandung City which is included in West Java Province has the lowest coverage of BIAN Immunization among the other City/Districts in West Java. This research is aimed to figure out what factors affected the low coverage of BIAN Immunization in Bandung City and analyze the situation regarding the field report in the implementation phase and how to solve it by applying better strategies in upcoming BIAN events. The analysis that was conducted are External and Internal Analysis to find what factors that related to the issue. In the External Analysis, the author uses Customer Analysis, Stakeholder Analysis, and PESTEL Analysis to indicate any external factors that affected the low coverage of BIAN Immunization. In the Internal Analysis, the author uses Tangible Analysis, Intangible Analysis, and Interview Data from stakeholders that hold the event. There are several findings in External and Internal Analysis especially in Advertisement and Public Relation became the strong factor that affected the main issue because of low awareness of the target audience. From several analyses and the findings, there are four proposed strategies to increase the awareness of the target audience to participate in the event to enhance the coverage of the immunization in Bandung City. The first one is improvement in Public Relation strategy that needs to be more organized and comprehensive especially with external parties. The second strategy is improvement in Advertisement strategy, especially in social media activation and Advocacy Advertisement. The third strategy is collaboration with brands that have a strong family image in the health sector. The fourth strategy is initiation in having Key Opinion (KOL) as the face of BIAN in Bandung City. In academic recommendation, the next author should consider the current situation of the target audience especially in their awareness towards the event and the current coverage of the immunization.

CHARACTER EDUCATION BASED ON LOCAL WISDOM SIRI™ NA PACCE IN SOCIOLOGY LEARNING

Technological developments have opened up the opportunity for every mixed culture, with negative and positive effects. One negative impact is that students tend to imitate foreign cultures and forget their culture's local wisdom values. Thus, the study intended to see the concept of integrating character education in siri™ na pacce™s local wisdom through sociology. This type of research is a library study that uses books and other literature as primary objects. Studies show how integrating siri™ na pacce™s value in sociology can be done through four strategies. First, exemplary strategy. The teacher, as the subject, gives an example of any action, behavior, or words that indicate siri™ na pacce's attitude. The second strategy is to intervene by integrating values-based character education siri™ na pacce in sociological studies. The third strategy is consistent breeding. After an exemplary and intervention strategy has been implemented, a strategy is needed to keep what has been implanted into a habit or habit. The fourth strategy is reinforcement. The strategy is basically done along with the breeding strategy. This integration can be an attempt to introduce and pass on local culture to the student, as well as good character creation.

Analysis of Histopathological Images for Early Diagnosis of Oral Squamous Cell Carcinoma by Hybrid Systems Based on CNN Fusion Features

Oral squamous cell carcinoma (OSCC) is one of the deadliest and most common types of cancer. The incidence of OSCC is increasing annually, which requires early diagnosis to receive appropriate treatment. The biopsy technique is one of the most important techniques for analyzing samples, but it takes a long time to get results. Manual diagnosis is still subject to errors and differences in doctors’ opinions, especially in the early stages. Thus, automated techniques can help doctors and patients to receive appropriate treatment. This study developed several hybrid models based on the fused CNN features for diagnosing OSCC-100x and OSCC-400x datasets for oral cancer, which have the ability to analyze medical images with a high level of precision and accuracy. They can detect subtle patterns, abnormalities, or indicators of diseases that may be difficult to recognize with the naked eye. The systems have the potential to significantly reduce human error and provide more consistent and reliable results, resulting in improved diagnostic accuracy. The systems also have the potential for early detection of OSCC for treatment success and improved patient outcomes. By detecting diseases at an early stage, clinicians can initiate interventions in a timely manner, potentially preventing OSCC progression and improving the chances of successful treatment. The first strategy was based on GoogLeNet, ResNet101, and VGG16 models pretrained, which did not achieve satisfactory results. The second strategy was based on GoogLeNet, ResNet101, and VGG16 models based on the adaptive region growing (ARG) segmentation algorithm. The third strategy is based on a mixed technique between GoogLeNet, ResNet101, and VGG16 models and ANN and XGBoost networks based on the ARG hashing algorithm. The fourth strategy for oral cancer diagnosis by ANN and XGBoost is based on features fused between CNN models. The ANN with fusion features of GoogLeNet-ResNet101-VGG16 yielded an AUC of 98.85%, accuracy of 99.3%, sensitivity of 98.2%, precision of 99.5%, and specificity of 98.35%.

Coconut farming development strategy in Bireuen Regency using hierarchy process analysis

Coconut is one of the main plantation commodities cultivated on small-scale plantations. Bireuen Regency is a coconut-producing area in Aceh Province. Efforts to develop coconut farming continue to be carried out by stakeholders. This study aims to determine alternative strategies for developing coconut farming in Bireuen Regency. The data collection method used purposive sampling with a sample of coconut stakeholders consisting of elements from the government, extension workers, academics, farmer groups, traders, and industry players. The analysis model of this research uses the Analytical Hierarchy Process with the help of the Expert Choice application. The results of this study indicate that alternative strategies in developing coconut farming follow the highest - lowest weighting results obtained: first, increasing coconut production and quality (0.293); the second priority is partnership arrangements with traders and industry (0.257); the third strategy is developing farmer institutions (0.231) and, the fourth strategy for expanding access to capital (0.220).

Strategies for Multidomain Sequence Analysis in Social Research

Multidomain/multichannel sequence analysis has become widely used in social science research to uncover the underlying relationships between two or more observed trajectories in parallel. For example, life-course researchers use multidomain sequence analysis to study the parallel unfolding of multiple life-course domains. In this article, the authors conduct a critical review of the approaches most used in multidomain sequence analysis. The parallel unfolding of trajectories in multiple domains is typically analyzed by building a joint multidomain typology and by examining how domain-specific sequence patterns combine with one another within the multidomain groups. The authors identify four strategies to construct the joint multidomain typology: proceeding independently of domain costs and distances between domain sequences, deriving multidomain costs from domain costs, deriving distances between multidomain sequences from within-domain distances, and combining typologies constructed for each domain. The second and third strategies are prevalent in the literature and typically proceed additively. The authors show that these additive procedures assume between-domain independence, and they make explicit the constraints these procedures impose on between-multidomain costs and distances. Regarding the fourth strategy, the authors propose a merging algorithm to avoid scarce combined types. As regards the first strategy, the authors demonstrate, with a real example based on data from the Swiss Household Panel, that using edit distances with data-driven costs at the multidomain level (i.e., independent of domain costs) remains easily manageable with more than 200 different multidomain combined states. In addition, the authors introduce strategies to enhance visualization by types and domains.

Introducing a four-fold way to conceptualize artificial agency

Recent developments in AI-research suggest that an AI-driven science might not be that far off. The research of for Melnikov et al. (2018) and that of Evans et al. (2018) show that automated systems can already have a distinctive role in the design of experiments and in directing future research. Common practice in many of the papers devoted to the automation of basic research is to refer to these automated systems as ‘agents’. What is this attribution of agency based on and to what extent is this an important notion in the broader context of an AI-driven science? In an attempt to answer these questions, this paper proposes a new methodological framework, introduced as the Four-Fold Framework, that can be used to conceptualize artificial agency in basic research. It consists of four modeling strategies, three of which were already identified and used by Sarkia (2021) to conceptualize ‘intentional agency’. The novelty of the framework is the inclusion of a fourth strategy, introduced as conceptual modeling, that adds a semantic dimension to the overall conceptualization. The strategy connects to the other strategies by modeling both the actual use of ‘artificial agency’ in basic research as well as what is meant by it in each of the other three strategies. This enables researchers to bridge the gap between theory and practice by comparing the meaning of artificial agency in both an academic as well as in a practical context.

The Strategy of BPJS Ketenagakerjaan Banda Aceh Branch in the Socialization of the Non-wage Recipient Program

This study aims to discuss the strategies implemented by BPJS Ketenagakerjaan Banda Aceh Branch Office in disseminating the Non-Wage Recipient program and to find out the obstacles of this program. The theory used in this paper is the promotion mix model which includes: sales promotion, personal selling, direct marketing, advertising, and public relations. This study uses qualitative analysis methods. This paper uses certain data collection techniques such as an interview with respondents and documentation supporting the research.The results showed that the strategy BPJS Ketenagakerjaan Banda Aceh Branch used in socializing the Non-Wage Recipient Program or BPU program was by advertising or promoting the benefits of the BPJS Ketenagakerjaan program for informal workers in various media. The second strategy is personal selling. The third strategy is public relations. The fourth strategy is direct marketing through the Market. The last strategy is sales promotion through socialization in informal worker forums. There are four obstacles faced by BPJS Ketenagakerjaan Banda Aceh Branch in conducting socialization this program, it is the public's view about BPJS is not an Islamic product, competition with commercial insurance, lack of firmness from the government.