Historical Decisions Research Articles

Breast radiotherapy planning: A decision-making framework using deep learning.

Effective breast cancer treatment planning requires balancing tumor control while minimizing radiation exposure to healthy tissues. Choosing between intensity-modulated radiation therapy (IMRT) and three-dimensional conformal radiation therapy (3D-CRT) remains pivotal, influenced by patient anatomy and dosimetric constraints. This study aims to develop a decision-making framework utilizing deep learning to predict dose distributions, aiding in the selection of optimal treatmenttechniques. A 2D U-Net convolutional neural network (CNN) model was used to predict dose distribution maps and dose-volume histogram (DVH) metrics for breast cancer patients undergoing IMRT and 3D-CRT. The model was trained and fine-tuned using retrospective datasets from two medical centers, accounting for variations in CT systems, dosimetric protocols, and clinical practices, over 346 patients. An additional 30 consecutive patients were selected for external validation, where both 3D-CRT and IMRT plans were manually created. To show the potential of the approach, an independent medical physicist evaluated both dosimetric plans and selected the most appropriate one based on applicable clinical criteria. Confusion matrices were used to compare the decisions of the independent observer with the historical decision and the proposed decision-makingframework. Evaluation metrics, including dice similarity coefficients (DSC) and DVH analyses, demonstrated high concordance between predicted and clinical dose distribution for both IMRT and 3D-CRT techniques, especially for organs at risk (OARs). The decision-making framework demonstrated high accuracy (90 ), recall (95.7 ), and precision (91.7 ) when compared to independent clinical evaluations, while the historical decision-making had lower accuracy (50 ), recall (47.8 ), and precision (78.6 ). The proposed decision-making model accurately predicts dose distributions for both 3D-CRT and IMRT, ensuring reliable OAR dose estimation. This decision-making framework significantly outperforms historical decision-making, demonstrating higher accuracy, recall, and precision.

An Analysis of Policy Frameworks on Business Formulization and Disaster Management to Mitigate Flood Loss and Damage Among Informal Businesses in Sri Lanka

The 27th Conference of the Parties to the United Nations Framework Convention on Climate Change in Egypt made a historical decision on disaster loss and damage for vulnerable countries. Even though parties agreed to salvage vulnerable nations with generous support, a fundamental question remains as to the extent to which these vulnerable countries can accurately identify the most vulnerable communities/areas. In Sri Lanka, informal businesses are particularly vulnerable to floods, but their informal status has limited the extent to which they could receive flood protection, including disaster-resilient infrastructure developments. The main objective of this paper, therefore, is to identify challenges that informal businesses have experienced in dealing with flood disaster risks. In doing so, we analyzed government policies and laws that are related to business formalization and disaster management. We also conducted interviews with key informants to verify our data. Our analysis found that the Sri Lankan government requires informal business owners to follow complicated rules to register their businesses. For these owners, who are not highly educated, these processes and fear of high tax rates discouraged registering their businesses. The central government tends to prioritize flood mitigation actions for formalized business areas. Informal businesses are not usually covered by flood insurance and compensation. In conclusion, we emphasize the need to establish widely available legal and administrative support for informal businesses to register. Adopting business continuity plans (BCPs) and keeping standardized business records also help businesses minimize flood loss and damage.

Knowledge-Assisted Actor Critic Proximal Policy Optimization-Based Service Function Chain Reconfiguration Algorithm for 6G IoT Scenario.

Future 6G networks will inherit and develop Network Function Virtualization (NFV) architecture. With the NFV-enabled network architecture, it becomes possible to establish different virtual networks within the same infrastructure, create different Virtual Network Functions (VNFs) in different virtual networks, and form Service Function Chains (SFCs) that meet different service requirements through the orderly combination of VNFs. These SFCs can be deployed to physical entities as needed to provide network functions that support different services. To meet the highly dynamic service requirements in the future 6G Internet of Things (IoT) scenario, the highly flexible and efficient SFC reconfiguration algorithm is the key research direction. Deep-learning-based algorithms have shown their advantages in solving this type of dynamic optimization problem. Considering that the efficiency of the traditional Actor Critic (AC) algorithm is limited, the policy does not directly participate in the value function update. In this paper, we use the Proximal Policy Optimization (PPO) clip function to restrict the difference between the new policy and the old policy, to ensure the stability of the updating process. We combine PPO with AC, and further bring the historical decision information as the network knowledge to offer better initial policies, to accelerate the training speed. We also propose the Knowledge = Assisted Actor Critic Proximal Policy Optimization (KA-ACPPO)-based SFC reconfiguration algorithm to ensure the Quality of Service (QoS) of end-to-end services. Simulation results show that the proposed KA-ACPPO algorithm can effectively reduce computing cost and power consumption.

NPCC4: Advancing climate justice in climate adaptation strategies for New York City.

The Advancing Climate Justice in Climate Adaptation Strategies for New York City (Equity) chapter of NPCC4 builds on the findings and recommendations from NPCC3 to identify additional metrics and adaptation efforts that can advance climate justice. First, the chapter assesses the efforts of the City to incorporate equity into climate adaptation efforts since NPCC3 and describes how the communities profiled in NPCC3 have implemented and evolved their approaches to addressing the intersecting climate, environmental, and social stressors that they continue to face. Second, it adds to the historical context of climate inequity by linking the bioregion's history of colonization, land dispossession, and slavery building on emerging evidence demonstrating how historical and contemporary land use patterns and decisions shape present and future climate risks and social vulnerability, including climate displacement. Third, it recommends a NYC-focused metric to identify areas of the city that are most vulnerable to the intersection of climate hazards, social vulnerability, and displacement. Finally, it highlights approaches to more equitable and just climate adaptation drawn from local, national, and international examples. As such, the chapter offers best practices that prioritize community-driven climate resilience approaches that are integrated, more equitable, and racially just.

Learning-based dynamic pricing strategy with pay-per-chapter mode for online publisher with case study of COL

We consider how to make dynamic pricing decision for Chinese Online (COL) at T time-points, an online publisher that allow authors to sell their ongoing book projects. Instead of paying for a book, readers pay for each chapter (pay-per-chapter mode) of the ongoing book project. This mode allows readers to pay for as many chapters as they want without taking the risk that the releasing of new chapters might be delayed or stopped. Despite of the dynamics of chapter-by-chapter released of COL products, the fixed pricing strategy (FPS) does not make fully use of the reading data generated by releasing chapters of the ongoing book. We propose a learning-based dynamic pricing strategy (LDPS) that exploits the newly information to maximize cumulative revenue for the publisher. The LDPS captures the ever changing features of readers. It employs the Thompson sampling method to balance the exploration of investigating different prices sufficiently with the exploitation of settling on the optimal price. Taking COL as a case study and implementing our strategy in the context of the aforementioned real-life data set, we show that LDPS outperform several classical strategies such as Greedy, Prior-Free TS and Prior-Given TS, and average revenue of LDPS is increased by 0.5 % average per time-point compared to the publisher's historical decisions. We also provide some management implications for the COL publisher by analyzing the pricing range of different genres of books and the choice of the exploration threshold parameter.

Navigating Reversed Valuation Dilemmas: SME Enterprises Nationalized in 1950s Poland - A Case Study

The publication describes a problem that often arises in court cases regarding the valuation of SME forcibly nationalized in the 1950s. Valuation of compensation to previous SME owners is an ongoing court debate in Poland. The authors proposed an approach that took into account the economic conditions at that time and complied with the principle of paying compensation for the actual losses incurred, and not for the potential losses that could be incurred as a result. At the same time, the publication analysed other similar judgments in cases regarding compensation for nationalized enterprises. The practical and scientific value of the publication lies in the fact that it addresses an existing practical problem and its solution. Research Design & Methods: the paper presents a conceptual proposal of the SME valuation method in unique circumstances, complemented by a detailed case study illustrating the application of this valuation approach. Findings: When calculating the compensation for nationalization or other forms of expropriation courts use various methods of valuing net assets (fair market value, book value, recreation value) plus separately determine the value of intangibles, such as licenses, based on the discounted average profits from such assets for a reasonable period of time. If indexation was employed (for historical cost valuation), it used either average interest rates in the economy, the cost of capital of a company, or the cost of lost opportunities for a particular company. Using discounted cash flow valuation for the entire business was uncommon and even if it was included in the bilateral investment treaties (which were meant to protect investors from expropriation) it was considered by the tribunals as speculative and unreliable. Our approach proposes net asset value approach with the use of inflation or average salaries for indexation (to calculate current values of assets) and a combined approach of historical values if they were known at the moment of nationalization and modern estimates of values of remaining assets (that belonged to the company but their value was not provided in the original documentation). Implications & Recommendations: The analysis of historical compensation decisions and bilateral investment treaties suggests that the most popular approach to valuing a loss from expropriation is the fair market value approach and net assets approach with a separate valuation of significant intangible assets provided that they gave certainty of profits for the investor. Contribution & Value Added: The practical and scientific value of the publication lies in the fact that it presents an existing practical problem of court calculation of compensations to owners of forcibly nationalized SMEs in Poland and its solution together with methodological framework that can be used in the future

MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing

With the increasing popularity of the Internet of Things (IoT), the proliferation of computation-intensive and timing-dependent applications has brought serious load pressure on terrestrial networks. In order to solve the problem of computing resource conflict and long response delay caused by concurrent application service applications from multiple users, this paper proposes an improved edge computing timing-dependent, task-offloading scheme based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) that aims to shorten the offloading delay and improve the resource utilization rate by means of resource prediction and collaboration among multiple agents to shorten the offloading delay and improve the resource utilization. First, to coordinate the global computing resource, the gated recurrent unit is utilized, which predicts the next computing resource requirements of the timing-dependent tasks according to historical information. Second, the predicted information, the historical offloading decisions and the current state are used as inputs, and the training process of the reinforcement learning algorithm is improved to propose a task-offloading algorithm based on MADDPG. The simulation results show that the algorithm reduces the response latency by 6.7% and improves the resource utilization by 30.6% compared with the suboptimal benchmark algorithm, and it reduces nearly 500 training rounds during the learning process, which effectively improves the timeliness of the offloading strategy.

The future of sustainable farming: An evolutionary game framework for the promotion of agricultural green production technologies

In the context of global climate change and increasing population, transitioning to resource-efficient, environmentally friendly agricultural technologies is essential for sustainability. In this context, the present study is anchored in the theories of information asymmetry and bounded rationality. It constructs a tripartite evolutionary game model under market mechanisms and government guidance, facing the challenges of promoting green agricultural technologies among multiple actors. This model explores the strategic choices and dynamic evolution of these actors under economic incentives and risk perception. The objective of the research is to reveal the dynamic evolutionary trends of stakeholder behavior under different policy intervention conditions and how governments can propel the promotion of green agricultural production technologies through policy instruments. To encompass the impact of historical decisions on the evolutionary process of green technology dissemination, this study employs a Delay Differential Equations (DDE) algorithm that considers historical time lags in its numerical simulations. Through stability analysis and DDE numerical simulations, the research identifies factors influencing the promotion of green agricultural production technologies. The findings indicate that under market mechanisms, there is a risk of “market failure” in the dissemination model of green agricultural technologies. Government participation can provide external incentive support, effectively enhancing the stability and efficiency of the evolutionary game system for promoting green technologies. Simulation results further suggest that government intervention can significantly improve the stability and efficiency of technology promotion. Economies of scale, initial willingness, risk perception, and subsidy intensity are decisive factors in technology adoption. Consequently, policymakers are advised to formulate precise and multifaceted subsidy strategies based on the varying needs of different actors, to stimulate engagement, reduce risk, and encourage widespread adoption of green technologies. This study lays a theoretical groundwork for smallholders and family farms to overcome technology adoption barriers, crucial for sustainable agricultural development. It provides a framework for understanding and addressing these challenges, guiding policymakers in crafting effective green agriculture policies to improve farming efficiency and sustainability.

Historical explanation of teleology and axiology of the Russian Procuratorate

Considering the historical and legal prerequisites for the creation of the first independent supervisory institutions in Russia (the fiscal office and the procuratorate), today legal scholars accord their attention mainly to the need to monitor the implementation of written legislation arose in the XVIII century. At the same time, an equally important factor is often overlooked, which seems as made a considerable contribution to the historical decision on what exactly this supervisory institution should be. This article reveals the socio-legal context of the creation of the Russian procurator supervision. Historical and legal researches and archival documents as well as specific examples from the criminal chronicle demonstrate that there were systemic problems, such as open disrespect for the law, abuse of power and the predominance of personal predilections over public interest. These problems had influenced the decision of Peter the Great no less than the fact of the rapid development of formal legislation. The absence of an authority or official in the Russian state to whom ordinary man could turn for help and protection (including the absence of an independent court) even exacerbated these problems and urged the creation of Procurator-General’s office.

Deconstructing the Laissez-Faire Legend: A Critical Analysis of the 1834 Poor Law Reform and Its Legacy

This research paper critically examines the 1834 Poor Law Reform in Britain, a seminal event that is often heralded as a triumph of laissez-faire economics. The study delves into the historical context of the Old Poor Law system, established in 1601, which mandated legal support for individuals unable to sustain themselves. The paper scrutinizes the perceived problems of the Old Poor Law and the subsequent radical reforms introduced by the New Poor Law. It challenges the conventional narrative that the Poor Law Reform was a necessary corrective to an inefficient and market-distorting welfare system. Through a detailed analysis of the Poor Law Commission Report of 1834 and its criticisms, the paper reveals significant gaps in the arguments presented. The study also considers the impact of the Industrial Revolution, the Napoleonic Wars, and the gold standard restoration on the Old Poor Law system, arguing that these external factors played a crucial role in shaping the economic and social landscape of the time. This study offers insights into the complexities of policy marking and the interplay of economic theories and social needs. It underscores the enduring impact of historical decisions on contemporary economic and political dynamics and challenges the notion of post-WWII liberal order as a perpetuation of the laissez-faire myth.

Assessing Path Dependency in Vietnam's Healthcare Legal Framework: Exploring Public-Private Collaboration in Ho Chi Minh City during the COVID-19 Crisis.

The COVID-19 pandemic prompted a nudge for public-private cooperation in healthcare to rapidly cope with limited resource. However, Vietnam's historical reliance on a public healthcare system, combined with a traditional emphasis on socialization in the Polanyian sense, hindered the swift integration of the private sector. This research investigates path dependency in Vietnam's public health sector, using theories including path dependency, Karl Polanyi's double movement with legal analysis method to analyze the interplay of historical decisions, and socialist policies in healthcare. Recognizing these institutional and market governance flaws, a deeper understanding of the role of law becomes vital in crafting strategies for a more resilient and sustainable healthcare system in Vietnam. In this article, Ho Chi Minh City is chosen as a case study due to its notable mix of public-private healthcare facilities and its status as a leading area in infection cases during the third COVID-19 wave. The article suggests that there should be a change in the way public and private sectors work together, one that is more in line with market-driven solutions which requires solid legal framework for cooperation beyond merely mobilizing resources.

Creating synthetic populations in transplantation: A Bayesian approach enabling simulation without registry re-sampling.

Computer simulation has played a pivotal role in analyzing alternative organ allocation strategies in transplantation. The current approach to producing cohorts of organ donors and candidates for individual-level simulation requires directly re-sampling retrospective data from a transplant registry. This historical data may reflect outmoded policies and practices as well as systemic inequities in candidate listing, limiting contemporary applicability of simulation results. We describe the development of an alternative approach for generating synthetic donors and candidates using hierarchical Bayesian network probability models. We developed two Bayesian networks to model dependencies among 10 donor and 36 candidate characteristics relevant to waitlist survival, donor-candidate matching, and post-transplant survival. We estimated parameters for each model using Scientific Registry of Transplant Recipients (SRTR) data. For 100 donor and 100 candidate synthetic populations generated, proportions for each categorical donor or candidate attribute, respectively, fell within one percentage point of observed values; the interquartile ranges (IQRs) of each continuous variable contained the corresponding SRTR observed median. Comparisons of synthetic to observed stratified distributions demonstrated the ability of the method to capture complex joint variability among multiple characteristics. We also demonstrated how changing two upstream population parameters can exert cascading effects on multiple relevant clinical variables in a synthetic population. Generating synthetic donor and candidate populations in transplant simulation may help overcome critical limitations related to the re-sampling of historical data, allowing developers and decision makers to customize the parameters of these populations to reflect realistic or hypothetical future states.

SVD-based adaptive fuzzy for generalized transportation

This research stems from the urgent need to address the complex challenges faced in multimodal transportation within the cargo sector, particularly concerning the reduction of transportation costs and CO2 emissions. Existing transportation systems often struggle to efficiently balance economic and environmental concerns, necessitating innovative solutions. In this paper, we have introduced a novel mathematical model that integrates Singular Value Decomposition (SVD), Genetic Algorithms (GA), and the Adaptive Neural Fuzzy Inference System (ANFIS). The synergy of GA and the ANFIS forms a dynamic learning system that continually refines solutions by adapting to historical decisions and aligning with supply-demand dynamics. This integrated approach aims to optimize cost allocation and resource utilization while simultaneously minimizing CO2 emissions. Deploying the ANFIS+GA+SVD model in multimodal scenarios yields impressive results, with a minimal average percentage error (MAPE) of 0.28% for transportation costs and a remarkable coefficient of determination R2 of 0.992 for the Truck + Ship scenario. Similarly, a MAPE of 0.11% for CO2 emissions, supported by an impressive R2 of 0.989, underscores the model's accuracy. Observations of people's transportation choices in response to emission price changes reveal a significant decrease in truck usage, plummeting from 84% to below 50%. Alternative transportation methods, especially in the 3S-3 M-3 C scenario, show a substantial surge, with railways leading at a remarkable 30% increase in utilization. The combined quantity and cost comparison, utilizing the ANFIS+GA+SVD model, emphasizes the suitability of the Truck + Ship scenario for Iraq's transportation network, reinforcing the case for sustainable and efficient multimodal transportation systems.

Digitizing the USPTO patent backfile

Abstract The digitization of the US Patent and Trademark Office’s (USPTO) backfile of six million patents undertaken between 1951 and 2001 was a five-decade struggle, featuring several media transitions from print and microfilm to CD-ROMs and, finally, the Web. This mass digitization project is on a similar scale to Google Books and the Internet Archive, but it is rarely discussed within critical digitization scholarship or for its significance as a tool for knowledge production. In this article, I focus on the USPTO’s patent document’s digital and physical material form and how the current paradigm of access and storage of the digital backfile emerged. Through this case study, I build upon Ian Milligan’s distinction between the ‘text’ and ‘platform’ layers of a digitization project to demonstrate how historical decisions regarding format and metadata continue to influence how users retrieve and interpret documents, such as patents, online.

Energy policy of the European Union: Impact of the renewable energies and perspectives for the year 2020

In order to fight against the climatic change the European Union (EU) adopted at the beginning of 20071 a historical decision: The 27 countries members of the European Union (EU) will have to cover, with obligatory character, a 20 % of their power consumption with renewable sources in 2020. Previously the people in charge of the competent portfolios in energy policies of the countries members had decided that the bio-fuel have, with strictly obligatory character, a 10 % of the fuel market.

Can Warren Buffetts Investment Decisions Independent of ESG? Evidence from Berkshire Hathaway

The past decade has witnessed a growing recognition of ESG (Environmental, Social, and Governance) investing, yet the application of ESG principles within Berkshire Hathaway, under the leadership of renowned investor Warren Buffett, has not been prominently evident. This study seeks to ascertain whether the historical investment decisions made by Buffett's Berkshire Hathaway, seemingly disassociated from active ESG involvement, may have nonetheless been subject to the influence of ESG factors. Employing methodologies encompassing correlation and regression analyses, this research unveils a discernible positive relationship between Berkshire Hathaway's investment quantities and the ESG scores of the invested firms. Moreover, companies demonstrating a positive trajectory in ESG considerations tend to be more aptly enlisted in the subsequent year's investment roster, while those witnessing a decline in ESG scores are prone to exclusion. In a comparative exploration of financial variables and ESG metrics, this study chose profit margin, ultimately revealing that ESG scores exert a notably more pronounced impact on Berkshire Hathaway's investment deliberations, specifically pertaining to share quantities, in contrast to the effect exerted by profit margin.

Sectoral Realism at the Junction of the Partition Plan of Palestine

ABSTRACTThis article applies qualitative dynamic content analysis to archival sources to demonstrate that religious identity was the primary motivation for Orthodox Greek Palestinians to join the Communist Party in 1948. Abandoned by the local Greek Orthodox Patriarchate, this sect of Palestinians hoped to gain the patronage of the Russian Orthodox Church. This was also their motive for supporting the plan of the United Nations to divide Mandatory Palestine into Arab and Jewish states, in contradiction of the national consensus at the time. Marxist theory, depicted as cosmopolitan, multinational, and multisectoral, helped this group camouflage its sectoral organization within the party’s higher echelons. The article stresses the importance of examining time and place when investigating historical decisions of political groups, such as those of the Palestinian communists in Israel, that have a significant impact on the process of shaping collective identity.

Clinical Validation of Explainable Deep Learning Model for Predicting the Mortality of In-Hospital Cardiac Arrest Using Diagnosis Codes of Electronic Health Records.

Using deep learning for disease outcome prediction is an approach that has made large advances in recent years. Notwithstanding its excellent performance, clinicians are also interested in learning how input affects prediction. Clinical validation of explainable deep learning models is also as yet unexplored. This study aims to evaluate the performance of Deep SHapley Additive exPlanations (D-SHAP) model in accurately identifying the diagnosis code associated with the highest mortality risk. Incidences of at least one in-hospital cardiac arrest (IHCA) for 168,693 patients as well as 1,569,478 clinical records were extracted from Taiwan's National Health Insurance Research Database. We propose a D-SHAP model to provide insights into deep learning model predictions. We trained a deep learning model to predict the 30-day mortality likelihoods of IHCA patients and used D-SHAP to see how the diagnosis codes affected the model's predictions. Physicians were asked to annotate a cardiac arrest dataset and provide expert opinions, which we used to validate our proposed method. A 1-to-4-point annotation of each record (current decision) along with four previous records (historical decision) was used to validate the current and historical D-SHAP values. A subset consisting of 402 patients with at least one cardiac arrest record was randomly selected from the IHCA cohort. The median age was 72 years, with mean and standard deviation of 69 17 years. Results indicated that D-SHAP can identify the cause of mortality based on the diagnosis codes. The top five most important diagnosis codes, namely respiratory failure, sepsis, pneumonia, shock, and acute kidney injury were consistent with the physician's opinion. Some diagnoses, such as urinary tract infection, showed a discrepancy between D-SHAP and clinical judgment due to the lower frequency of the disease and its occurrence in combination with other comorbidities. The D-SHAP framework was found to be an effective tool to explain deep neural networks and identify most of the important diagnoses for predicting patients' 30-day mortality. However, physicians should always carefully consider the structure of the original database and underlying pathophysiology.

Decision-making and Utility Theory

Decision-making is a process that would affect people's lives from simple daily decisions even to major life decisions. The understanding of judgment is critical for making better decisions as well as improving individual and collective outcomes. This paper would focus on historical decision theory, utility theory, prospective theory, and their applications in real life. The utility theory of the decision-making theory is a crucial part of the decision-making theories, as well as the prospect theory in the later paper. According to prospect theory and utility theory, people would make decisions based on the relative importance of the options' predicted values, and biases and heuristics would influence their choices. Both theories have applications in many fields such as economics, psychology, and medicine. They are used to form models and predict consumer behavior or financial decisions. Peoples lives are strongly associated with different decision makings, from simple to complex. The decision-making process is essential to human existence and fully connect to humans. By figuring out the decision-making processes, people can identify the factors that would influence the choices and make adjustments based on them. The application of the theories could be more practical. Thus, it is crucial to analyze or quantify it for more stable and reasonable choice-making.

Uwalnianie przestrzeni publicznej od symboli komunizmu — zakres obowiązków wojewodów (w świetle najnowszego orzecznictwa Naczelnego Sądu Administracyjnego)

The analysis of the so-called Decommunization Act’s application, after several years of its being in force, points to a significant legal problem. The recent decisions of the Supreme Administrative Court further expose said problem. The issue can be encompassed in the following question: what is, ultimately, the scope of voivodes’ duties with regard to investigating the facts of decommunization proceedings, especially in view of the opinions drawn up by the Institute of National Remembrance (Instytut Pamięci Narodowej, IPN) for the purposes of these proceedings? The answer to this question depends largely on the weight given to these opinions. In particular, if the voivodes are not bound by the IPN’s decisions, one may argue that they should be required to investigate the factual background on the issues on which the IPN had already fi led an opinion. Consequently, voivodes should make historical and legal decisions against the analyzed factual background independently.Analyses included in the current study suggest that, considering the recent decisions of the Supreme Administrative Court of Poland (which have a significant impact on the legal practice), voivodes are not bound by the IPN’s opinions. There is a strong emphasis on the view that the IPN’s opinions should each time be treated as part of evidence subject to verification, constituting grounds for proceedings pursuant to the provisions on the Decommunization Act. Therefore, in each case, verifying those opinions should be the obligation of the voivode. At the conceptual level, there was a condition that voivodes’ verifications of opinions should not interfere in substantive issues and should only examine methodological issues. At the practical level, however, there are cases in which verifying the IPN’s opinions beyond the methodological scope is allowed. The findings indicated above are considered in the context of the author’s belief that:
 — the analysis of the text of the Decommunization Act must lead to the conclusion that the legislator decided on the binding force of the IPN’s opinions, especially with regards to voivodes;
 — considering the legal status of the IPN, the scope of its tasks and competences, and the scope of tasks, capacity and competences of voivodes — as well as the specific nature of activities and proceedings based on the Decommunization Act — the opinions of the IPN cannot be treated as a regular part of evidence, requiring verification by the voivodes; from a systemic perspective, they should be treated as statements of knowledge by a competent state institution cooperating with the voivodes in activities and proceedings related to the Decommunization Act.