Probable Impact Research Articles

Changes in IL-6, IL-12, IL-5, IL-10 and TGF-β1 Concentration in Patients with Treatment-Resistant Schizophrenia (TRS) Following Electroconvulsive Therapy (ECT)-A Pilot Study.

Background/Objectives: Treatment-resistant schizophrenia (TRS) may be considered as a neuro-immune disorder. Electroconvulsive therapy (ECT) remains an important therapeutic option for patients with TRS, however, its impact on cytokine profile is barely investigated. Therefore, this study attempts to establish associations between serum cytokines IL-6, IL-12, IL-5, IL-10 and TGF-β1 changes (pre- and post-ECT) and the effectiveness of ECT in TRS patients. The second aim is to search for correlations between serum concentrations of the above specified cytokines and psychometric assessments of clinical schizophrenia symptoms. Methods: The cytokine concentrations were measured in eight TRS patients on psychopharmacological treatment prior to and following ECT and in 13 control subjects. Psychopathology assessment was based on the Positive and Negative Syndrome Scale (PANSS). Results: Prior to ECT, IL-10 concentration was significantly higher in TRS patients, while IL-5 was decreased in comparison to the controls. A significant concentration decrease in the pro-inflammatory cytokines IL-6 (p = 0.012), IL-12 (p = 0.049) and anti-inflammatory IL-10 (p = 0.012) post-ECT vs. pre-ECT was observed, whereas concentrations of IL-5 and TGF-β1 did not significantly change. Also, a significant decrease in schizophrenia symptoms measured by the PANSS post-ECT was found. Furthermore, the pattern of correlations between PANSS scores and cytokine concentrations was different when comparing levels pre- and post-ECT. Additionally, correlations between changes in PANSS scores and cytokine concentrations were found. Conclusions: These results may indicate the probable impact of electroconvulsive therapy on the balance between pro- and anti-inflammatory cytokines, which may correspond to a neurobiological therapeutic effect of ECT in TRS patients.

Can Historical Jurisprudence Inform the Artificial Intelligence and Law Debate?

The publication of a monograph by Dr Luca Siliquini-Cinelli on the history of scientia iuris in which he argues that law is a constructed form of knowledge that differs from experience is not just an important and very learned contribution to historical jurisprudence. The book’s thesis is also making an important contribution to the debate about the impact, and probable future impact, of artificial intelligence (AI) on law, legal thought and legal reasoning. In critically reviewing the book, this essay will briefly indicate how and why Dr Siliquini-Cinelli’s book is establishing a fundamental relationship between historical jurisprudence (understood as the history of legal thought) and AI. Keywords: artificial intelligence (AI); epistemology; legal singularity; map; model; philosophy; rule-theorist; territory.

Plan de manejo basado en ecosistemas para el santuario Chenkan, Campeche

The objective of this work was to design an ecosystem-based management plan for the Chenkan Beach Sanctuary, located in the municipality of Champoton, state of Campeche, using as methodology the twelve principles of the ecosystem approach mentioned by Shepherd (2006) grouped into the following steps: determination of the main actors, characterization of the structure and function of the ecosystem, identification of the relevant economic aspects that will affect the ecosystems and their inhabitants, determination of the probable impact of the ecosystem on adjacent ecosystems and decision of long-term goals and flexible mechanisms to achieve them. The results obtained indicate that the Chenkan Beach Sanctuary was named a protected natural area due to the great variety of species that are at risk, which are registered in the NOM-059-SEMARNAT-2010 and because it is a nesting site for the hawksbill turtle and the green turtle. It was found that the place has erosion problems that put the nesting sites at risk and that various anthropogenic activities have altered the ecosystems causing the turtles to have to migrate to find a new suitable site to nest. Based on all these problems, proposals are put forward to help mitigate the existing problems in the area. Keywords: Chenkan Beach, environmental problems, ecosystem services, ecosystem approach.

Imbalanced graph learning via mixed entropy minimization

Imbalanced datasets, where the minority class is underrepresented, pose significant challenges for node classification in graph learning. Traditional methods often address this issue through synthetic oversampling techniques for the minority class, which can complicate the training process. To address these challenges, we introduce a novel training paradigm for node classification on imbalanced graphs, based on mixed entropy minimization (ME). Our proposed method, GraphME, offers a ‘free imbalance defense’ against class imbalance without requiring additional steps to improve classification performance. ME aims to achieve the same goal as cross-entropy-maximizing the model’s probability for the correct classes-while effectively reducing the impact of incorrect class probabilities through a “guidance” term that ensures a balanced trade-off. We validate the effectiveness of our approach through experiments on multiple datasets, where GraphME consistently outperforms the traditional cross-entropy objective, demonstrating enhanced robustness. Moreover, our method can be seamlessly integrated with various adversarial training techniques, leading to substantial improvements in robustness. Notably, GraphME enhances classification accuracy without compromising efficiency, a significant improvement over existing methods. The GraphME code is available at: https://github.com/12chen20/GraphME.

AI-enabled detection of QRS fragmentation from 12-lead electrocardiogram and its clinical relevance for predicting malignant arrhythmia onset.

Electrocardiographic markers differentiating between death caused by ventricular arrhythmias and non-arrhythmic death could improve the selection of patients for implantable cardioverter-defibrillator (ICD) implantation. QRS fragmentation (fQRS) is a parameter of interest, but subject to debate. We investigated the association of an automatically quantified probability of fragmentation with the outcome in ICD patients. From a single-center retrospective registry, all patients implanted with an ICD between January 1996 and December 2018 were eligible for inclusion. Patients with active pacing were excluded. From the electronical medical record, clinical characteristics at implantation were collected and a 12-lead ECG was exported and analyzed by a previously validated machine-learning algorithm to quantify the probability of fQRS. To compare fQRS(+) and fQRS(-) patients, dichotomization was performed using the Youden index. Patients with a high probability of fragmentation in any region (anterior, inferior or lateral), were labeled fQRS(+). The impact of this fQRS probability on outcomes was investigated using Cox regression. A total of 1,242 patients with a mean age of 62.6 ± 11.5 years and a reduced left ventricular ejection fraction of 31 ± 12% were included of which 227 (18.3%) were female. The vast majority suffered from ischemic heart disease (64.3%) and were implanted in primary prevention (63.8%). 538 (43.3%) had a high probability of fragmentation in any region. Patients with a high probability of fragmentation had more frequently dilated cardiomyopathy (39.4% vs. 33.0%, p = 0.019), left bundle branch block (40.8% vs. 32.5%, p = 0.006) and a higher use of cardiac resynchronization therapy with defibrillator (CRT-D) devices (33.9% vs. 26.3%, p = 0.004). After adjustment in a multivariable Cox model, there was no significant association between the probability of global or regional fQRS and appropriate ICD therapy, inappropriate shock and short- or long-term mortality. There was no association between the automatically quantified probability of the presence of fQRS and outcome. This lack of predictive value might be due to the algorithm used, which identifies only the presence but not the severity of fragmentation.

The Impact of Tax Audit Intensity and Probability of Fraud Detection on Tax Evasion: The Moderating Role of Tax Officials' Service

This study investigates the relationships between tax audit intensity, the probability of fraud detection, and tax evasion while examining the moderating role of tax officials' service. The research reveals that tax audit intensity significantly reduces tax evasion, confirming its effectiveness as a deterrent. However, the hypothesis regarding the probability of fraud detection's impact on tax evasion was rejected, indicating that the likelihood of detection does not directly influence taxpayer behavior in this context. Additionally, the study found no significant moderating effect of tax officials' service on the relationship between tax audit intensity, fraud detection probability, and tax evasion. These findings suggest that while audit intensity is crucial for enhancing compliance, the quality of service provided by tax officials does not substantially alter taxpayer responses to enforcement measures. The results underscore the need for tax authorities to prioritize strengthening audit processes and detection mechanisms while recognizing that service quality, though important for building long-term trust, may not significantly influence immediate compliance behavior. Future research should explore other moderating factors that could impact taxpayer decisions in varying economic and cultural contexts.

Legal Reforms Addressing Sexual Harassment in India: Examining the Role of Women’s Dress as a Contributing Factor

Women's clothing choice is mainly used as a reason for violence against them. The socially constructed aggression toward women is made to fit into the role the woman might be perceived to have in an attack. This shifts part of the responsibility for this violation onto the victim and the perpetrator. This paper discusses how the concept of "choice of dress" has emerged as one of the factors contributing to sexual violence against women. This paper discusses public opinion on this issue and attempts to enter into the perception that women's attire is being used as a justification for violating their rights. This paper has proceeded to analyze the role of law in preventing sexual harassment and considers public opinions on the possible introduction of legal regulations concerning women's dress codes, together with the probable impact such regulations may have on women's self-esteem. This research is based on both primary (convenient sampling and analysis through SPSS software) and secondary sources (books, articles, reports, legal commentaries, and case laws) dealing with sexual harassment laws. It attempts to provide reform proposals that shift attention away from women's clothing and instead focus on accountability on the part of the perpetrators in legal frameworks.

A COMPREHENSIVE REVIEW ON GRAPHENE: HISTORICAL DEVELOPMENT, PROPERTIES, APPLICATIONS, CHALLENGES AND FUTURE SCOPE

Graphene, due to its extraordinary properties is revolutionizing various fields. This paper explores some unusual characteristics of graphene such as mechanical properties, electrical and thermal conductance, and optical transparency. We will discuss its potential uses in various areas: super-strong composites for ultra-light and extra-durable materials and next-generation gadgets where graphene exposure might offer improved speeds. In addition, the possible applications of this nano-material in developing novel clear and flexible touch panels and advanced water filtration technologies are explored. But there are also problems with increasing output, maintaining the quality, and managing safety and health aspects despite its great potential. The paper also discusses the probable future impact of graphene on the sector of medicine, the usage of renewable energy, smart communities, and more. This analysis enhances the identification of graphene as a future wonder material by highlighting the state of research, the application of the material, and the opportunities as well as the challenges that the field has and will encounter.

Assessment Literacy of Secondary School Teachers and Its Impact on Student's Performance in the Division of Gingoog City

This study examined teacher’s assessment literacy and its probable impact on students’ performance through the intervening variables of the teachers. The study had 234 public secondary school teachers in the Division of Gingoog City. It employed the Input-Process-Output (IPO) Model of David Bushnell which illustrates how teachers’ demographics (input): length of service, DepEd issuances, and assessment training/s attended, assessment literacy (process): assessment knowledge, assessment practices/beliefs and assessment result utilization will impact students’ performance (output). The results showed that most of the respondents have been in the service for five years and below, a little over half have attended training on test construction and table of specification, and on average only a little more than one-fourth of the teachers received copies of DepEd issuances relative to assessment. Additionally, it revealed that there is no conclusive evidence to show a direct correlation between teachers’ assessment literacy and students’ performance which means none of the assessment literacy variables significantly impact students’ performance. The results further revealed that no significant difference was noted in teachers’ length of service and DepEd issuance to assessment literacy. However, a highly significant difference was noted in assessment training attended and assessment literacy. It also disclosed that no matter how long the teachers have been in the service, whether they have copies of all DepEd issuances, these do not contribute to their literacy in assessment. The findings in this study underscored the need to provide quality and effective inputs from both the department and the respondents. There has to be a relevant and appropriate process to effect quality output, which is commendable student performance.

Perancangan Risk Assessment dengan Metode Kualitatif dan Kuantitatif pada Proyek Pembangunan Ruko di PT XYZ

PT XYZ, a contractor company in the construction sector, is currently working on a project to build 24 shop houses on Jl. Hospital, Bandung City, with a duration of 21 weeks from June to November 2024. This project is divided into several works, including preparation, structure, installation, plumber installation, electrical installation, and wall painting. The project faced challenges such as work accidents, material delays, and conflicts between foremen and subcontractor workers. Due to the lack of effective risk assessment, PT XYZ has difficulty providing a significant response to this problem. Research was conducted to design a risk assessment that includes qualitative and quantitative risk analysis. Qualitative methods are used to create probability impact matrices, risk watch lists and priority risks, while quantitative methods are used to calculate sensitivity analysis related to risk costs. From the research, 186 negative risks and 5 positive risks were found with classifications ranging from insignificant to catastrophic, with the largest risk cost reaching IDR 851,529,000. Researchers also designed contingency plans, checklist forms, material quality checks, and risk reporting to assist PT XYZ in handling project risks.

Impact of bedaquiline resistance probability on treatment decision for rifampicin-resistant TB

<sec><title>BACKGROUND</title>Accurate diagnosis of bedaquiline (BDQ) resistance remains challenging. A Bayesian approach expresses this uncertainty as a probability of BDQ resistance (prBDQR) with a 95% credible interval. We investigated how prBDQR information influences BDQ prescribing decisions.</sec><sec><title>METHOD</title>We performed a discrete choice experiment with 55 international rifampicin-resistant tuberculosis physicians. We employed mixed-effects multinomial logistic regression to quantify the effect of prBDQR, patient attributes, and contextual factors on the decision to continue BDQ or not when sequencing results become available.</sec><sec><title>RESULTS</title>PrBDQR was the most influential factor for BDQ decision-making, three times greater than treatment response. Each percentage point increase in prBDQR resulted in 8.2% lower odds (OR 0.92, 95% CI 0.90–0.93) of continuing BDQ as a fully effective drug and 5.0% lower odds (OR 0.95, 95% CI 0.94–0.96) of continuing it but not counting it as an effective drug. The most favourable patient profile for prescribing BDQ as a fully effective drug was a patient receiving the BPaLM regimen (BDQ, pretomanid, linezolid and moxifloxacin) with low prBDQR, good 1-month treatment response, fluoroquinolone-susceptible TB, and no prior BDQ treatment. Physicians with higher discomfort with uncertainty and more years of experience with BDQ were more inclined to stop BDQ.</sec><sec><title>CONCLUSION</title>Given the uncertainty of genotype-phenotype associations, physicians valued prBDQR for BDQ decision-making in rifampicin-resistant TB treatment.</sec>

Analisis Manajemen Risiko Proyek dengan Metode Severity Index (SI) pada Proyek Pembangunan Gedung X Kota Surakarta

The construction of a high-rise building is a construction project that can be said to have high risk factors so that it will have a negative effect on the project schedule, quality, and costs needed. Project risk management analysis is needed to control risks and to determine risk handling to prevent the occurrence of dominant risks in construction projects. In this study, primary data collection was carried out by distributing questionnaires, while secondary data was obtained through data from projects and literature studies. The data obtained from the questionnaire will be analyzed for risk variables using the Severity Index (SI) method. Meanwhile, the risk level analysis uses the Probability Impact Matrix (PIM). The results of the analysis show that there is a dominant risk that has the most influence on the time aspect is the risk of delay in material delivery with a high level of risk. The dominant risk that has the most influence on the cost aspect is the occurrence of equipment damage while working with a medium risk level. The dominant risk that has the most influence on the quality aspect is that the quality of the material is not in accordance with the quality specifications with a medium risk level.

Do macroprudential measures increase inequality? Evidence from the euro area household survey

AbstractBorrower‐based macroprudential policies—such as caps on loan‐to‐value (LTV) ratios and debt‐service‐to‐income (DSTI) limits—contain the build‐up of systemic risk by reducing the probability and conditional impact of a crisis. While LTV/DSTI limits can increase inequality at introduction, they can dampen the increase in inequality under adverse macroeconomic conditions. The relative size of these opposing effects is an empirical question. We conduct counterfactual simulations under different macroeconomic and macroprudential policy scenarios using granular income and wealth data from the Households Finance and Consumption Survey for Ireland, Italy, Netherlands and Portugal. Simulation results show that borrower‐based measures are associated with a moderate increase in wealth inequality, while the impact on income inequality is negligible.

The life of the Sun

Understanding the development and evolution of the Sun is critical for understanding the origins of our solar system and the circumstances for life on Earth. We can learn about our suns dynamics, energy output, and magnetic activity by analysing its evolution, and we can even forecast solar events that affect our world. Extensive study has indicated that the Sun is now in its main sequence stage, but research on its future development, such as becoming a red giant or crashing into a white dwarf, is still underway. Solar evolution research also sheds light on the inner workings of other stars and fundamental processes in the cosmos. The Hertzsprung-Russell diagram aids in the classification and understanding of stars by providing essential information about their life cycle and attributes. The pre-main sequence, main sequence, and post-main sequence stages of the Suns origin and development are discussed in this article. It examines the origin of the Sun from interstellar materials as well as the probable impact of core-collapse supernovae. It also delves into the main sequence period, where stars masses and ages play important roles in shaping their properties. The main sequences existence is linked to hydrostatic equilibrium and the balance of energy production and gas weight. The scattering of stars that depart from the main sequence, such as red giants and white dwarfs, is also discussed in the article. Finally, it dives into post-main sequence development, in which the Sun switches from hydrogen fusion to helium fusion, causing the star to expand and cool. Finally, the Suns ultimate phase of development involves the burning of hydrogen and helium, which results in the expulsion of outer layers and the creation of a planetary nebula. The remaining core cools and transforms into a black dwarf, bereft of nuclear and gravitational energy sources.

The Impact of Statistics and Probability on Educational Artificial Intelligence

Artificial intelligence has transformed e-learning by enabling personalized and efficient teaching. This manuscript analyzes the importance of statistics and probability in educational AI. Statistical methodologies improve decision-making, personalize learning, and optimize educational outcomes. Challenges such as data privacy and ethics are addressed. Case studies demonstrate the practical applications of AI in diverse educational contexts. Future directions suggest a need for robust research to further understand and implement AI-driven educational strategies. The findings underscore the critical role of data-driven approaches in shaping the future of education. Statistics and probability are not only foundational to the development of AI but also essential for analyzing vast amounts of educational data. They allow for the creation of predictive models that can identify student needs and adapt instructional methods accordingly. This adaptability enhances the learning experience by providing targeted support and resources to students, thereby improving their academic performance. Ethical considerations are fundamental when using AI to handle educational data. Protecting student data with privacy and security is crucial to maintaining trust in AI applications. This manuscript examines how educators and policymakers can collaborate to create guidelines that safeguard student information while utilizing data to enhance education. Integrating statistics and probability into educational AI significantly impacts and improves e-learning. Educators can enhance learning by employing data-driven strategies that provide personalized and effective teaching. This approach not only benefits individual learners but also contributes to the overall advancement of educational practices. Embracing these data-driven methodologies is essential for the continued evolution of teaching and learning in the digital age.

Analysis of Risk Factors for Delays in the Implementation of the Meninting Dam Construction Project, West Lombok Regency

The Meninting Dam Construction Project is part of the National Strategic Project (PSN). Delays can occur in any project, including the construction of the Meninting Dam in West Lombok. This study aims to identify and analyze risk factors impacting the project, determine the risks classified as dominant major risks, and evaluate the residual risks of the Meninting Dam construction project to formulate risk responses that minimize the impact on the project. The study employs the Fault Tree Analysis (FTA) and Probability Impact Matrix (PIM) methods and uses related sources as theoretical foundations. Based on the study results, the Meninting Dam project identified four factors that most significantly affect delays using the FTA method: 1) “Delay in decision-making by the owner,” probability = 17.443%, 2) “Lack of control over work by Construction Management,” probability = 13.771%, 3) “Inadequate planning and control by the Contractor,” probability = 17.207%, and 4) “Weather intensity or high rainfall/heat,” probability = 13.041%. From the identification, four dominant risk variables with high risk levels were obtained using the PIM method: 1) Objections from local residents regarding land acquisition, 2) Delays in material arrival, 3) Delays in procurement of work equipment, and 4) High rainfall causing uncompact soil to become landslides. The risk mitigation category used in this study is risk reduction. The assessment of residual risk shows that of the four risks initially at a high level, these risks decreased after risk mitigation, with two risks becoming moderate and two risks becoming low.

Resilience against Catastrophic Cyber Incidents: A Multistakeholder Analysis of Cyber Insurance

Catastrophic cyber incidents—events of low probability but high impact, with the potential to incur billions of dollars in damages—are prompting insurers to elevate premiums, create higher barriers for potential buyers, and tighten policies with exclusions. While these responses of the insurance industry are important to prevent its insolvency during catastrophic incidents due to excessive claims, they lead to a notable gap in market protection. Using a content analysis of multistakeholder comments submitted in response to a Treasury Department Request for Information (RFI), this study seeks to define what constitutes a catastrophic cyber event, identify mitigation strategies, evaluate the current capacity of the cyber insurance sector to handle such incidents, and investigate the potential roles and support mechanisms that the government can provide to enhance the insurance sector’s capacity to manage these extreme risks. This paper is one of the pioneering studies using data and a multistakeholder perspective to provide essential guidance for policymakers, regulators, the insurance industry, and the cybersecurity sector in formulating robust policies and strategies to address catastrophic cyber risks, ultimately enhancing national economic and technological resilience.

The probable impact of policies and policy measures on globalisation

This paper aims to identify the impact of the recent economic and non-economic shocks on the globalisation of trade in terms of trade policy and structural openness, and to take a side in the discussion about the reversal, the slowdown, and the continuation of trade globalisation. The report argues that geopolitical decisions based on pure political priorities and different macro- and micro-policies may harm trade globalisation temporarily. Nevertheless, in the long run, underlying economic factors, such as decreasing trade costs and expanding services trade remain the critical driving forces of globalisation. The speed of the process is likely to be slower than before, and its characteristics are different. With weakening multilateralism and increasing fragmentation, the emerging global world order will be suboptimal from the point of view of economic efficiency. Journal of Economic Literature (JEL) codes: F01, F02, F13, F60.

The Effectiveness of Semi-Supervised Learning Techniques in Identifying Calcifications in X-ray Mammography and the Impact of Different Classification Probabilities

Identifying calcifications in mammograms is crucial for early breast cancer detection, and semi-supervised learning, which utilizes a small dataset for supervised learning combined with deep learning, is anticipated to be an effective approach for automating this identification process. This study explored the impact of semi-supervised learning on identifying mammographic calcifications by including 712 mammographic images from 252 patients in public datasets. Initially, 212 mammogram images were segmented into patches and classified visually for calcification presence. A subset of these patches, derived from 169 mammogram images, was used to train a ResNet50-based classifier. The classifier was evaluated using patches generated from 43 mammograms as a test data set. Additionally, 500 more mammogram images were processed into patches and analyzed using the trained ResNet50 model, with semi-supervised learning applied to patches exceeding certain classification probabilities. This process aimed to enhance the classifier’s accuracy and achieve improvements over the initial model. The findings indicated that semi-supervised learning significantly benefits the accuracy of calcification detection in mammography, underscoring its utility in enhancing diagnostic methodologies.

Assessment of environmental impact of road construction based on results of remote sensing monitoring

Remote sensing detected the impact of road construction on important environmental properties. The digital elevation model was applied to assess the impact of road construction on environmental regimes. The study focused on the total area of the 3-km zone of influence (monitoring zone of probable impact) around the national road H-31 Dnipro-Tsarychanka-Kobeliaky-Reshetylivka from Loboikivka village to the boundary of Dnipropetrovsk Oblast. The following derivatives of the digital elevation model are considered as geomorpho­logical variables: the topographic wetness index (TWI) and erosion factor (LS). To model the effect of road construction on the moisture regime and dynamics of erosion processes, the TWI and LS indices were calculated for a digital terrain model without a road and with a road with an elevation that corresponds to the planned level. Thus, the road is considered as an anthropogenic landform that changes the direction of water redistribution along the topography and thus affects the potential moisture conditions and erosion risks. The modeling results indicate that the transformation of the water regime will be observed within the entire 3-km monitoring area. Deviations from the normal value can be up to 7 units by module, which is about one third of the landscape-wide range of topographic moisture index values. Almost no impact of construction can be predicted for 80.7% of the territory. For 10.6% of the territory, the increase in moisture regime will be moderate, and for 3.2% of the area the increase will be very significant. In turn, for 4.3% of the area the decrease in humidification will be moderate, and for 1.2% the decrease will be very significant. The greatest impact on the redistribution of moisture conditions is predicted in the immediate vicinity of the road. Changes in the moisture regime can have significant negative consequences for both soil cover and biotic components of ecosystems. Changes in vegetation as a consequence of changes in both moisture and trophic conditions can be considered as possible scenarios for the development of landscape cover dynamics.