Vian S. Ismail, Sawza Abdulsalam Mohammed, Sumaya Tahir Juju
et al.
A simple, accurate, and sensitive spectrophotometric method has been developed for the quantitative determination of acetaminophen (paracetamol, PAR) in pharmaceutical formulations including tablets, syrups, and injectable solutions. The method involves acid hydrolysis of PAR to yield p-aminophenol, which is subsequently subjected to diazotization to form a diazonium salt. This diazonium intermediate reacts with resorcinol and 1-naphthol to form colored azo dyes, exhibiting maximum absorbance at 480 nm and 510 nm, respectively. The method adheres to Beer’s law over concentration ranges of 3–15 μg/mL for the resorcinol system and 2.5–20 μg/mL for the 1-naphthol system. The calculated molar absorptivity values were 18.5 × 10³ and 9.8 × 10³ L·mol⁻¹·cm⁻¹, with corresponding Sandell’s sensitivities of 0.008 and 0.015 μg·cm⁻², respectively. Method validation demonstrated satisfactory accuracy, with mean percentage recoveries ranging from 94.78% to 105.73%. Furthermore, the method exhibited good linearity, low limits of detection and quantification, and minimal relative error, underscoring its reliability and suitability for routine quality control analysis of PAR in commercial pharmaceutical preparations.
Thermal energy storage (TES) is an effective method for load shifting and demand response in buildings. Optimal TES control and management are essential to improve the performance of the cooling system. Most existing TES systems operate on a fixed schedule, which cannot take full advantage of its load shifting capability, and requires extensive investigation and optimization. This study proposed a novel integrated load prediction and optimized control approach for ice-based TES in commercial buildings. A cooling load prediction model was developed and a mid-day modification mechanism was introduced into the prediction model to improve the accuracy. Based on the predictions, a rule-based control strategy was proposed according to the time-of-use tariff; the mid-day control adjustment mechanism was introduced in accordance with the mid-day prediction modifications. The proposed approach was applied in the ice-based TES system of a commercial complex in Beijing, and achieved a mean absolute error (MAE) of 389 kW and coefficient of variance of MAE of 12.5%. The integrated prediction-based control strategy achieved an energy cost saving rate of 9.9%. The proposed model was deployed in the realistic building automation system of the case building and significantly improved the efficiency and automation of the cooling system.
Thijs L van der Plas, Stephen Law, Michael JO Pocock
The growing demand for scalable biodiversity monitoring methods has fuelled interest in remote sensing data, due to its widespread availability and extensive coverage. Traditionally, the application of remote sensing to biodiversity research has focused on mapping and monitoring habitats, but with increasing availability of large-scale citizen-science wildlife observation data, recent methods have started to explore predicting multi-species presence directly from satellite images. This paper presents a new data set for predicting butterfly species presence from satellite data in the United Kingdom. We experimentally optimise a Resnet-based model to predict multi-species presence from 4-band satellite images, and find that this model especially outperforms the mean rate baseline for locations with high species biodiversity. To improve performance, we develop a soft, supervised contrastive regularisation loss that is tailored to probabilistic labels (such as species-presence data), and demonstrate that this improves prediction accuracy. In summary, our new data set and contrastive regularisation method contribute to the open challenge of accurately predicting species biodiversity from remote sensing data, which is key for efficient biodiversity monitoring.
A selection of new results from the CMS experiment is presented. These results focus on searches for dark-sector particles using Run 2 or Run 3 data. Dedicated data streams were utilised to explore the low-mass parameter space. Machine learning techniques were employed to discriminate between signal and background.
If you take a superposition of n IID copies of a point process and thin that by a factor of 1/n, then the resulting process tends to a Poisson point process as n tends to infinity. We give a simple proof of this result that highlights its similarity to the law of large numbers and to the law of thin numbers of Harremoës et al.
Context: Large Language Models (LLMs) enable automation of complex natural language processing across domains, but research on domain-specific applications like Finance remains limited. Objectives: This study explored open-source and commercial LLMs for financial report analysis and commentary generation, focusing on software engineering challenges in implementation. Methods: Using Design Science Research methodology, an exploratory case study iteratively designed and evaluated two LLM-based systems: one with local open-source models in a multi-agent workflow, another using commercial GPT-4o. Both were assessed through expert evaluation of real-world financial reporting use cases. Results: LLMs demonstrated strong potential for automating financial reporting tasks, but integration presented significant challenges. Iterative development revealed issues including prompt design, contextual dependency, and implementation trade-offs. Cloud-based models offered superior fluency and usability but raised data privacy and external dependency concerns. Local open-source models provided better data control and compliance but required substantially more engineering effort for reliability and usability. Conclusion: LLMs show strong potential for financial reporting automation, but successful integration requires careful attention to architecture, prompt design, and system reliability. Implementation success depends on addressing domain-specific challenges through tailored validation mechanisms and engineering strategies that balance accuracy, control, and compliance.
Introduction. The article focuses on the model legislation of the International Swaps and Derivatives Association, which has been publishing model laws for over-the-counter financial market participants for nearly three decades. The association has been extremely successful in this endeavor, with its model laws implemented in over 80 jurisdictions.Methodology and materials. The research methodology is a combination of methods employed to achieve the research objectives. The doctrinal method is used for the direct analysis of the content of standard contracts and model acts of the Model Law on International Swaps and Derivatives Association (ISDA). Through a historical approach, a general context is provided that accompanied the development of the model laws discussed in this publication. The statistical method is applied to describe the favorable economic effects resulting from the implementation of the ISDA model legislation.Research results and their discussion. The article provides a brief description of its activities, with a particular emphasis on its standard framework agreements and contractual terms widely spread in international commercial turnover. In particular the article dwells upon the procedure for termination of contractual obligations envisaged in standard agreements of the International Swaps and Derivatives Association and commonly known as close-out netting. The publication provides the grounds for international legal harmonization to achieve enforceability of contractual provisions encompassing close-out netting. It then examines the International Swaps and Derivatives Association model law published in 1996, which was primarily aimed at recognizing close-out netting provisions in all affected jurisdictions.Conclusions. The third part of the article is devoted to the 2002 model law, which provided much-needed protection for collateral provisions. The article concludes with recommendations for amending domestic contractual standards governing derivative financial instruments to bring them in line with recommendations of International Swaps and Derivatives Association. The said reform may overall contribute to competitive strengths of domestic financial market.
Karin G. M. Lenssen, Karin G. M. Lenssen, Alie de Boer
et al.
IntroductionEven though botanicals are increasingly popular ingredients for food supplements, health claims related to their putative bene ts remain unclearly regulated.MethodsThrough an analysis of EU and national case law from the Netherlands, including self-regulatory decision-making, we have studied the implications of case law on botanical health claims.ResultsOur analysis reveals that there are multiple issues related to claims on botanical-containing products: whether it should be classi ed as food or medicine; what statements should be understood as health claims; what type of evidence should underlie health claims and, more specically, botanical health claims; and how to deal with online commercial communication. The case law analysis highlights rst that a gray area will continue to exist when classifying products as foods or medicinal products, particularly when it comes to products that contain botanical ingredients. Most importantly, our study also reveals that claims—even when they are on hold, like botanical claims—need a certain scienti c foundation before they can be used on products. Finally, the courts believe that even though on-hold claims will continue to give a certain level of uncertainty for food business operators, this is not a legal but rather a regulatory issue.DiscussionThe findings from our case law analysis highlight that even though case law is useful in further interpretation of legislation, it does not provide any policy advancement. In the case of botanicals, a political decision regarding their substantiation is highly desired.
Existing methods for learning urban space representations from Point-of-Interest (POI) data face several limitations, including issues with geographical delineation, inadequate spatial information modelling, underutilisation of POI semantic attributes, and computational inefficiencies. To address these issues, we propose CaLLiPer (Contrastive Language-Location Pre-training), a novel representation learning model that directly embeds continuous urban spaces into vector representations that can capture the spatial and semantic distribution of urban environment. This model leverages a multimodal contrastive learning objective, aligning location embeddings with textual POI descriptions, thereby bypassing the need for complex training corpus construction and negative sampling. We validate CaLLiPer's effectiveness by applying it to learning urban space representations in London, UK, where it demonstrates 5-15% improvement in predictive performance for land use classification and socioeconomic mapping tasks compared to state-of-the-art methods. Visualisations of the learned representations further illustrate our model's advantages in capturing spatial variations in urban semantics with high accuracy and fine resolution. Additionally, CaLLiPer achieves reduced training time, showcasing its efficiency and scalability. This work provides a promising pathway for scalable, semantically rich urban space representation learning that can support the development of geospatial foundation models. The implementation code is available at https://github.com/xlwang233/CaLLiPer.
Viktoriia Rudevska, Iryna Boyarko, Artem Shcherbyna
et al.
In the context of escalating military and political uncertainties, a crucial component of banking system stability is the establishment of an adequate level of resource provision – liquidity. The research aims to analyze the transmission impact of the banking system's liquidity and its structure on the volumes of financing for the real sector of the economy in the second year of the war in Ukraine. In the conditions of war and the corresponding intensification of military-political threats and uncertainties, the regulator has made numerous complex decisions and restrictions aimed at balancing the challenges with the current situation in financial markets.
The research found that the banking sector of Ukraine currently accumulates a significant amount of excess liquidity and demonstrates high profitability. However, in the conditions of war, the transmission mechanism works improperly, requiring constant intervention from the regulator to balance the liquidity of the banking sector and state finances, which, in turn, affects the behavior of commercial banks and changes the structure of their asset portfolios. An analysis of the structure of active operations portfolios of the banking, corporate, and private sectors provides grounds to assert that there is no reason to expect a change in investment behavior from these groups in the perspective of the next year due to the specificity of the conditions imposed by the Ministry of Finance and the regulator. To maintain macroeconomic stability in the national economy in the conditions of martial law it is necessary to introduce conditions to reduce demand for foreign currency and, as an alternative, offer the preservation of savings solvency through simplifying access to investments in government securities for households. The low financial literacy of the population and the underdeveloped stock market in Ukraine, as well as the more complex mechanism of purchasing government bonds or military bonds compared to deposit services, make such investments by the population insignificant and limited in demand.
The superelastic constitutive model implemented in the commercial finite element code ABAQUS is verified using the method of exact solutions (MES). An analytical solution for uniaxial strain is first developed under a set of simplifying assumptions including von Mises-like transformation surfaces, symmetric transformation behavior, and monotonic loading. Numerical simulations are then performed, and simulation predictions are compared to the exact analytical solutions. Results reveal the superelasticity model agrees with the analytical solution to within one ten-thousandth of a percent (0.0001%) or less for stress and strain quantities of interest when using displacement-driven boundary conditions. Full derivation of the analytical solution is provided in an Appendix, and simulation input files and post-processing scripts are provided as supplemental material.
Terahertz (THz) technologies have been of interest for many years due to the variety of applications including gas sensing, nonionizing imaging of biological systems, security and defense, etc. To date, scientists have used different classes of materials to perform different THz functions. However, to assemble an on-chip THz integrated system, we must understand how to integrate these different materials. Here, we explore the growth of Bi2Se3, a topological insulator (TI) material that could serve as a plasmonic waveguide in THz integrated devices, on technologically-important GaAs (001) substrates. We explore surface treatments and find that atomically smooth GaAs surface is critical to achieving high-quality Bi2Se3 films despite the relatively weak film/substrate interaction. Calculations indicate that the Bi2Se3/GaAs interface is likely selenium-terminated and shows no evidence of chemical bonding between the Bi2Se3 and the substrate. These results are a guide for integrating van der Waals materials with conventional semiconductor substrates and serve as the first steps toward achieving an on-chip THz integrated system.
In the Republic of Moldova, we believe that the institution of mandatory mediation in certain categories of disputes could be given a chance of success because, in the current crisis that European states are going through due to the armed conflict in Ukraine, it is justified, all the more, for it to it is given a real chance to prove itself, this time from the position of compulsory mediation for a series of minor disputes carried out by mediators and not by other legal professionals. In our opinion, the obligation of mediation can be instituted in all categories of disputes listed in art. 25-30 of the mediation law, but in order to help the Moldovan society through the institution of mediation to overcome these prolonged crises, we propose the legislator to be inspired by the experience of the Italian legislator who found a way to penetrate the national and European legislation.
Private international law. Conflict of laws, Jurisprudence. Philosophy and theory of law
Muhammad Arsalan Hashmi, Abdullah, Rayenda Khresna Brahmana
et al.
The study investigates whether effective audit committees, gender-diverse boards, and corruption controls affect the level of voluntary disclosures of Asian banks. Further, we analyze whether directors’ experience moderates the impact of audit committee independence, audit committee meetings, board gender diversity, and corruption controls on voluntary disclosures. We use data for commercial banks operating in six Asian countries, i.e., China, India, Pakistan, Malaysia, Hong Kong, and Singapore. For empirical analysis, we apply several robust statistical techniques. We find that commercial banks with effective audit committees, gender-diverse boards, and corruption controls tend to disclose less information voluntarily as they perceive limited benefits from optional disclosures. Further, we find unique evidence that directors’ experience significantly moderates the impact of audit committee independence, audit committee meetings, board gender diversity, and corruption controls on voluntary disclosures of Asian banks. Our unique findings are consistent with the proprietary cost theory. Further, our results indicate that commercial banks operating in countries that maintain rule of law, regulatory quality, and government effectiveness tend to disclose less information voluntarily.
In order to explore the connotation and construction path of the principle of good faith from the perspective of civil and commercial law, this paper combines intelligent algorithms to analyze the connotation and construction path of the principle of good faith from the perspective of civil and commercial law. Moreover, this paper presents a topology network specially used to improve the energy absorption efficiency of the isolation resistor used in the power divider and theoretically analyzes the effectiveness of the isolation topology. In addition, this paper further optimizes the topology of the two-way broadband filter power divider and builds a simulation model based on the basic content of the principle of good faith. From the research results, the simulation model proposed in this paper can play a certain role in the connotation and construction path of the principle of good faith from the perspective of civil and commercial law. On this basis, some suggestions on the connotation and construction path of the principle of good faith are put forward.
Image registration has been widely studied over the past several decades, with numerous applications in science, engineering and medicine. Most of the conventional mathematical models for large deformation image registration rely on prescribed landmarks, which usually require tedious manual labeling and are prone to error. In recent years, there has been a surge of interest in the use of machine learning for image registration. In this paper, we develop a novel method for large deformation image registration by a fusion of quasiconformal theory and convolutional neural network (CNN). More specifically, we propose a quasiconformal energy model with a novel fidelity term that incorporates the features extracted using a pre-trained CNN, thereby allowing us to obtain meaningful registration results without any guidance of prescribed landmarks. Moreover, unlike many prior image registration methods, the bijectivity of our method is guaranteed by quasiconformal theory. Experimental results are presented to demonstrate the effectiveness of the proposed method. More broadly, our work sheds light on how rigorous mathematical theories and practical machine learning approaches can be integrated for developing computational methods with improved performance.
Game agents such as opponents, non-player characters, and teammates are central to player experiences in many modern games. As the landscape of AI techniques used in the games industry evolves to adopt machine learning (ML) more widely, it is vital that the research community learn from the best practices cultivated within the industry over decades creating agents. However, although commercial game agent creation pipelines are more mature than those based on ML, opportunities for improvement still abound. As a foundation for shared progress identifying research opportunities between researchers and practitioners, we interviewed seventeen game agent creators from AAA studios, indie studios, and industrial research labs about the challenges they experienced with their professional workflows. Our study revealed several open challenges ranging from design to implementation and evaluation. We compare with literature from the research community that address the challenges identified and conclude by highlighting promising directions for future research supporting agent creation in the games industry.