ethicalabs/Research-Intent-Collab huggingface dataset:
Experimental multivariable adaptive optimization of the steady‐state cellular productivity of a continuous baker's yeast culture

AbstractA multivariable adaptive optimization algorithm that uses transient data to improve the optimization speed was successfully implemented on‐line to maximize the steady‐state cellular productivity of a continuous culture of baker's yeast. The algorithm was shown to be stable even during periods of oscillatory growth and was able to reoptimize the culture when planned disturbances were introduced. Although adaptive tuning of the forgetting factor improved the performance, further refinements in the adaptive forgetting factor algorithm are necessary for completely satisfactory results.
International human rights standards and indigenous peoples’ land and human rights in Asia

This chapter starts with a review of the formal legal status of indigenous peoples in the different countries in Asia, since the nature of the status often determines the nature and scope of application of the rights concerned, both in reference to international human rights norms and standards, and national constitutional and legal provisions. Indigenous peoples are expressly and directly recognized as 'indigenous' in only two national constitutions in Asia: Nepal and the Philippines. Nepal may also be regarded as a potentially strong example of the protection of indigenous peoples' rights considering the provisions of its interim constitution and several drafts of the proposed constitutional measures that were discussed in its first constituent assembly, prior to its dissolution in May 2012. The chapter provides a brief summary of the status of civil and political rights in some of the countries of South and Southeast Asia, along with an account of major infringements on land and resource rights.
Data localization as contested and narrated security in the age of digital sovereignty: the case of Switzerland

The construction and effects on national boundaries have become central topics in public and academic debates on digital sovereignty. Both state and non-state actors increasingly consider jurisdictions and traditional governing structures as means to capture and regulate digital data flows. This article delves into the intricate phenomenon of ‘data localization’, conceptualizing it as a socio-technical assemblage reflecting the evolving expectations surrounding Internet architecture and national boundaries. Interviewing the users of Threema – a Swiss secure messaging app – this study unravels data localization practices as a hybrid black box, intertwining technical changes, political discourses, socio-technical imaginaries, and shifting social norms. Drawing on the field of Science and Technology Studies, we mobilize the analytical tools of controversy and discourse to highlight data localization as a locus of political contestation in Switzerland, where imaginaries of national boundaries are often mobilized to symbolize security and reliability. The article provides three key contributions to the discourse on digital sovereignty, fragmentation, and governance. Firstly, it argues for the usefulness of Science and Technology Studies in understanding Internet governance, emphasizing the need for analyses grounded in specific socio-technical contexts. Secondly, it advocates for a social perspective on digital sovereignty, emphasizing user agency, social movements, and collective action as crucial factors shaping the governance of data flows. Lastly, the article sheds light on users resorting to state jurisdictions as a means to reinforce control over data flows, exploring the discursive mobilization of national boundaries in the digital public sphere.
A characterization of first-order phase transitions for superstable interactions in classical statistical mechanics

zbMATH Open Web Interface contents unavailable due to conflicting licenses.
AI-Driven Closed-Loop Agricultural Robotics for Precision Phenotyping and Autonomous Selective Harvesting

This paper addresses the persistent gap between robotic crop monitoring and autonomous harvesting by proposing an integrated, closed-loop agricultural system that unifies real-time AI-driven phenotyping with selective fruit harvesting in a single operational framework.Design/methodology/approach: We developed and field-evaluated AgriLoop-1, a unified robotic platform combining a multi-modal perception suite, a compliant end-effector for damage-aware fruit handling, and a co-designed AI software architecture. The perception pipeline leverages a YOLOv8-Seg model to perform simultaneous fruit detection, instance segmentation, ripeness classification, and yield estimation, while a dynamic Harvestability Score guide’s target prioritization and optimized picking sequences based on spatial, phenotypic, and reachability constraints.Findings: Field trials conducted in a commercial apple orchard demonstrated reliable real-world performance, achieving an operational success rate of 87.0% and an effective harvesting throughput of 122 fruits per hour over 15 hours of continuous deployment. The system maintained stable closed-loop operation under varying environmental conditions, confirming the robustness of the integrated perception–decision–action architecture.Originality/value: This study presents a field-validated robotic system that effectively closes the perception–action loop in agricultural robotics. By tightly integrating precision phenotyping with adaptive harvesting decisions, the proposed framework moves beyond conventional modular approaches and establishes a practical foundation for intelligent, data-driven crop management systems capable of addressing labor shortages and supporting sustainable agricultural production.
International Efforts in Conflict Resolution and Peace Building in Somalia

ABSTRACT  The study investigated the relationship between conflict resolution and peace building in Somalia. The studywas guided by three objectives, determine; the extent of conflict resolution in Somalia, the extent of peacebuilding in Somalia and determining whether there is a significant relationship between conflict resolution and peace building in Mogadishu Somalia. The study comprised of a population of 356 respondents, and adescriptive research design was used to collect data from 188 respondents using self-administered questionnaires as the main data collection instrument. The findings established that, the extent of conflict resolution was generally satisfactory, the two variables are positive and significantly correlated, and this implies rejecting the null hypothesis that there is a significant relationship between conflict resolution and peacebuilding in Mogadishu, Somalia. Arising from the findings appropriate recommendations and areas of further research were made, the conclusions were made in line with the objectives of the study where the study concurred with Adams's theory of conflict management (1982) which states that conflicts between human beings are unavoidable and varies from a mild disagreement to a win-or-lose, emotion- packed, confrontation.There is a significant effect between conflict resolution and peace building in Somalia (F=252.797, Sig=0.000),significant relationship between conflict resolution and peace building in Somalia (r= .295 & Sig=0.003) andregression indicated by high adjusted R squared of 68% was established. Recommendations based on findingswere that; political leaders in Somalia should apply avoiding as a conflict resolution style when solving conflictsamong different groups, should always involve collaboration of parties working together to solve issues concerning conflicts in Somalia, there must exist discussions among conflicting parties as a method of solving conflicts in Somalia and the active combatants should be discharged from armed forces and this will maintainpeace in Somalia. Further research can be carried out on accommodation and peace building in Somalia, Conflict resolution and negotiation practices and Conflict resolution and negotiation practices in Somalia.
Penyelesaian Sengketa Minyak Kelapa Sawit Mentah Indonesia dengan Uni Eropa Melalui Dispute Settlement Body World Trade Organization

Indonesia dan Uni Eropa mengalami sengketa perdagangan internasional terkait minyak kelapa sawit yang diselesaikan melalui Dispute Settlement Body (DSB) World Trade Organization. Sengketa ini dipicu oleh kebijakan Uni Eropa, seperti Renewable Energy Directive II dan ketentuan terkait biofuel berbasis kelapa sawit, yang dianggap diskriminatif dan merugikan ekspor Indonesia. WTO melalui DSB berhasil menengahi Indonesia dan Uni Eropa yang sedang bersengketa dagang. Dalam kacamata hukum internasional, WTO hadir sebagai lembaga yang menerapkan aturan mengikat, sehingga mampu menyelesaikan sengketa perdagangan secara adil. Panel WTO mengkaji Technical Barriers to Trade Agreement dan General Agreement on Tariffs and Trade 1994, sebagaimana inti dari keberatan Indonesia. Hasil panel menunjukkan bahwa beberapa kebijakan Uni Eropa melanggar prinsip non diskriminasi, sementara sebagian klaim Indonesia tidak berhasil dibuktikan. Artikel ini juga meninjau implikasi keputusan tersebut melalui perspektif teori keunggulan komparatif, yang mendukung hak Indonesia untuk memanfaatkan keunggulan alamnya dalam memproduksi kelapa sawit secara efisien. Keputusan WTO ini diharapkan dapat mendorong Uni Eropa memperbaiki kebijakannya dan memperkuat posisi Indonesia dalam perdagangan global.
Impact of Industry4.0/ICTs, Lean Six Sigma and quality management systems on organisational performance

PurposeThis paper compares impact of Industry 4.0 / emerging information and communication Technologies (ICTs), for example, Internet of things (IOT), machine learning, artificial intelligence (AI), robotics and cloud computing, on 22 organisational performance indicators under nine combinations of Lean Six Sigma (LSS) and quality management systems (QMS).Design/methodology/approachSurvey of 105 Indian organisations was done about their experience of using QMS, Lean Six Sigma and emerging ICTs. Respondents included both manufacturing and service enterprises of different scales and sectors. The responses collected were compared, and statistically significant difference among them was evaluated using chi-square test.FindingsThe study confirmed statistically significant difference among 20 organisational performance indicators under different combinations of QMS, LSS and ICTs. These indicators include quality performance, delivery performance, sales turnover, inventory level and so forth. However, for two indicators, namely, absenteeism and throughput, significant difference in responses was not established.Research limitations/implicationsAll possible combinations of QMS, LSS, only LSS tools and ICTs were not studied because of either theoretical impossibility (e.g. using LSS without LSS tools) or practically rare situations (e.g. organisations using ICTs and LSS without QMS). Furthermore, the impact from different sequences of implementing QMS, LSS and ICTs can be studied.Practical implicationsUsing this study, practitioners can identify which LSS, Quality System and ICT combination results in best performance and quick success. On theoretical front, the study confirms impact of LSS and QMS on organisational performance.Originality/valueThis study evaluates organisational performance under several possible combinations of QMS, LSS, and emerging ICTs, which was so far unexplored.
Simulation and Optimization of Cargo Transportation Process in Logistic Transportation System

Network model and algorithm for the solution of optimization problem concerning cargo transportation process within multi-terminal network in accordance with several efficiency criteria of transportation system operation have been constructed and elaborated in the paper.The paper gives the algorithm that makes it possible to trace routes and cyclic routes of maximum carrying capacity of a transportation flow.
Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms

Online interactive recommender systems strive to promptly suggest to consumers appropriate items (e.g., movies, news articles) according to the current context including both the consumer and item content information. However, such context information is often unavailable in practice for the recommendation, where only the users' interaction data on items can be utilized. Moreover, the lack of interaction records, especially for new users and items, worsens the performance of recommendation further. To address these issues, collaborative filtering (CF), one of the recommendation techniques relying on the interaction data only, as well as the online multi-armed bandit mechanisms, capable of achieving the balance between exploitation and exploration, are adopted in the online interactive recommendation settings, by assuming independent items (i.e., arms). Nonetheless, the assumption rarely holds in reality, since the real-world items tend to be correlated with each other (e.g., two articles with similar topics). In this paper, we study online interactive collaborative filtering problems by considering the dependencies among items. We explicitly formulate the item dependencies as the clusters on arms, where the arms within a single cluster share the similar latent topics. In light of the topic modeling techniques, we come up with a generative model to generate the items from their underlying topics. Furthermore, an efficient online algorithm based on particle learning is developed for inferring both latent parameters and states of our model. Additionally, our inferred model can be naturally integrated with existing multi-armed selection strategies in the online interactive collaborating setting. Empirical studies on two real-world applications, online recommendations of movies and news, demonstrate both the effectiveness and efficiency of the proposed approach.
Pedagogical Alignment of Large Language Models

Large Language Models (LLMs), when used in educational settings without pedagogical fine-tuning, often provide immediate answers rather than guiding students through the problem-solving process. This approach falls short of pedagogically best practices and limits their effectiveness as educational tools. We term the objective of training LLMs to emulate effective teaching strategies as `pedagogical alignment.' In this paper, we investigate Learning from Human Preferences (LHP) algorithms to achieve this alignment objective. A key challenge in this process is the scarcity of high-quality preference datasets to guide the alignment. To address this, we propose a novel approach for constructing a large-scale dataset using synthetic data generation techniques, eliminating the need for time-consuming and costly manual annotation. Leveraging this dataset, our experiments with Llama and Mistral models demonstrate that LHP methods outperform standard supervised fine-tuning (SFT), improving pedagogical alignment accuracy by 13.1% and 8.7% respectively. Existing evaluation methods also lack quantitative metrics to adequately measure the pedagogical alignment of LLMs. To address this gap, we propose novel perplexity-based metrics that quantify LLMs' tendency to provide scaffolded guidance versus direct answers, offering a robust measure of pedagogical alignment. Our analysis provides compelling evidence for the superiority of LHP methods over SFT in optimizing LLMs' behavior, underscoring the potential of LHP methods in better aligning LLMs with educational objectives and fostering effective learning experiences. Code and models are available \href{https://github.com/luffycodes/Tutorbot-Spock}{here}.
Error-corrected phase estimation averaged over variable grids on a trapped-ion quantum computer: hyperacuity spectra of a CO molecule adsorbed onto $χ$-Fe$_5$C$_2$

Quantum phase estimation (QPE) is an underlying technology for extracting the excitation spectra of many-electron systems, yet its practical use on current hardware is hindered by low grid resolution and environmental noises. Here we propose QPE averaged over variable grids (QAVG), a vernier-type approach that combines low-resolution QPE with multiple origin shifts and physically motivated continuous parametrization to reconstruct the spectra accurately. We introduce this approach into an end-to-end workflow for the {\it ab initio}-based model system for a CO molecule adsorbed onto the $χ$-Fe$_5$C$_2$ surface. We perform experiments on Quantinuum H2-2 using both physical QPE circuits and logical QPE circuits encoded in the Steane code with offline bit-flip correction. We demonstrate that QAVG accurately reconstructs the spectra with deviations much smaller than the nominal QPE resolution, even when the noisy histograms are used. The cost landscapes averaged over the shifted grids substantially suppress the local minima arising from the spectral leakage, thereby stabilizing the optimization of trial parameters. These results indicate that QAVG provides a robust route to quantum simulations of correlated spectra toward the era of early-fault-tolerant quantum computers.
Understanding the Kinetics, Stereoselectivity, and Catalysis of Pericyclic Reactions in Chemistry and Biology through Quantum Mechanical Computations

Pericyclic reactions are important class of chemical reaction because they occur stereospecifically often with a high degree of regio-, stereoselectivity, and atom economy. This dissertation summarizes our efforts – using chemical computations – to better understand how these reactions occur in both biological and abiological settings.This dissertation is divided into two portions; the first of which describes quantum mechanical studies of biosynthetic and enzyme-catalyzed cycloadditions. Chapter 1, 2, and 3 detail studies of these enzymatic processes. Using state-of-the-art density functional theory, we have determined whether these reactions are feasible biosynthetic transformations, elucidated the mechanistic subtleties of these reactions, and proposed how enzymes may catalyze such transformations. Chapter 1 describes a computational study of a strained biosynthetic transannular 1,3-dipolar cycloaddition. Computations reveal that substrate preorganization overrides distortion in the transitions, resulting in a reaction is feasible. Efforts to account for the influence of water – through solvent-solute hydrogen-bonding interactions – were modeled using “mircosolvated” reactant and transition states: We conclude that the reaction remains feasible in aqueous media. Strategic hydrogen bonding, according to theory, can accelerate the reaction by 2000-fold. Chapter 2 summarizes a computational investigation of the Diels-Alder reaction involved in the biosynthesis of spinosyn A. We find that the mechanism of this transannular cycloaddition is “ambimodal”, proceeding through a “bis-pericyclic” transition structure that leads directly two products, the observed Diels-Alder adduct and an unstable [6+4] adduct. This [6+4] adduct can readily and irreversibly rearrange into the Diels-Alder adduct via a Cope rearrangement. Simulations of the reaction mechanism determine that the nonenzymatic cycloaddition occurs predominantly via a mechanism that involves the intermediacy of the [6+4] adduct. Chapter 3 discusses an ongoing ...
EARLY WARNING SYSTEMS HELPs DISASTER RISK REDUCTION

Climate change is one of the most important issues being discussed around the world, as it has local and regional impacts that deeply affect communities. Early Warning System is an adaptive measure for climate change, using integrated communication systems to help communities to be prepared for hazards related to extreme weather events. A successful EWS saves lives, jobs, land, infrastructure and supports long-term sustainability. EWS help public officials and administrators in their planning, saving money in the long run and protecting economies. The UN, working in various partnerships, has introduced a number of innovative early warning systems initiatives in vulnerable areas around the world. Risks are not mutually exclusive. Several hazards, i.e. a cyclone and a pandemic, can occur at the same time or one after the other. When this happens the effects of one disaster can make it even more difficult for communities to respond to the next one. As Rosalind Cook, external relations officer at the United Nations Office for Disaster Risk Reduction (UNDRR) said, “We need to shift from a hazard by hazard to a multi-hazard approach. We need a comprehensive holistic approach looking at the long term based on data, and for that, we need to improve our metrics for risks.” Adaptation solutions need to be inclusive. This means they have to respond to the specific needs of all impacted stakeholders, taking into account that groups will have varying needs. To achieve this, it is important to reflect on why and how different groups are vulnerable and empower them to make active decisions regarding their resilience. “Inclusive solutions should be crosscutting, involving women, youth, migrants, smallbusinesses and people with disabilities,” said Krishnan Narasimhan, a programme manager at the United Nations Capital Development Fund (UNCDF). “Empowering the beneficiaries with measures is a key step to build resilience. Building the financial capacity and competency of end-users, for example, is key to the sustainable use of financial products.” This study will prove the importance of early warning systems in our country, Albania. Adequate early warning systems for floods, droughts, heat waves, storms or other disasters enable planning to minimize negative impacts. Extreme weather cannot be prevented, but countries and international organizations must take care to mitigate the consequences of natural disasters as much as possible.
Efficient computations of encodings for quantum error correction

16 pages, REVTeX, 3 figures within the text
Graph Signal Reconstruction Techniques for IoT Air Pollution Monitoring Platforms

Air pollution monitoring platforms play a very important role in preventing and mitigating the effects of pollution. Recent advances in the field of graph signal processing have made it possible to describe and analyze air pollution monitoring networks using graphs. One of the main applications is the reconstruction of the measured signal in a graph using a subset of sensors. Reconstructing the signal using information from sensor neighbors can help improve the quality of network data, examples are filling in missing data with correlated neighboring nodes, or correcting a drifting sensor with neighboring sensors that are more accurate. This paper compares the use of various types of graph signal reconstruction methods applied to real data sets of Spanish air pollution reference stations. The methods considered are Laplacian interpolation, graph signal processing low-pass based graph signal reconstruction, and kernel-based graph signal reconstruction, and are compared on actual air pollution data sets measuring O3, NO2, and PM10. The ability of the methods to reconstruct the signal of a pollutant is shown, as well as the computational cost of this reconstruction. The results indicate the superiority of methods based on kernel-based graph signal reconstruction, as well as the difficulties of the methods to scale in an air pollution monitoring network with a large number of low-cost sensors. However, we show that scalability can be overcome with simple methods, such as partitioning the network using a clustering algorithm.