ethicalabs/Research-Intent-Collab huggingface dataset:
The Acceptance of Human Resource Information System (HRIS) Usage for Small and Medium-sized Enterprises (SMEs) in Bangkok and Surrounding Area

This research aimed to: 1) investigate the levels of opinion on factors influencing technology acceptance, 2) analyze the relationships between these acceptance variables, and 3) test the goodness-of-fit of a causal relationship model for the adoption of Human Resource Information Systems (HRIS) in Bangkok and its surrounding areas. A mixed methods approach, combining quantitative and qualitative methodologies, was employed. For the quantitative component, a survey was administered to a sample of 500 entrepreneurs from Small and Medium-sized Enterprises (SMEs) via online questionnaires. The collected data were analyzed using descriptive statistics and Structural Equation Modeling (SEM) with AMOS software. For the qualitative component, semi-structured, in-depth interviews were conducted with 30 key informants. The findings revealed that the sample group held a highly positive opinion regarding the use of ready-made software. The factors with the most significant weights were effectiveness (0.935), perceived ease of use (0.945), intention to use (0.935), and usage behavior (0.935). Users perceived that the system helped reduce costs, save time, minimize errors, and enhance decision-making accuracy, while also effectively supporting future analysis and planning. Furthermore, the model's fit with the empirical data was deemed acceptable across all measured indices (CMIN/df = 1.977, CFI = .984, NFI = .968, TLI = .982, RMSEA = .044). Hypothesis testing confirmed that all proposed hypotheses were supported, indicating the model's suitability in explaining the acceptance behavior of HRIS within the context of SMEs in the studied region. These findings contribute to the HRIS adoption literature by providing an empirically validated model that integrates system effectiveness with traditional technology acceptance factors, specifically tailored to the unique operational context of SMEs in an emerging urban economy.
Contrastive representation learning in recommendation systems--The investigation of the performance of the self-supervised learning in large-scale recommendation systems

Self-supervised learning (SSL) has been proposed in machine learning projects for its convenience of reducing self-labeled datasets in recent years. However, the implementation of SSL in large-scale recommendation systems has lagged behind the evolution because of their scarce and tailed characteristics. In 2021, an article proposed the use of SSL in recommendation systems to pursue an improvement in the performance of recommender models. This article is built on this previous investigation and aims to further explore the role of SSL in recommendation systems and to investigate an improvement of the model’s efficiency. To answer research questions, this paper tests three models with different numbers of towers to discover the best performance of the use of SSL in recommender models. Consequently, it is found that implementing SSL on the item side only (two-tower DNNs) produced the best result. Then, when constructing the two-tower DNNs model, this article examines different numbers of negative pairs to change the InfoNCE loss to investigate a tradeoff between the number of positive and negative samples in the performance of the model. As a result, it turns out to be a weak correlation between this ratio and the performance; Hence, it is concluded that the change of the number of positive and negative samples would not necessarily affect the two-tower DNNs model. In our experiential stage, this paper uses a real-world dataset with 100k training samples to testify and compare our results.
ChemInform Abstract: Electrochemistry of Chemically Lithiated NaV<sub>3</sub>O<sub>8</sub>: A Positive Electrode Material for Use in Rechargeable Lithium‐Ion Batteries.

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.
Logistics, Inventory Control, and Supply Chain Management

Many argue that the focus point (and perhaps the linchpin) of successful supply chain management is inventories and inventory control. So how do food and agribusiness companies manage their inventories? What factors drive inventory costs? When might it make sense to keep larger inventories? Why were food companies quicker to pursue inventory reduction strategies than agribusiness firms? In 1992, some food manufacturers and grocers formed Efficient Consumer Response to shift their focus from controlling logistical costs to examining supply chains (King & Phumpiu, 1996). Customer service also became a key competitive differentiation point for companies focused on value creation for end consumers. In such an environment, firms hold inventory for two main reasons, to reduce costs and to improve customer service. The motivation for each differs as firms balance the problem of having too much inventory (which can lead to high costs) versus having too little inventory (which can lead to lost sales). A common perception and experience is that supply chain management leads to cost savings, largely through reductions in inventory. Inventory costs have fallen by about 60% since 1982, while transportation costs have fallen by 20% (Wilson, 2004). Such cost savings have led many to pursue inventory-reduction strategies in the supply chain. To develop the most effective logistical strategy, a firm must understand the nature of product demand, inventory costs, and supply chain capabilities. Firms use one of three general approaches to manage inventory. First, most retailers use an inventory control approach, monitoring inventory levels by item. Second, manufacturers are typically more concerned with production scheduling and use flow management to manage inventories. Third, a number of firms (for the most part those processing raw materials or in extractive industries) do not actively manage inventory. Many agribusiness firms do not actively manage inventory. This does not mean that they ignore inventory. Rather, they hold large inventories because any potential savings from inventory reductions are far outweighed by the inventory-induced reductions in production, procurement, or transportation costs. Often economies of size cause long productions runs which lead to inventory accumulation. Simultaneously, seasonality leads to inventory buildups of key inputs like seed as well as outputs like corn. Economies in procurement such as forward buying in the food industry and quantity discounts increase inventories. Similarly, unit trains and other forms of bulk shipping discounts contribute to inventory buildups. Yet, such firms must be alert to changing conditions that may require more exact inventory management. One example would be if crops are marketed as small lots of value-added grain instead of commodities. Production proliferation in the seed industry may be another instance. Finally, whether due to food safety concerns, GMOs, food labeling, or the growth of organic food markets, identity preservation requires more precise inventory control.
Investigation of urology intraoperative events leading to root cause analysis at national VA medical centers.

Adverse events in urologic procedures are poorly studied. This study analyzes the Veterans Health Administration (VHA) Root Cause Analysis (RCA) data for patient safety adverse events during urologic procedures performed in a VHA operating room (OR).The VHA National Center for Patient Safety RCA database was queried for fiscal years 2015-2019 using urologic terms including vasectomy, prostatectomy, nephrectomy, cystectomy, cystoscopy, lithotripsy, ureteroscopy, urethral, TURBT, etc. RCAs for events outside a VHA OR were excluded. Cases were categorized based on type of event.Sixty-eight RCAs were identified for 319,713 urologic procedures. The most common pattern identified was equipment or instrument issue, including broken scopes or smoking light cords, with 22 cases. Eighteen RCAs involved a sentinel event, including 12 retained surgical items (RSI) (surgical sponge, retained guidewire) and 6 wrong site surgeries (WSS) (incorrect laterality, wrong procedure) representing a serious safety event rate of 1 in 17,762 procedures. In addition, 8 RCAs pertained to medical or anesthesia events (incorrect dosing, postoperative myocardial infarction), 7 to pathology errors (missing or mislabeled specimen), 4 to incorrect patient information or consent, and 4 to surgical complications (hemorrhage, duodenal injury). In 2 cases there was inappropriate work up. One case caused a delay in treatment, one case had an incorrect count, and one case identified lack of credentialing.RCAs of patient safety adverse events occurring during urologic OR procedures highlight the need for targeted quality improvement projects to prevent WSS events, prevent RSI events, and maintain properly functioning equipment.
Platial Information Fusion: Integrating Natural Language Processing and Graph Neural Networks for Context-Aware Geospatial Knowledge Extraction

Recent advances in geospatial artificial intelligence (GeoAI) have highlighted the potential of combining natural language processing (NLP) and graph-based learning for extracting semantically enriched spatial knowledge. This study describes the development of a lightweight hybrid framework for context-aware platial information that integrates NLP techniques with Graph Neural Networks (GNNs) to extract and analyze geosemantic knowledge from textual and spatial data. The main purpose is to create a platial knowledge graph that represents cities not simply as locations on maps, but as multidimensional entities with spatial, cultural, historical, and social characteristics. The methodology applied is based on three levels. The first level involves the semantic analysis of descriptive texts about 163 Greek cities which contain historical, cultural and social characteristics. The second level consists in the extraction of geographic data, such as the location of cities and other natural features like mountains, rivers, and seacoast obtained from open sources such as OpenStreetMap. The third level involves the construction of a knowledge graph with multidimensional features and relations. More specifically, we construct a diverse platial knowledge graph where cities are represented as nodes connected to other cities based on semantic features derived from both linguistic content and spatial context. By integrating GNNs, we manage to model and predict latent relationships between urban entities, enabling clustering and comparison of cities with similar geographic and semantic profiles. The preceding methodology is applied to a dataset involving large Greek cities to discover whether geosemantic fusion can reveal patterns of urban identity and regional connectivity.  By adopting this methodology, groups of cities that present similar characteristics are identified, without necessarily being geographically adjacent. Indicative examples show that cities sharing similar semantic and environmental characteristics can be grouped even when they are geographically distant. For example, Ioannina and Tripoli can be grouped due to their mountainous character and historical importance, while cities such as Kalamata, Kavala and Rhodes, although geographically dispersed, are correlated through their coastal tourism profile. The study focuses on Greece but provides a generalizable model for the multidimensional conceptualisation of place, which can be extended to other regions or countries. The resulting embeddings and city groupings are evaluated through clustering analysis, enabling the identification of latent similarities in urban identity and regional characteristics. This research is an attempt to expand the scientific approach of Platial Information Science, proposing a flexible and scalable tool for analyzing cities in a way that goes beyond traditional geographic mapping. The results highlight the potential for applications in spatial humanities, cultural geography, and urban planning.
Business ethics and corporate social responsibility : can corporate social responsibility survive in the market economy? : if it does will it be efficient?

Business Ethics and Corporate Social Responsibility (CSR) are a subject of very great present interest. Business ethics is not a special set of ethics, it is normal ethics applied to business and business situations. CSR is a firm’s contribution to the society; it can be anything from helping the poor to reducing CO2 emissions. The questions I try to look closer to in this thesis are the following: If the cost of CSR exceeds the profit it generates to the firm, will CSR be able to survive in the market economy? If CSR survives, is it efficient to the society as a whole? I start in chapter one by defining Business Ethics and CSR, and introducing the debate around CSR. Some economists like Friedman and Shleifer are sceptical to CSR. They argue that CSR is a cost to the firm and will make a firm less competitive. While others like Porter and Kramer argue that CSR can help increase the competitiveness of a firm and chosen right CSR increase the profit of the business. In chapter two I look at ethical pressure from NGOs, activists and the government in a competitive market and chose to elaborate one example of unethical behaviour: Child labour. Chapter three is my simple economic illustration, in the first part I assume that the ethical standard is absolute and in the second part I assume that it is variable. My simple model shows that when an ethical standard is imposed the companies that can not bear this cost will go bankrupt. Sometimes the society’s punishment is not equal to all firms and can be either smaller or bigger than the cost of implementing the ethical standard. In this case, the results may be socially inefficient because some firms will be able to survive despite that their income is inferior to the cost of ethics and others will not survive despite that their income is greater than the income of the ones surviving. The model also shows a correlation between high moral standard and productivity but this should not be misinterpreted. A moral standard is a cost to the firm, but the correlation is due to the fact that productive firms can bear higher ethical cost. The conclusion is presented in chapter four.
Persistent Homology : A Modern Application of Algebraic Topology in Data Analysis

Topological data analysis emerged as a field in the 2000s and has proven very useful for examining the shape of data sets. Using persistent homology as their main methodology researchers has succesfully applied the theory presented in this paper to study as varied subjects as robot motion, brain connectivity, network theory, finger print analysis and computer vision.  The mathematical theory behind persistent homology has traditionally required training far beyond what an average engineer would have. Therefore much theory is usually left out of presentations meant for an audience outside of a mathematics department. This paper contains a novel approach to the presentation of this theory, maintaining mathematical rigour while only using linear algebra as its building blocks.
Sovereignty and Human Rights in Africa: Analysing Tanzania’s withdrawal from The African Court on Human and Peoples’ Rights

As regional human rights courts have become increasingly crucial in promoting and protecting human rights, a troubling trend has emerged: states withdrawing from these courts under the name of state sovereignty. Such a phenomenon threatens to undermine the very fabric of regional human rights protection, leaving vulnerable individuals without a remedial mechanism to address violations of their fundamental rights. This paper undertakes a critical examination of the complex context surrounding Tanzania’s withdrawal from the African Court on Human and Peoples’ Rights, addressing its legality and potential legal repercussions. This paper does so by comparing the European and Inter-American human rights systems. This article aims to shed light on the darker side of state sovereignty and its impact on the protection of human rights. Through this in-depth analysis, this paper aims to uncover the root causes of state withdrawal from regional human rights courts and provide actionable recommendations for promoting and protecting human rights at the regional level.
PyThaiNLP: Thai Natural Language Processing in Python

We present PyThaiNLP, a free and open-source natural language processing (NLP) library for Thai language implemented in Python. It provides a wide range of software, models, and datasets for Thai language. We first provide a brief historical context of tools for Thai language prior to the development of PyThaiNLP. We then outline the functionalities it provided as well as datasets and pre-trained language models. We later summarize its development milestones and discuss our experience during its development. We conclude by demonstrating how industrial and research communities utilize PyThaiNLP in their work. The library is freely available at https://github.com/pythainlp/pythainlp.
Exploring the potentials of genetically modified organisms for achieving food security and sustainable agriculture: The dilemma of African countries

Genetically Modified Organisms (GMOs), which often carry genes from bacteria or virus, have started taking root in Africa. Since the advent of this technology, arguments have been raging between the proponents of GMOs, who believe it has a lot of benefits and can lead to food security in the developing world, and the opponents who are against it. Africa is the second largest and second most populous continent on earth with a projected population of about 2.4 billion by 2050. Certainly, the continent needs to find ways to increase food production in order to solve the problems of food shortage, malnutrition and poverty. Even though agriculture is the most economically important activity providing employment for about two-thirds of the continent’s population, it remains at subsistence level with serious challenges, ranging from lack of sophisticated farming tools to insufficient or high cost of fertilizer, inadequate improved seeds, poor soils and poor irrigation facilities, postharvest losses, diseases and pests. The countries have been experimenting with different agricultural programmes and policies, which have not made significant impact on sustainable agricultural development. Can GMOs provide a solution for Africa? This paper explores the potentials of GMOs in achieving food security, the dilemma and implications for Africa.
Accountability, anti-corruption, and transparency policies in Public owned enterprises (POEs): The case of Italy

The paper depicts, in the Italian case, the interconnections between two themes: bribery and anti-corruption policies, on the one hand, and the role of public controlled entities, on the other. Italy turns out to be an interesting case study because, on the one hand, it is among the worst performing countries in the G7 and the EU members in terms of corruption and, on the other, it has experienced a strongly proliferation of Public owned enterprises (POEs) during the last two or three decades, involving nearly all activity sec-tors of the economy. This process is deemed to have weakened the chain of control on shared units, delegating relevant financial and political decisions to a milieu of ambiguity and uncertain accountability (the ‘escape' argument). A progressive change of direction occurred more recently, when corporatized public enterprises have been submitted to specific and increasing limits, including transparency and anti-corruption policies. The anticorruption package, adopted in Italy starting with 2012, has promoted important steps in this direction, yet the effects of these new measures have not been resolutive, and many challenges are still open.
Statistical modeling and optimization of process parameters for additive manufacturing of styrene–ethylene–butylene–styrene block copolymer parts using solvent cast                     <scp>3D</scp>                     printing technique

Abstract                                                             Additive manufacturing of thermoplastic elastomers is challenging using fused deposition modeling due to their high melt viscosity, low column strength, and poor fusion among layers. Solvent‐cast 3D printing (SC‐3DP) is an efficient alternative to successfully 3D print such materials. However, selection of suitable 3D printing parameters is crucial to realize parts with optimum physicomechanical properties. In this work, statistical modeling and SC‐3DP parameter optimization for styrene–ethylene–butylene–styrene (SEBS) block copolymer were performed. The effect of 3D printing process parameters on shrinkage, relative density, and tensile strength was analyzed using response surface methodology. Experiments were planned as per central composite design and analysis of variance was performed to evaluate the significant parameters. SEBS content in the polymer solution significantly affected the shrinkage of SC‐3DP samples. Moreover, relative density and tensile strength were significantly affected by print speed and layer height. A significant interaction between print speed and layer height was also noticed for tensile strength and relative density of printed samples. Multi‐objective optimization using genetic algorithm was also performed to minimize shrinkage and maximize relative density and tensile strength. Finally, a case study was conducted comparing the physicomechanical properties of SC‐3DP samples printed at optimized process parameters and compression molded samples.                                                           Highlights                                                                                                Statistical models were developed using response surface methodology.                                                                             Genetic algorithm based multi‐objective optimization was performed.                                                                             Optimum solvent‐cast 3D printing (SC‐3DP) process parameters were determined.