ethicalabs/Research-Intent-Collab huggingface dataset:
Optimizing Transportation Logistics through Enterprise Architecture: A Case Study of Integrated Management Systems

This abstract captures the essence of optimizing transportation logistics through Enterprise Architecture (EA) by presenting an Integrated Management Systems (IMS)-centered case study. Effective logistics management is crucial for enhancing operational effectiveness and attaining competitive advantages in today's rapidly changing business environment. Achieving this optimization, however, requires a comprehensive strategy that harmonizes disparate aspects such as operational processes, data administration, and technological infrastructure. This study demonstrates that Enterprise Architecture has the potential to function as the link between these domains. This study examines the difficulty of dispersed logistics operations and the resulting inefficiencies, cost escalation, and compromised responsiveness. The case study is presented as Integrated Management Systems powered by Enterprise Architecture principles. This integration intends to optimize workflows, encourage cross-functional collaboration, and provide real-time visibility into logistical activities. This investigation demonstrates how a well-designed EA framework can effectively mitigate transportation logistics and supply chain management disparities. By analyzing the results of the case study, this study aims to identify the benefits and challenges of implementing Enterprise Architecture within the realm of transportation logistics. This study explains how EA can improve operational efficiency, resource utilization, lead time, and customer satisfaction. Therefore, the research contributes to a broader comprehension of how Enterprise Architecture can optimally resolve the complexities inherent to contemporary transportation logistics. The potential for increased operational agility and competitiveness in the logistics industry is highlighted by applying EA principles to the context of integrated management systems.
Investigating Vulnerability, Adaptation, and Resilience: A Comprehensive Review within the Context of Climate Change

This review seeks to enhance the understanding of the critical concepts of vulnerability, adaptation, and resilience within the context of global environmental challenges, with a particular focus on climate change. Climate change is characterized by rising global temperatures and an increase in extreme weather events, making the comprehension and addressing of these concepts crucial for effective adaptation strategies. Despite widespread recognition of the interconnectedness of vulnerability, adaptation, and resilience, there remains a gap in a comprehensive understanding of how these concepts interrelate. Through synthesizing existing literature, this review provides a detailed examination of their definitions and the interrelationships among vulnerability, adaptation, resilience, and climate-related disasters. Additionally, it explores the impact of climate change on future disaster risk reduction efforts by analyzing the nexus between climate change adaptation and disaster risk reduction. Key findings highlight the necessity of incorporating social, institutional, economic, and environmental factors into adaptation planning and call for innovative approaches to boost adaptive capacity and resilience. This review not only furthers the discourse in research, policy, and practice in this vital area but also offers strategic insights for developing more resilient and adaptive societies amidst the challenges posed by climate change.
There is a responsibility to protect in Yemen : Qualitative Content Analysis of the United Nations Security Council’s approach to the situation in Yemen

Tämän pro gradu -työn tavoitteena on tunnistaa ja analysoida Yhdistyneiden Kansakuntien turvallisuusneuvoston (UNSC) suojeluvastuun periaatteen (R2P) mukaiset toimet, joita se on käyttänyt Jemenin kriisin ratkaisemiseen ajanjaksolla 2011 – 2018, inhimillisen turvallisuuden viitekehyksen avulla, joka priorisoi yksilöä valtion sijasta. Metodina käytetään laadullista sisällönanalyysiä, ja datan koostuu YK:n turvallisuusneuvoston dokumenteista ja julkilausumista, joista suojeluvastuun periaatteen mukaisia toimia etsitään. R2P rinnastetaan usein virheellisesti sotilaallisen voiman käyttöön, ja tästä johtuen on esitetty väitteitä, että kansainvälinen yhteisö on sivuuttanut Jemenin kriisin. Kaiken lisäksi Jemenin kriisin käsittely mediassa on huomattavasti vähäisempää kuin alueen muiden kriisien käsittely. Vaikka inhimillinen turvallisuus on Jemenissä täysin puutteellinen, olisi harhaanjohtavaa ja epäoikeudenmukaista väittää, että kansainvälinen yhteisö olisi jättänyt Jemenin huomiotta. Tämä pro gradu -työ pyrkii osoittamaan, että kansainvälinen yhteisö on toiminut Jemenissä suojeluvastuun periaatteen mukaisesti. Suojeluvastuun periaatteen toteuttaminen on Jemenissä myös aiheellista, sillä on syytä epäillä, että kaikki konfliktin osapuolet ovat syyllistyneet törkeisiin ihmisoikeusrikkomuksiin, mukaan lukien mahdollisiin sotarikoksiin ja rikoksiin ihmisyyttä vastaan. YK:n turvallisuusneuvosto on toteuttanut Jemenin tapauksen käsittelyssä suojeluvastuun periaatteen pilareita II ja III, jotka vaativat turvallisuusneuvoston valtuutuksen. Näihin toimiin lukeutuvat useat siirtymäkauden aikana (2011 – 2014) neuvotellut sopimukset, kaksi eri selvitysvaltuuskuntaa, sanktioita ja rauhanneuvotteluyrityksiä. Nämä toimet jo osoittavat, että kansainvälinen yhteisö on toiminut edistääkseen Jemenin konfliktin ratkaisemista. Vaikka kriisiä ei ole saatu ratkaistua, se ei silti tarkoita sitä, etteikö tätä olisi yritetty. On kuitenkin huomattava, että Jemenin konflikti on vaikea ratkaistava sen vaikeaselkoisuuden, monitahoisuuden ja osaltaan myös YK:n turvallisuusneuvoston kokoonpanon vuoksi. Jemenin kriisin, kuten minkä tahansa muunkin konfliktin ratkaisu, joka uhkaa kansainvälistä rauhaa ja turvallisuutta, vaatii suunnattomasti poliittista tahtoa paikallisesti, alueellisesti ja kansainvälisesti.
The Labeninae (Hymenoptera: Ichneumonidae): a study in phylogenetic reconstruction and evolutionary biology

Abstract   The internal phylogenetic relationships of the ichneumonid subfamily Labeninae (Hymenoptera) are re-assessed using 136 characters for 54 species representing all currently known species-groups. New character sets are used. These include a new form of seta (named the grossulariform setae) which are present on the inner surface of the hind coxa of some females, and the extensive structural modifications of the terminal segments of the female metasoma, which are described and structural homologies established. The monophyly of the Labeninae and of the four component tribes, Groteini, Poecilocryptini, Xenothyrini and Labenini, is demonstrated. The Groteini is shown to be the sister-group to the Poecilocryptini + (Xenothyrini + Labenini). Monophyletic genera are established, including three new genera  Ozlabium  gen. nov.  Gauld & Wahl (type-species  O. arcadium  sp. nov. ) in the Groteini from Australia  Torquinsha  gen. nov.  Gauld & Wahl (type-species  Certonotus invictus  Porter) in the Labenini from Chile, and  Gauldianus  gen. nov.  Lanfranco (type-species   Gauldianus chilensis   sp. nov.  Lanfranco) in the Labenini from Chile. Apechoneura  is removed from synonymy and re-instated as a valid genus for a complex of tropical American species. The biogeographic patterns shown by the Labeninae are discussed. Evidence suggests that the group radiated on Gondwanaland after the separation of Africa/India/Madagascar, but prior to the separation of Australia. Species occurring off the southern continental fragments, in North America, are shown to be members of highly derived lineages, a result consistent with the hypothesis that labenines colonized North America recently through Mesoamerica. Patterns of host utilization and mating behaviour are examined for the group. Members of the more derived lineages show progressively more female specializations for attacking hosts deeply concealed in woody substrates. Males in the more specialized lineages are shown to have a wider array of secondary sexual characteristics and genitalic ornamentations than more plesiomorphic taxa.
Optimization methods for combined problems in transportation and logistics management

Στην παρούσα διδακτορική διατριβή εξετάζεται και επιλύεται μια συλλογή σύνθετων προβλημάτων επιχειρησιακής έρευνας με εφαρμογές στην διοίκηση μεταφορών και εφοδιαστικής αλυσίδας. Το κύριο χαρακτηριστικό των μοντέλων που μελετώνται είναι ότι ενσωματώνουν τις κλασικές διαδικασίες δρομολόγησης οχημάτων με άλλες διαδικασίες όπως αποφάσεις παραγωγής, έλεγχο αποθέματος και αποφάσεις διανομής. Με άλλα λόγια, αυτά τα μοντέλα ενσωματώνουν τις διαδικασίες λήψης αποφάσεων σε καθημερινό επίπεδο με τις αποφάσεις σε τακτικό επίπεδο, δημιουργώντας με αυτό τον τρόπο προοπτικές εξοικονόμηση κόστους. Τα προβλήματα αυτά προκύπτουν σε πρακτικές εφαρμογές της εφοδιαστικής αλυσίδας, και ως εκ τούτου, έχουν μεγάλη σημασία τόσο για τους επαγγελματίες όσο και για τους ερευνητές. Η παρούσα διατριβή παρουσιάζει ένα φάσμα μεθοδολογικών προσεγγίσεων (ακριβείς, μεταευρετικές, μαθ-ευρετικές) και επίσης, επεκτείνει υπάρχοντα μοντέλα με στόχο τη δημιουργία περισσότερων εφαρμόσιμων και ρεαλιστικών παραλλαγών που αντικατοπτρίζουν καλύτερα το επιχειρηματικό περιβάλλον. Συγκεκριμένα, μελετάται το Πρόβλημα Δρομολόγησης Αποθέματος και το Πρόβλημα Δρομολόγησης Παραγωγής. Διαφορετικές μέθοδοι βελτιστοποίησης ανταπτύσσονται και δοκιμάζονται ξεκινώντας από έναν αλγόριθμο Branch-and-Cut και Tabu Search και φτάνοντας μέχρι έναν υβριδικό αλγόριθμο που ενσωματώνει στοιχεία μαθηματικού προγραμματισμού σε ένα πλαίσιο τοπικό αναζήτησης. Οι προτεινόμενες μεθοδολογίες καταφέρνουν να βελτιώσουν τις καλύτερες γνωστές λύσεις της βιβλιογραφίας για πλήθος προβλημάτων. Επιπλέον, από την άποψη της μοντελοποίησης αναπτύσσονται αποδοτικά μοντέλα μαθηματικού προγραμματισμού, τύπου two-commodity flow, ενώ μια νέα παραλλαγή προβλήματος εισάγεται προς την κατεύθυνση της γεφύρωσης θεωρητικής γνώσης και πράξης.
Patient-specific uncertainty and bias quantification of non-transparent convolutional neural network model through knowledge distillation and Bayesian deep learning

Assessing the reliability of convolutional neural network (CNN)-based CT imaging techniques is critical for reliable deployment in practice. Some evaluation methods exist but require full access to target CNN architecture and training data, something not available for proprietary or commercial algorithms. Moreover, there is a lack of systematic evaluation methods. To address these issues, we propose a patient-specific uncertainty and bias quantification (UNIQ) method that integrates knowledge distillation and Bayesian deep learning. Knowledge distillation creates a transparent CNN ("Student CNN") to approximate the target non-transparent CNN ("Teacher CNN"). Student CNN is built as a Bayesian-deep-learning-based probabilistic CNN that, for each input, always generates statistical distribution of the corresponding outputs, and characterizes predictive mean and two major uncertainties - data and model uncertainty. UNIQ was evaluated using a low-dose CT denoising task. Patient and phantom scans with routine-dose and synthetic quarter-dose were used to create training, validation, and testing sets. To demonstrate, Unet and Resnet were used as backbones of Teacher CNN and Student CNN respectively and were trained using independent training sets. Student Resnet was qualitatively and quantitatively evaluated. The pixel-wise predictive mean, data uncertainty, and model uncertainty from Student Resnet were very similar to the counterparts from Teacher Unet (mean-absolute-error: predictive mean 1.5HU, data uncertainty 1.8HU, model uncertainty 1.3HU; mean 2D correlation coefficient: total uncertainty 0.90, data uncertainty 0.86, model uncertainty 0.83). The proposed UNIQ can potentially systematically characterize the reliability of non-transparent CNN models used in CT.
Bamboo Construction Inspired by Vernacular Techniques for Reducing Carbon Footprint: A Life Cycle Assessment (LCA)

Whilst upcoming innovations on digital technology and renewable energy can have a significant impact on the reduction of operational carbon emissions in the construction industry, readily available fast-growing building materials like bamboo are already proving reductions in the embodied carbon of dwellings above 60% when compared to traditional brickwork in Colombia. This paper presents a like-by-like comparison of the environmental impact of a conventional clay brick house (CBH) and a bamboo house for social housing in Colombia, which was built using adapted vernacular technologies. The bamboo house uses bamboo species Guadua angustifolia Kunth as the main structural support for the light cement bamboo frame (LCBF) system, a.k.a. ‘cemented bahareque’, whilst the CBH combines clay bricks and steel for the load-bearing walls. Traditionally built Guadua angustifolia Kunth bahareque (GaKB) houses are a key part of the vernacular architecture in the ‘coffee cultural landscape of Colombia’ (CCLC) recognised by UNESCO. A life cycle assessment (LCA) was performed to calculate the carbon footprint of the houses following four phases: (1) definition of objective and scope; (2) inventory analysis; (3) impact assessment; and (4) interpretation of results. The results show that the carbon footprint of the GaKB house accounts for about 40% of the CBH, i.e., the GaKB generates a carbon footprint of 107.17 CO2-eq/m2 whilst the CBH results in a carbon footprint of 298.44 kg CO2-eq/m2. Furthermore, from a carbon balance calculation, the carbon footprint of the GaKB house is further reduced to about 36% of the CSB house. LCA results for the built GaKB house demonstrate that vernacular housing projects that preserve cultural heritage can also be resilient and climate-neutral. This paper sets a precedent for the establishment of targeted government policies and industry practices that preserve the cultural heritage and vernacular technologies in the CCLC region and in other emergent economies worldwide whilst promoting future-proof and net-zero carbon construction.
Additional file 1 of TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines

Supplementary Material 1
Patient Safety Climate, Quality of Care, and Intention of Nursing Professionals to Remain in Their Job During the COVID-19 Pandemic

Objective             This study aimed to examine the relationship between patient safety climate, quality of care, and intention of nursing professionals to remain in their job.                                   Methods             A cross-sectional study was carried out in a teaching hospital in Brazil wherein nursing professionals were surveyed. The Brazilian version of the Patient Safety Climate in Healthcare Organizations tool was applied to measure the patient safety climate. Spearman correlation coefficient and multiple linear regression models were applied for the analysis.                                   Results             A high percentage of problematic response was observed for most dimensions, except for fear of shame. Quality of care resulted in a strong correlation with organizational resources for safety and with overall emphasis on patient safety, and the nurse-perceived staffing adequacy was strongly correlated with organizational resources for safety. The multiple linear regression model showed higher scores in quality of care in dimensions related to organizational, work unit, and interpersonal aspects as well as in the adequacy of the number of professionals. A higher score in intention to stay in one’s job was also found in the dimensions of fear of blame and punishment, provision of safe care, and adequacy of the number of professionals.                                   Conclusions             The organizational and work unit aspects can lead to a better perception of the quality of care. Improving interpersonal relationships and increasing the number of professionals on staff were found to increase nurses’ intention to remain in their jobs. Assessing a hospital’s patient safety climate will enable improvement in the provision of safe and harm-free health care assistance.
Analysis of Recognition and Protection of Indigenous Peoples Rights in Land Policy in Indonesia

This study aims to examine the recognition of Indigenous land rights in Indonesia, the challenges faced in their implementation, and provide policy recommendations to strengthen the protection of Indigenous rights in the land sector. Although Indonesia already has regulations that recognize indigenous peoples ' rights, such as the 1945 Constitution and various sectoral laws, implementation of recognition of Indigenous land rights is often hampered by regulatory overlap and conflicts of interest between the forestry, plantation, and mining sectors. In addition, the role of indigenous peoples in policy making is still limited. This study identifies the gap between regulation and field reality, as well as analyzing the social, economic, and environmental implications of the recognition of Indigenous land rights. The results showed the importance of harmonizing regulations, increasing the participation of indigenous peoples, and accelerating the certification of indigenous lands to increase legal certainty. This study suggests multi-stakeholder collaboration as a strategic step to strengthen the position of indigenous peoples in the management of their lands and Natural Resources.
THE CONNECTION BETWEEN MENTAL HEALTH SYMPTOMS AND SOCIAL MEDIA EXPERIENCES IN ADOLESCENCE

ABSTRACT Objectives: Abundant use of social media is linked to young people's internalizing and externalizing mental symptoms. In this study, we examined how internalizing (depression) and externalizing (conduct symptom) symptoms are associated with whether social media produces positive or negative experiences in adolescents. Material and methods: The data was obtained from the Adolescent Mental Health Cohort and Replication study (AMHC) and collected during the academic years 2018–19. The study cohort consisted of 1386 voluntary 9th graders in Tampere, Finland. Depression was measured with R-BDI, conduct symptom with YSR and perceived social support with PSSS-R. Honesty of responding was assessed with a sincerity question, and socioeconomic adversities and age were controlled for. The data were analysed using cross-tabulations with chi-square statistics and logistic regression. Results: Girls reported more positive (79.1%) and negative (10.4%) social media experiences than boys (72.1% and 5.4%). Depression was inversely associated with positive and directly with negative social media experiences in both sexes. Conduct symptom was positively associated with both negative and positive social media experiences in girls but not in boys. Social support from friends and family attenuated the disadvantageous associations between mental health and social media experiences and was linked to more positive and fewer negative experiences. Inclusion of honesty and socioeconomic variables in the models did not alter these findings. Possible pathways between symptom dimensions and social media experiences are discussed. Future research should use longitudinal designs to establish causality.
A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks

The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a Deep Reinforcement Learning (DRL) multi-objective algorithm, called a Double Deep Q-Learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 %
The climatology of Thailand and future climate change projections using the regional climate model precis

The climate of Thailand has not been studied in as much depth as in other parts of continental Southeast Asia. The baseline climate of Thailand during 1961-1990 is first analysed using daily observational data from five surface stations, each representing a different region of Thailand, supplemented by the high resolution 0.5° monthly gridded observational dataset, CRUTS2.1. The latter leads to a deeper understanding of the spatial variation in seasonal cycles of key climate variables in Thailand. Also revealed is an increase in the number of tropical depressions crossing Thailand during La Niña years. It was found that there is a statistically significant intensification (reduction) of precipitation during La Niña (El Niño) years at Surat Thani (Chiang Mai) in southern (northern) Thailand during ON (JJAS). This work facilitates the Regional Climate Model validation work which follows. The Providing REgional Climates for Impact Studies regional climate model, PRECIS, was run for the first time over Southeast Asia to specifically study the climate of Thailand. The first phase is model validation during the 1961-1990 baseline period. An ensemble of RCM runs is undertaken to study the sensitivity to the driving GCM. The added value provided by PRECIS in comparison to the coarser driving models is discussed. The possible causes of model bias are investigated. The model projections for the end of this century are undertaken based on high (SRESA2) and low (SRES-B2) emission scenarios which estimate the range of possible climate change in Thailand. These RCM simulations suggest trends in temperature that are broadly in line with those reported by IPCC. PRECIS A2 and B2 simulations mostly produce small precipitation increases in JJAS and small precipitation increases (decreases) during DJF under the A2 (B2) scenario. Wet season precipitation increases appear to be related to higher rain intensity on fewer rain days.