Suitable Actions Research Articles

Spatial-temporal Data Model for Indoor Fire Response Considering Multi-Factors

Abstract. The complexity of high-rise building structures and functions significantly increases the challenges in emergency responses to indoor fire incidents. This complexity is due to multiple factors that must be evaluated for decision-making within the fire scenario, such as the entities within the building, the status of environment, and the actions of people. An effective and precise indoor fire emergency management model should fully take into consideration of multiple factors, aiming to ensure the safety and effectiveness of emergency response action.This paper proposes a spatial-temporal data model to integrate the entity-status-action, for designing the most suitable action strategies in various environmental status by considering all related entities in the indoor fire scenario. Firstly, a list of factors is designed to represent the scenario features including spatial location, semantic description, attribute characteristics, geometric form, evolutionary process, and the relationships among objects. Then, employs an ontology-based descriptive method to integrate multi-dimensions and effectively represents the interrelations among entities, actions, and environmental status within the context of an indoor fire scenario. These environmental status considered in the model focus on the supporting path-planning during execution of direct response actions. Finally, a set of path-planning rules has been developed to infer a series of environmental status supporting to formulate the optimal evacuation strategy to minimize the risk of evacuation failure. The case study shows that the proposed model is effective in unified expression for multitude of factors associated with emergency response in indoor fire scenario and supporting the decision-making of the emergency response actions.

Digital transformation technologies for conveyor belts predictive maintenance: a review

The availability of condition-monitoring data has increased due to internet of things (IoT) technologies, providing information on various parameters like vibration, temperature, current, and voltage. Cloud computing and big data facilitate the prevention of failures and estimation of remaining useful life through advanced mathematical models and artificial intelligence (AI) techniques. These enable prompt and suitable maintenance actions. This article conducts a systematic review of digital transformation technologies, including cloud computing, edge computing, AI, machine learning (ML), and TinyML, in predictive maintenance for conveyor belt systems. This article reviews how these digital transformation technologies improve predictive maintenance strategies for conveyor belts. The systematic review summarizes the results and challenges of various methodologies used in conveyor belt systems and suggests areas for further research. This paper aimed to serve as a useful resource for researchers, practitioners, and industry professionals seeking insights into current predictive maintenance technologies for conveyor belt systems. The takeaways of the review are expected to ignite discussion on efficient and proactive maintenance strategies and promote the development of innovative solutions for ensuring the reliability and longevity of conveyor belt systems in the digital era.

Efficient autonomous navigation for mobile robots using machine learning

The ability to navigate autonomously from the start to its final goal is the crucial key to mobile robots. To ensure complete navigation, it is mandatory to do heavy programming since this task is composed of several subtasks such as path planning, localization, and obstacle avoidance. This paper simplifies this heavy process by making the robot more intelligent. The robot will acquire the navigation policy from an expert in navigation using machine learning. We used the expert A*, which is characterized by generating an optimal trajectory. In the context of robotics, learning from demonstration (LFD) will allow robots, in general, to acquire new skills by imitating the behavior of an expert. The expert will navigate in different environments, and our robot will try to learn its navigation strategy by linking states and suitable actions taken. We find that our robot acquires the navigation policy given by A* very well. Several tests were simulated with environments of different complexity and obstacle distributions to evaluate the flexibility and efficiency of the proposed strategies. The experimental results demonstrate the reliability and effectiveness of the proposed method.

European Respiratory Society statement on preschool wheezing disorders: updated definitions, knowledge gaps and proposed future research directions.

Since the publication of the European Respiratory Society (ERS) task force reports on the management of preschool wheezing in 2008 and 2014, a large body of evidence has accumulated suggesting that the clinical phenotypes that were proposed (episodic (viral) wheezing and multiple-trigger wheezing) do not relate to underlying airway pathology and may not help determine response to treatment. Specifically, using clinical phenotypes alone may no longer be appropriate, and new approaches that can be used to inform clinical care are needed for future research. This ERS task force reviewed the literature published after 2008 related to preschool wheezing and has suggested that the criteria used to define wheezing disorders in preschool children should include age of diagnosis (0 to <6 years), confirmation of wheezing on at least one occasion, and more than one episode of wheezing ever. Furthermore, diagnosis and management may be improved by identifying treatable traits, including inflammatory biomarkers (blood eosinophils, aeroallergen sensitisation) associated with type-2 immunity and differential response to inhaled corticosteroids, lung function parameters and airway infection. However, more comprehensive use of biomarkers/treatable traits in predicting the response to treatment requires prospective validation. There is evidence that specific genetic traits may help guide management, but these must be adequately tested. In addition, the task force identified an absence of caregiver-reported outcomes, caregiver/self-management options and features that should prompt specialist referral for this age group. Priorities for future research include a focus on identifying 1) mechanisms driving preschool wheezing; 2) biomarkers of treatable traits and efficacy of interventions in those without allergic sensitisation/eosinophilia; 3) the need to include both objective outcomes and caregiver-reported outcomes in clinical trials; 4) the need for a suitable action plan for children with preschool wheezing; and 5) a definition of severe/difficult-to-treat preschool wheezing.

Technology for a Food-secure Future: A Review of Technology Advances in Sustainable Agriculture

There is no doubt that advanced technologies play a very important and crucial role in driving the agricultural sustainable practices in order to satisfy the increasing demand for food production as well as to minimize environmental impacts. This review article aims to provide comprehensive points of view regarding a current state as well as the future advances in smart farming techniques for developing an agricultural sustainability. The digital sensors are considered as key techniques for agricultural transformation which provide an accurate managing and monitoring the various agricultural activities such as crop production, soil moisture, micro climate data, and others. Valuable detailed data are acquired by these sensors to be delivered to the farmers in order to make suitable decisions, ensuring the sustainable and efficient utilization of the agricultural resources. Moreover, managing irrigation using the advanced technologies is benefited wireless monitoring remotely and controlling systems. These technologies and advanced tools help the farmers to reduce water consumption and increase their benefits as well as promotes sustainable management practices of the water resources. Additionally, the drones such as unmanned aerial vehicles (UAVs) can be attached with several kinds of sensors or cameras for capturing detailed information of crop health, soil status, required fertilizers, irrigation, and pesticides. Besides these benefits of drones, they provide a function of an early detection of agricultural problems which allow the decision makers for taking suitable actions. Furthermore, the biotechnology advances such as CRISPR gene editing, and transgenic animals’ developing are very beneficial for enhancing the crop yield, disease resistance, and nutrients availability, and agricultural sustainability. The precision agriculture (PA) integration with geographic information system (GIS) and remote sensing (RS) provides site-specific management and its decisions in order to optimize the inputs utilization, waste reduce, and sustainable practices promotion. Also, advanced technologies’ application (i.e. artificial intelligence ‘AI’, machine learning ‘ML’, and the Internet of Things ‘IoT’) has been found to be a promising for achieving an agricultural sustainability. Therefore, by using these powerful technologies, the farmers can enhance an agricultural production, decrease an environmental effect and achieve the food security.

Finite control set model predictive current control for three phase grid connected inverter with common mode voltage suppression

This research introduces an advanced finite control set model predictive current control (FCS-MPCC) specifically tailored for three-phase grid-connected inverters, with a primary focus on the suppression of common mode voltage (CMV). CMV is known for causing a range of issues, including leakage currents, electromagnetic interference (EMI), and accelerated system degradation. The proposed control strategy employs a system model that predicts the inverter’s future states, enabling the selection of optimal switching states from a finite set to achieve dual objectives: precise current control and effective CMV reduction, a meticulously designed cost function evaluates the potential switching states, balancing the accuracy of current tracking against the necessity to minimize CMV. The approach is grounded in a comprehensive mathematical model that captures the dynamics of CMV within the system, and it utilizes an optimization process that functions in real-time to determine the most suitable control action at each interval, Experimental validations of the proposed FCS-MPCC scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters, Experimental validations of the proposed (MPC with CMV) scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters. The proposed method achieved substantial reductions in CMV, notable improvements in current tracking accuracy, and extended system lifespan compared to conventional control methods.

Damage assessment on the discarded macro-benthic fauna in the Italian striped venus clam (Chamelea gallina) fisheries

The striped venus clam fishery in Italy is carried out by means of hydraulic dredges and it is one of the most important socio- economic activities in the Italian fisheries sector. Dredging has traditionally been considered among fishing activities with the greatest impact at the ecosystem level. Here, therefore, we investigated the disturbance level exerted by dredging on the studied area. Also, damage and mortality rates exerted by dredging on the discarded macro-benthic fauna living associated with Chamelea gallina during the summer season were investigated using a four-level damage scale, given three different haul time duration (3, 6 and 9 minutes). Despite the fact that macro-benthic fauna represented on average only 4.4% of the total catch, the most represented taxa in terms of both abundance and number were Malacostraca, Bivalvia, and Echinoidea. The analysis of the macro-benthic communities’ structure between hauls revealed that the species composition was very similar, and ABC plots together with the Warwick Statistics (W) revealed a moderately disturbed macro-benthic community. No significant difference was found in damage and mortality rates between hauls duration when stratified at taxon or species level. Overall, 61.0% of individuals of the catch were undamaged, whereas 16.1%, 3.7%, and 19.2% displayed slight, intermediate, and severe damage, respectively. We found that soft-shelled or soft-bodied species were the most affected by the harvesting process, whereas thick-shelled or thick-bodied species suffered the slightest damage. In particular, the species suffering major (severe) damage were the sea urchin Echinocardium cordatum (84.6%), the bivalves Mactra stultorum and Polititapes aureus (32.5% and 20.0%, respectively), the crab Liocarcinus vernalis (4.0%), and the sea star Astropecten irregularis (4.8%). The overall mortality rate of all discarded individuals was 22.9%, with E. cordatum showing the highest mortality rate of 96.0%. These findings highlight the importance of guaranteeing the integrity of the entire ecosystem through the adoption of suitable management plans and actions.

Flexible recommendation for optimizing the debt collection process based on customer risk using deep reinforcement learning

Finance sector loss can be minimized by reducing the number of defaulters who often miss payments during debt collection. Most research focused on the credit risk analysis before approving the loan to the customer, and few papers related to predicting the defaulters. Certain works are focused on minimizing the number of calls made by debt collectors during the collection process. We have developed a Flexible Recommender System using Deep Reinforcement Learning (FRS-DRL) to provide Flexible recommendations based on the risk category of the customers. Initially, we categorized customers into defaulters and non-defaulters. The defaulters are again classified into Very Low, Low, Medium, High, and Very High-Risk customers. Based on this category, we recommend suitable actions to the Field Collection Agent (FCA) to increase the collection rate. The suggestions include how frequently the customer should be contacted, statements, and the dates and times of visits. At the same time, the statements define instructions to FCA for collecting money. Managers are traditionally in charge of this type of allocation; they must understand each customer personally. Our proposed recommendation method eliminates the need for manual mapping. We build a Hybrid Actor-Critic algorithm with an Adaptive Trust region Policy Optimization algorithm for classifying the customer category and providing the appropriate recommendations to reduce the defaulter rate, thereby increasing the collection efficiency and reducing the human effort. This FRS-DRL is tested in a real-time environment for three months and provides an improved reward rate with and without transfer learning at 73.22 % and 62.44 %. The collection percentage improved by 20.34 % in five different regions, with a 16.30 % reduction in the number of visits made by the FCA.

&lt;b&gt;Seguimiento y acompañamiento que reciben las comisiones d&lt;/b&gt;e &lt;b&gt;autoevaluación&lt;/b&gt; de la División de Educología

The permanent activity Self-Evaluation and Quality Management of the Division of Educology (APAGCDE), carries out within its multiple tasks; accompany and follow up on the improvement plans of the self-assessment and accreditation commissions (CAE's) of own and shared careers, therefore, hold meetings and consult with different social actors involved in the substantive actions of the Academic Unit (UA) , as are the representatives of the Division of Educology (DED) in the CAE's, this with the purpose of offering the support required from the UA Directorate and this permanent activity. In 2023, the APAGCDE followed up, held an annual meeting and applied a consultation to the people participating in said commissions, to know the current status of the self-assessment and accreditation processes of the DED careers, as well as assess the support that They receive these commissions from the curricular advisory service of the Vice-Rector for Teaching (VD). The consultation responds to a formative evaluation that promotes suitable and relevant actions that benefit the DED and university bodies with the sole purpose of continuing to improve actions and seek excellent academics.

Deep Ensemble learning and quantum machine learning approach for Alzheimer’s disease detection

Alzheimer disease (AD) is among the most chronic neurodegenerative diseases that threaten global public health. The prevalence of Alzheimer disease and consequently the increased risk of spread all over the world pose a vital threat to human safekeeping. Early diagnosis of AD is a suitable action for timely intervention and medication, which may increase the prognosis and quality of life for affected individuals. Quantum computing provides a more efficient model for different disease classification tasks than classical machine learning approaches. The full potential of quantum computing is not applied to Alzheimer’s disease classification tasks as expected. In this study, we proposed an ensemble deep learning model based on quantum machine learning classifiers to classify Alzheimer’s disease. The Alzheimer’s disease Neuroimaging Initiative I and Alzheimer’s disease Neuroimaging Initiative II datasets are merged for the AD disease classification. We combined important features extracted based on the customized version of VGG16 and ResNet50 models from the merged images then feed these features to the Quantum Machine Learning classifier to classify them as non-demented, mild demented, moderate demented, and very mild demented. We evaluate the performance of our model by using six metrics; accuracy, the area under the curve, F1-score, precision, and recall. The result validates that the proposed model outperforms several state-of-the-art methods for detecting Alzheimer’s disease by registering an accuracy of 99.89 and 98.37 F1-score.

Conservation genetics and status of Orania sylvicola palms in Singapore - native relicts or historical re-introductions?

Orania sylvicola (Griff.) H.E.Moore is a large, single-stemmed palm species widely distributed across Malesia. It is the only species of Orania present in Peninsular Malaysia and Singapore. In Singapore, the species is rare and critically endangered, although a full understanding of its distribution is hampered by sparse historical collections. However, conflicting records on the known localities in the literature, along with a recent discovery of a substantial population of Orania sylvicola within the Tyersall Learning Forest in Singapore Botanic Gardens, have led to questions on the origin of the extant populations of O. sylvicola. There are known instances of past introductions of non-native plant species in Singapore, which raised suspicions that some of the individuals were historical reintroductions from overseas. Genetic analyses, a comprehensive survey of existing literature and herbarium collections, and a topographical study of existing Orania sylvicola populations were carried out to determine the origin of these individuals in Singapore. Our results suggested that all Orania sylvicola individuals in Singapore are of native origin, with two populations present. The distribution of the two populations overlaps in the Singapore Botanic Gardens, which may indicate that some of the plants have been transferred to the Botanic Gardens from another location in Singapore. Preliminary results also revealed that sampled populations within Peninsular Malaysia and between Peninsular Malaysia and Singapore are highly differentiated from each other with very little gene flow either by pollen or seed dispersal. Our study highlights how the integration of genetic information, historical collections and topographical analyses can contribute to assessing the origin and conservation potential of endangered plant species in Singapore and provide insights into suitable conservation actions for Orania sylvicola.

The Risk Factors Associated with the Prevalence of Multimorbidity of Anaemia, Malaria, and Malnutrition among Children Aged 6-59 Months in Nigeria.

In the last ten years, multimorbidity in children under the age of five years has become an emerging health issue in developing countries. The study of multimorbidity of anaemia, malaria, and malnutrition (MAMM) among children in Nigeria has not received significant attention. This study aims to investigate what risk factors are associated with the prevalence of multimorbidity among children aged 6 to 59 months in Nigeria. This study used two nationally representative cross-sectional surveys, the 2018 Nigeria Demographic and Health Survey and the 2018 National Human Development Report. A series of multilevel mixed-effect ordered logistic regression models were used to investigate the associations between child/parent/household variables (at level 1), community-related variables (at level 2) and area-related variables (at level 3), and the multimorbidity outcome (no disease, one disease only, two or more diseases). The results show that 48.3% (4917/10,184) of the sample of children aged 6-59 months display two or more of the disease outcomes. Being a female child, the maternal parent having completed higher education, the mother being anaemic, the household wealth quintile being in the richest category, the proportion of community wealth status being high, the region being in the south, and place of residence being rural were among the significant predictors of MAMM (p < 0.05). The prevalence of MAMM found in this study is unacceptably high. If suitable actions are not urgently taken, Nigeria's ability to actualise SDG-3 will be in grave danger. Therefore, suitable policies are necessary to pave the way for the creation/development of integrated care models to ameliorate this problem.

Potential Adverse Outcome Pathways of Chlorinated Organophosphate Flame Retardants.

Chlorinated organophosphate flame retardants (Cl-OPFRs) are frequently detected chemicals in the environment and biological samples, yet there is a lack of systematic evaluation regarding the adverse effects and toxicological mechanisms of Cl-OPFRs. This study utilizes the adverse outcome pathway (AOP) framework to assess the health implications and mechanisms of Cl-OPFRs, identifying multi-system toxicity, with a particular emphasis on reproductive issues and the possible toxic mechanisms. These results enhance knowledge of the health hazards linked to Cl-OPFRs, supporting the creation of focused risk evaluations and suitable regulatory actions.

Establishing a Budget for Optimal Response Strategies for Risks Categorized into Distinct Groups by using a Mathematical Model and Genetic Algorithm

Construction projects may be subjected to various risks which must be identified, evaluated, and a suitable response to each risk must be determined. The risk response stage is a crucial and significant phase in risk management that requires particular attention. This paper proposes an effective mathematical model for determining the most suitable strategy and action in dealing with both primary and secondary risk events in different risk categories that may arise in a construction project. It also provides a method for estimating or forecasting the anticipated budget for a risk response plan. Another contribution of this study is the development of an innovative approach that combines binary programming with the genetic algorithm. The efficacy of the proposed methodology was examined by its implementation in a real geothermal project. The results demonstrated that the proposed framework serves as a useful tool to tackle the challenges related to the selection and optimization of risk response strategies, as well as setting an appropriate budget for the risk response plan. The suggested model can help decision-makers to assess the variety of viable risk response actions and strategies and arrive at a more well-informed decision.

Thermoluminescence characterization of flooring tiles from Malaysia for potential use in retrospective dosimetry

In the event of natural disasters or technical malfunctions at nuclear sites, the surrounding media, including individuals, may be subject to unforeseen radiation exposure. Thus, it is not possible to determine the precise level of radiation exposure that each person has received in this scenario. To handle such a problem, the use of retrospective dosimetry can offer an accurate evaluation of the radiation dose acquired as a result of such incidents. This information enables the development of suitable remedial actions. Normally, in the region that has been impacted, several artifacts can serve as natural dosimeters. The study involved the utilization of locally available seven different brands of flooring tiles in Malaysia for the possibility of being used in retrospective dosimetry through exposures to 60Co gamma-rays within a range of 10–200 Gy. To understand the luminescence features of this material, a convenient thermoluminescence (TL) technique has been employed. The dosimetric features of the samples were investigated, including their effective atomic numbers (Zeff), reproducibility, glow curve, dose response, sensitivity, linearity index, energy dependency and fading, which is representative of the usual TL method. Additionally, by analyzing the glow curves of the irradiated samples together with the peak shape method and the initial rise methods, several kinetic parameters, including the kinetic orders (b), activation energy (E) or trap depth, frequency factor (s) or escape probability, and trap lifetime (τ) were calculated. Different kinds of tiles exhibit distinct scenarios in terms of kinetic characteristics. T1 sample exhibits the highest degree of sensitivity and a highly linear dose response among the tile samples that were evaluated. Also, T1, T3, and T5 samples show minimal loss of TL intensity (∼38–39%) one year after irradiation, making them ideal for use as a retrospective dosimeter.

Assessment of Water Quality of Tighra Reservoir using Weighted Arithmetic Water Quality Index (WA-WQI)

Water is a natural resource used for drinking, agriculture, and industry. Being a vital source of life for living beings, its quality needs to be assessed regularly and the environmental health of water resources should be maintained accordingly. This study assessed the water quality of Tighra reservoir, which is the primary source of water supply in Gwalior, M.P. For this task, the water sampling was carried out during Jun-Sep, 2023. Total 32 samples were collected to cover pre &amp; post monsoon and monsoon season. Subsequently, the water quality parameters such as pH, Turbidity, Acidity, Alkalinity, Chloride, Hardness, Total solids (TS), Total dissolved solids (TDS), Total suspended solids (TSS), Dissolved solid (DS), Iron (Fe), MPN were determined to assess weighted arithmetic water quality index (WA-WQI). Consequently, the WQI ranges from 15.91 to 121.76 respectively. Furthermore, poor water quality was observed at SS7 followed by SS6, and SS5. However, good quality of water was observed at SS1 followed by SS2, SS3, and SS8. The seasonal distribution of WQI reveals that June (19.38-121.75) with SS7 and SS6 have poor water quality, July (16.74-66.87) with SS5, SS6, and SS7 have poor water quality, August (38.08-79.28) with SS4, SS5, SS6, and SS7 have poor water quality, September (15.91-101.11) with SS4, SS5, SS6, and SS7 have poor water quality. The aforementioned findings will surely help to local authorities to take suitable action for the upgradation water quality of Tighra reservoir.

Projected future climatic forcing on the global distribution of vegetation types.

Most emissions scenarios suggest temperature and precipitation regimes will change dramatically across the globe over the next 500 years. These changes will have large impacts on the biosphere, with species forced to migrate to follow their preferred environmental conditions, therefore moving and fragmenting ecosystems. However, most projections of the impacts of climate change only reach 2100, limiting our understanding of the temporal scope of climate impacts, and potentially impeding suitable adaptive action. To address this data gap, we model future climate change every 20 years from 2000 to 2500 CE, under different CO2 emissions scenarios, using a general circulation model. We then apply a biome model to these modelled climate futures, to investigate shifts in climatic forcing on vegetation worldwide, the feasibility of the migration required to enact these modelled vegetation changes, and potential overlap with human land use based on modern-day anthromes. Under a business-as-usual scenario, up to 40% of terrestrial area is expected to be suited to a different biome by 2500. Cold-adapted biomes, particularly boreal forest and dry tundra, are predicted to experience the greatest losses of suitable area. Without mitigation, these changes could have severe consequences both for global biodiversity and the provision of ecosystem services. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Disruption of early embryonic development in mice by polymethylmethacrylate nanoplastics in an oxidative stress mechanism

Polymethylmethacrylate (PMMA) has been used in many products, such as acrylic glass, and is estimated to reach 5.7 million tons of production per year by 2028. Thus, nano-sized PMMA particles in the environment are highly likely due to the weathering process. However, information on the hazards of nanoplastics, including PMMA in mammals, especially reproductive toxicity and action mechanism, is scarce. Herein, we investigated the effect of PMMA nanoplastics on the female reproductive system of mice embryos during pre-implantation. The treated plastic particles in embryos (10, 100, and 1000 μg/mL) were endocytosed into the cytoplasm within 30 min, and the blastocyst development and indices of embryo quality were significantly decreased from at 100 μg/mL. Likewise, the transfer of nanoplastic-treated embryos at 100 μg/mL decreased the morula implantation rate on the oviduct of pseudopregnant mice by 70%, calculated by the pregnant individual, and 31.8% by the number of implanted embryos. The PMMA nanoplastics at 100 μg/mL significantly increased the cellular levels of reactive oxygen species in embryos, which was not related to the intrinsic oxidative potential of nanoplastics. This study highlights that the nanoplastics that enter systemic circulation can affect the early stage of embryos. Thus, suitable action mechanisms can be designed to address nanoplastic occurrence.

Mixed-batch scheduling to minimize total tardiness using deep reinforcement learning

This study addresses the issue of scheduling batch machine to minimize total tardiness. Vacuum heat treatment allows multiple jobs to be processed as a batch, as long as they do not exceed the machine's weight and size limits; the weight and size of the jobs both impact the batch processing time. By considering the differences in the release time and due date for each job, a mixed-integer linear programming model is developed and validated using small-scale instances. To tackle large-scale scheduling problems, an intelligent algorithm based on deep reinforcement learning is proposed. Double deep Q-learning networks are designed, and the environmental state feature matrix is extracted as input parameters for the networks. Four job-sorting rules and three scheduling time windows are defined, resulting in twelve action rules within the action space. The most suitable action rule is dynamically selected based on the current batch's environmental status during the scheduling process. Through extensive comparison using large-scale random data, the proposed algorithm demonstrates significantly improved scheduling performance compared to the benchmark algorithms.

Poisson–Lie analogues of spin Sutherland models revisited

Some generalizations of spin Sutherland models descend from ‘master integrable systems’ living on Heisenberg doubles of compact semisimple Lie groups. The master systems represent Poisson–Lie counterparts of the systems of free motion modeled on the respective cotangent bundles and their reduction relies on taking quotient with respect to a suitable conjugation action of the compact Lie group. We present an enhanced exposition of the reductions and prove rigorously for the first time that the reduced systems possess the property of degenerate integrability on the dense open subset of the Poisson quotient space corresponding to the principal orbit type for the pertinent group action. After restriction to a smaller dense open subset, degenerate integrability on the generic symplectic leaves is demonstrated as well. The paper also contains a novel description of the reduced Poisson structure and a careful elaboration of the scaling limit whereby our reduced systems turn into the spin Sutherland models.