The sustainable development of resource-based cities has long been a focal point for both governmental policies and academic research. A wealth of academic literature identifies the 'resource curse' as a critical barrier to sustainable development in these urban areas. Despite extensive studies, a consensus on effective strategies to circumvent or resolve this challenge remains elusive. Recent empirical data reveals a noticeable pattern of economic growth differentiation in China's resource-based cities. Why do some resource-based cities experience economic decline while others achieve sustainable economic growth? Therefore, this paper analyzes the reasons for the divergence of economic growth in resource cities from the perspective of heterogeneity. The results of the study show that the phenomenon of the resource curse is conditional, and the heterogeneity of demo-graphic changes, geographic location and institutional environment has a significant impact on the economic growth of resource cities. These factors are investigated to elucidate the observed diversification and heterogeneity in the economic growth of resource-based cities. Building upon this analysis, the paper proposes a framework for differentiated sustainable development pathways tailored to the unique contexts of these cities. This approach not only contributes to the current understanding of the 'resource curse' but also offers novel insights for future research aiming to adopt a more nuanced and context-specific perspective in examining sustainable urban development.

This research article investigates the application of artificial intelligence (AI) in human resource management (HRM) within the Australian commercial media sector, addressing a critical gap in scholarly knowledge. Despite increasing interest in AI’s potential, there is a lack of consensus among managers and HR professionals regarding its application. Through qualitative interviews with 22 industry professionals, this study examines perceptions, understanding, and the potential for leveraging AI across key HRM functions. Findings indicate significant potential for AI to enhance HRM processes, particularly in job/work design and staffing. This research contributes to a deeper understanding of AI’s role in HRM and offers insights to optimize organizational efficiency and effectiveness in the Australian commercial media sector.

The process of managing people in an organised way to assist a company in achieving its goals is known as human resources management. an increase in workplace stress, which is detrimental to an employee's health and well-being as well as the performance of the organisation. Certain guiding concepts and precepts are evergreen and can support the development of human resources while ensuring the wellbeing of the individual and the company. In this study, we examine how the methods and practices of yoga might benefit public organisations by lowering employee stress levels and boosting their altruism and greedlessness.

This study examines the dynamic relationship between Green Human Resource Management (Green HRM), Environmental Performance (EP), Green Employee Behavior (GEB), and Environmental Knowledge and Awareness (EKAW) within the Egyptian hospitality sector context. Using Pearson correlation coefficients and regression analyses on a sample of 400 staff members from hotels and tourist villages in Egypt, the study examines Green HRM practices’ influence on EP, mediated by GEB and moderated by EKAW. The findings reveal significant positive correlations between Green HRM practices and these mediators, indicating that comprehensive environmental strategies and incentive management are pivotal in promoting eco-friendly practices among employees. The study further confirms GEB and EKAW’s substantial mediating role in enhancing EP. The results also suggest that while EKAW and GEB independently contribute to EP, their interaction and the role of environmental awareness as a potential moderator warrant further examination. This research contributes to the literature on sustainable business practices by underscoring human resource strategies’ integral role in achieving environmental sustainability goals, highlighting the importance of incentivizing green practices, and cultivating an organizational culture that prioritizes EKAW. These insights are particularly valuable for organizations seeking to enhance their ecological footprint through effective Green HRM practices.

Starting from the fact that the units of local self-government of the Republic of Serbia are not sufficiently oriented towards innovations related to human resources and the environment, the application of quantitative research tradition examines the positioning and understanding of certain procedures and decisions of human resources management regarding the functioning of the environmental protection system, on the one hand, and future strategies for changing the given system, on the other. A series of 105 face-to-face interviews were conducted during the whole of 2016 in 105 of the 191 municipalities in the Republic of Serbia. Examination of the attitudes of the local self-government was carried out using a specially designed closed electronic questionnaire. The results of the multivariate regressions show that the most important predictor of the possible improvement of the environment is the size of the municipality. The remaining variables (e.g., human resources policy, human resources service) did not have significant effects on the ability to improve the environment. In more than half of local self-governments, there was no clearly defined human resources strategy in the field of environmental protection, an environmental protection service, an Environmental Action Plan, as well as an employment plan for waste management in local governments, etc. The research has shown that top managers of local governments agree that the management of human resources in environmental protection represents a significant opportunity to improve the quality of the environment. The results of the research can be used to improve the organization of human resource competence in environmental protection and the quality of a working and sustainable environment in the area of local self-government.

This qualitative research investigates the pivotal role of Human Resource Management (HRM) in enhancing supply chain effectiveness. Through semi-structured interviews with 25 professionals across various industries, the study explores how HRM practices influence key aspects of supply chain management (SCM), including talent management, training and development, leadership, organizational culture, and technology integration. Findings highlight the strategic alignment between HRM and SCM as crucial for optimizing supply chain performance and responsiveness. Effective talent management strategies, such as recruitment, development, and succession planning, emerged as critical factors in ensuring operational stability and innovation within supply chains. Training programs were identified as instrumental in equipping employees with the necessary skills to navigate technological advancements and market complexities, fostering collaboration and enhancing decision-making capabilities. Leadership development initiatives were also found to be pivotal in promoting organizational effectiveness and fostering a culture of continuous improvement. The study further underscores the role of HRM in cultivating a collaborative and inclusive organizational culture that enhances communication, trust, and teamwork among supply chain stakeholders. Additionally, the integration of digital technologies and sustainability considerations emerged as transformative trends shaping modern supply chains, with HRM practices playing a crucial role in facilitating technological adoption and promoting environmentally responsible practices. Addressing challenges such as resource constraints, regulatory compliance, economic uncertainties, and global supply chain complexities remains crucial for organizations aiming to optimize HRM practices and achieve sustainable supply chain performance. Overall, this research contributes to a deeper understanding of how HRM can drive innovation, collaboration, and resilience within supply chains, positioning organizations for long-term success and growth.

Cloud computing nowadays is not an emerging topic, and virtualization is an indispensable technology to expedite cloud computing to become the next sign of the coming Internet revolution. In real life, scientists never stop at exploring the possibilities from such technology by investigating millions of experiments and applications to enhance the quality of virtual services. However, isolated construction for the virtual machine doesn’t save the technology from unwanted data volumes or insensitive processing time. Containers are created to address such problems, by distributing applications without initiating the entire virtual machine. Docker, as an important player in this game, is an open-source application of the container family. The management tool from Docker containers, Swamskit, does not take heterogeneities in either virtualized containers or physical nodes. There are different nodes in the cluster, and each node is different in configurations, resource availability, or concerning resource, etc. Furthermore, the requirements initiated by different services change all the time. The demand might be CPU-intensive (e.g. Clustering services) and also memory-intensive (e.g. Web services), or completely at the opposite. In this paper, we focus on exploring the Docker container cluster and designing, DRAPS, a resource-aware placement scheme, to improve the system performance in a heterogeneous cluster.

Cloud infrastructure provides computing services where computing resources can be adjusted on-demand. However, the adoption of cloud infrastructures brings concerns like reliance on the service provider network, reliability, compliance for service level agreements (SLAs), etc. Software-defined networking (SDN) is a networking concept that suggests the segregation of a network’s data plane from the control plane. This concept improves networking behavior. In this paper, we present an SDN-enabled resource-aware topology framework. The proposed framework employs SLA compliance, Path Computation Element (PCE) and shares fair loading to achieve better topology features. We also present an evaluation, showcasing the potential of our framework.

The resource curse indicates that economic growth performs poorly in countries with significant natural resources. Nevertheless, certain countries rich in energy managed to protect their resource riches in the long run. It is necessary to enforce effective policies in resource-rich countries to fully leverage the advantages which can come from the abundance of natural resources. This study aimed to evaluate how oil-rich countries would avoid resource flows by successful fiscal and management policies. By taking the guidance of Norway and implementing fiscal policy focused on tax rules on its oil management, it is proposed that oil-exporting countries benefit significantly. The framework attempts to mitigate this resource curse and utilise oil revenues in the interest of the country.

The explosion of data has transformed the world since much more information is available for collection and analysis than ever before. To extract valuable information from the data in different dimensions, various deep learning models have been developed in the past years. Although these models have demonstrated their strong capability on improving products and services in various applications, training them is still a time-consuming and resource-intensive process. Presently, cloud, one of the most powerful computing infrastructures, has been used for the training. However, how to manage cloud computing resources and to perform the training efficiently is still challenging current techniques. For example, general resource scheduling approaches, such as spread priority and balanced resource schedulers, actually do not work well with deep learning workloads. Besides, the resource allocation problem on a cluster can be divide into two subproblems: (1) local resource optimization: improve resource configuration for a single machine; (2) global resource optimization: improve the cluster-wide resource allocation. In this thesis, we propose two novel container schedulers, FlowCon and SpeCon, that are designed to address these two subproblems respectively and specifically to optimize performance of short-lived deep learning applications in the cloud. FlowCon focuses on resource configuration of single-node in a cluster, as show that it efficiently improves deep learning tasks completion time and resource utilization, and reduces the completion time of a specific job by up to 42.06\% without sacrificing the overall system time. SpeCon targets on cluster-wide resource configuration that speculatively migrate slow-growing models to release resources for fast-growing ones. Based on our experiments, SpeCon improves makespan for up to 24.7\%, compared to current approaches.

The article deals with human resource management and selected personnel indicators in 10 sustainable development. The main goal of this paper is to analyse and describe human resource 11 management focused on audit of workload, motivation of employees, the competence of staff, their 12 knowledge and experience. The article shows how the selected personnel indicators are examined 13 in selected establishments of multinational company in Slovakia, as an attractive employer inter-14 ested in growth of its employees, thus employer branding. For research methods, a case study meth-15 odology is used. Data collection was carried out through a questionnaire survey with employees in 16 the company as well as an interview with sale advisors and managers. The research aimed to point 17 out a case study of the investigation of selected personnel indicators in human resources manage-18 ment in a selected company and to examine the perception of the performance of employees in 19 connection with their financial evaluation and their satisfaction in the selected company with an 20 impact on employees´ performance. Furthermore, the research aimed to determine whether there 21 was a dependence between the subjectively perceived performance of the employees and selected 22 aspects. Further indicators were calculated, such as employment rate, proportion of wasted time 23 and labour productivity.

The article deals with human resource management and selected personnel indicators in sus-tainable development. The main goal of this paper is to analyse and describe human resource management focused on audit of workload, motivation of employees, the competence of staff, their knowledge and experience. The article shows how the selected personnel indicators are examined in selected establishments of multinational company in Slovakia, as an attractive em-ployer interested in growth of its employees, thus employer branding. For research methods, a case study methodology is used. Data collection was carried out through a questionnaire survey with employees in the company as well as an interview with sale advisors and managers. The research aimed to point out a case study of the investigation of selected personnel indicators in human resources management in a selected company and to examine the perception of the per-formance of employees in connection with their financial evaluation and their satisfaction in the selected company with an impact on employees´ performance. Furthermore, the research aimed to determine whether there was a dependence between the subjectively perceived performance of the employees and selected aspects. Further indicators were calculated, such as employment rate, proportion of wasted time and labour productivity.

In this article, management control has been studied from the perspective of intention towards sustainable development of companies. The main idea focuses on the analysis of relations between sustainable management control and the system of performance appraisal using as a reference the resource-based theory. These relations have been assessed both theoretically by reviewing literature in the field, and empirically based on data collected in a questionnaire from top management, as well as data from Annual Reports from the main segment of Bucharest Stock Exchange. The authors have shown that financial performance has a significant influence on management control, and the return on capital employed is one of the most recommended indicators. The sustainable nature attributed to management control is the result of institutional theory evolution, provisions of Directive 2014/95/EU and Guide of Global Management Accounting Principles (2015) using stewardship. Consequently, profits are not the main element any more in the assessment of financial „health” of a company. To ensure the sustainable success, sustainable balanced scorecard is one of real measures for managing financial and non-financial performance.

Keywords: Green human resource management1; Green supply chain management2; Regulation and legislation3; Sustainable development goals4.

For an enterprise, the most critical aspect of development is resource management, which has a significant impact on all aspects of the enterprise. Therefore, enterprises must pay attention to resource management allocation, which can better promote the sustainable development of the enterprise and obtain optimal benefits. In the production and development of modern enterprises, the acquisition of benefits also involves the allocation of resources in enterprise management. This paper proposed a benefit optimization scheduling algorithm based on a macroeconomic prediction model under auction mechanism and a grid resource scheduling algorithm driven by a benefit function to allocate resources reasonably. This article used macroeconomic forecasting models to fully understand the resource needs of various departments and the resources held by enterprises, and rationally allocate various resources in various departments. This can improve the work efficiency of various departments, thereby reducing the cost of the enterprise, and achieving optimal benefits for the enterprise. The experimental results in this paper showed that the cost of resource management allocation for the benefit optimization scheduling algorithm and the grid resource scheduling algorithm based on the benefit function driven under the auction mechanism was 105.6 yuan and 46.8 yuan respectively when the task volume was 125 under the multi user environment. The time allocated for resource management was 36.6s and 18.9s respectively. It can be seen that the efficiency function driven grid resource scheduling algorithm had a lower cost and time for resource management allocation, so the efficiency function driven grid resource scheduling algorithm can achieve enterprise efficiency optimization.

This study considers the values of the credit payments that are made at random values for both payment size and number. The simultaneous existence of the two random variables increases the total value of the volatility of the payment process. The dependence for the simultaneous influence of the two random variables is derived by applying conditional probabilistic formulations. Formal relations apply to animal husbandry credit payments. The credit policy was analyzed for the cases of regular payments and with a stochastic number of payments. A recommendation for the upper level of credit payments is proposed based on a model predictive approach using historical payment data. A credit management algorithm is derived, in which decision-making for credit payments is formally derived and numerically discussed. The application of derived quantitative relations is empirically applied to real data on animal husbandry. The recommendations from the dependencies obtained allow a reduction in the mean values of credit payments, which correspond to a reduction in the credits used for business management, without a change in business policy.

The current organisation is facing intensifying competition in the global business world with loads of challenges and tries to sustain itself in the market through innovation and cost-cutting. The scientific community has a growing interest in management to make a niche in the global competitive era. They have started focusing on the expediency of green human resource management (GHRM) to steer the way to excel the organizations and their employees for sustainability. Green HRM tries to build an organization that encourages employees to adopt environmentally friendly workplace behaviours and minimize costs. The aim of this study is to identify and analyse the comprehensive research advances made in the field of green human resource management through bibliometric analysis. The Scopus database has been used to conduct the research. A total of 551 documents were inferred from Scopus, considering the department time period, 2004 to 2023. The outcomes of the study shed light on the eminence of green HRM research, identifying prolific authors and co-authorship patterns, commonly used keywords, country-wise and organization-wise analysis, research themes, theoretical foundations and simple statistics. This comprehensive exploration has provided valuable insights into the concept of green HRM, its objectives, and its potential benefits for businesses and society as a whole.

Globally, groundwater is the largest distributed storage of freshwater and plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often change as a result of land usage, abstraction, as well as variation in climate. To solve these challenges, many conventional solutions, such as certain numerical techniques, have been proffered for groundwater modelling. The global evolution of the Internet of Things (IoT) has enhanced the culture of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. At the moment, some studies in the literature reveal that groundwater managers lack an efficient and real-time groundwater management system that is needed to gather the required data. Additionally, the literature reveals that the existing methods of collecting data lack the required efficiency to meet computational model requirements and meet management objectives. Unlike previous surveys, which solely focussed on particular groundwater issues related to simulation and optimisation management methods, this paper seeks to highlight the current groundwater management models as well as the IoT contributions.

Packaged water consumption has grown rapidly in urban areas of many low and middle income countries, but particularly in Ghana. However, the sources of water used by this growing packaged water industry and its implications for water resource management and transport-related environmental impacts have not been described. This study aimed to assess the spatial distribution of regulated packaged water production in Ghana, both in relation to demand and for natural mineral water, to hydrogeological characteristics. 764 addresses for premises licenced to produce packaged water from 2009-2015 were mapped and compared to regional sachet water consumption and beverage import/export data examined. We find evidence to suggest packaged water is transported shorter distances in Ghana than in developed countries. For natural mineral waters, producers should be able to address the most widespread water quality hazards (including high salinity, iron and nitrates) in aquifers used for production through reverse osmosis treatment. The study suggests there is scope to integrate beverage product and groundwater regulatory databases to support groundwater management.

Based on the Organizational Support Theory, this study examines the relationship between human resource management practices and service performance of sharing workers by demonstrating the mediation role of work engagement. We tested this theoretical model using an in-person interview questionnaire survey of 318 downwind drivers. Results showed that: (1) the main effect of human resource management practices of sharing workers on service performance was significant; (2) work engagement played a prominent mediation role between human resource management practices and service performance of sharing workers; (3) the mediation role of employee vigor between the platform incentives and the performance of employee services was significant; (4) employee dedication had an obvious and indirectly positive mediating effect between sharing workers' dimensions of human resource management practice and their service performance; (5) employee absorption on the mediation role between the various dimensions of the sharing human resources management practices (platform support, platform incentives and platform constraints) and employee service performance was significant. This study has important value for the study on human resource management practices in the context of sharing economy, and provides practical enlightenment for employee management of the sharing economy platform.

Efficient alternatives in energy production and consumption are constantly investigated by increasingly strict policies. In this way, the pollutant emissions that contribute to the greenhouse effect reduce and sustainability of the electricity sector increase. With more than a third of the world's energy consumption, buildings have great potential to contribute these sustainability goals. Additionally, with growing incentives in the Distributed Generation (DG) and Electric Vehicle (EV) industry, it is believed that Smart Buildings (SBs) can be a key in the field of residential energy sustainability in the future. In this work, an energy management system in SBs are developed to reduce the power demanded of a residential building. In order to balance the demand and power provided by the grid, microgrids such as Battery Energy Storage System (BESS), EVs and Photovoltaic Generation panels (PV) are used. Here, a Mixed Binary Linear Programming formulation (MBLP) is proposed to optimize the charge and discharge scheduling of EVs and also BESS. In order to show the efficiency of the model, a case study involving three scenarios and an economic analysis is considered. The results point a 65% reduction in peak load consumption supplied by grid and a 28.4% reduction in electricity consumption costs.

Food and Agricultural Organisation estimates from 2014 suggest 64% of the high seas migratory stocks are either overfished or undergoing overfishing. Illegal fishing exacerbates the damage to marine ecosystems, human rights, and food security, and is yet to be included in the recent United Nations (UN) draft agreement to protect high seas biodiversity. We used catch data from the Sea Around Us Project to determine trends in fishing gear use and the catches of countries fishing in the high seas. Using Two-way ANOVA, Principal Coordinates Analysis, and SIMPER Test, we found that Spain, Taiwan, Japan, Indonesia, and Ecuador had the highest catches in 2004-2014. Each decade, only 1 in 10 countries (10.8%) caught over 500,000 tonnes, leaving the remaining 90% of countries catching less than 500,000 tonnes. Further analysis showed the mean high seas catch per fishery decreased 55% between the 2000s and the 2010s. Purse seine and longline were increasingly used towards the 2010s. Catch from purse seine and longline accounted for 70.5% and 78.4% of total high seas catch in 1950-2014 and 2004-2014, respectively. Discards were unreported throughout 1950-2014, and the reporting levels of landings varied greatly between countries and gears. We call for immediate action and recommend (1) including international, overarching high seas governance to end illegal and unsustainable fishing in the high seas in the UN draft agreement, and (2) using large mobile marine protected areas in high seas fisheries management through the UN draft agreement.

Abstract: Purpose – In this article we lay out the change management practices adopted by financial firms in small states within the Eurozone. We determine whether these organisations have the ability to identify triggers for change (Red Flags) and subject them to eight thematic elements to understand whether management practices can continue to exist and support operational environments, even when unexpected circumstances affect their day to day operations and processes. In doing this we examine the extent to which the eight thematic elements from the model designed by Dalli Gonzi, (2019) (The Dali Model) can assist organisations in risk identification and business continuity planning. Design/methodology/approach – A self-administered questionnaire purposely designed for this study was administered to personnel working in internal controls within financial institutions of small Eurozone states. The participants were asked to grade statements using a 5-point Likert scale, ‘1’ being ‘totally disagree’ and ‘5’ being totally agree’ to the statement posed under the thematic elements forming the basis of the Dali Model. Findings – Factor analysis provided support for the eight hypothesised dimensions of the decision-making model: connection, capacity, governance, network, policy, training, process improvement, standards. Originality/value – The study provides a better understanding and support of “best practice” in change management through an understanding and assessment of the eight factors that are the basis of this model. It addresses practical recommendations to ensure application to a wider frame of use.

The River Basin Management Plan (RBMP) is an essential component of the European Union Water Framework Directive that details an integrated approach required to protect, improve and sustainably manage water resources. RBMP were intended to be produced for the periods 2009-2015, 2016-2021 and 2022-2027. However, after two years of delays in the development processes, the Republic of Ireland produced its first RBMP in 2010. The second RBMP cycle was also implemented in 2018 and is expected to run until the end of 2021 to give way to the third RBMP, whose consultation processes have been ongoing since December 2019. This paper contributes to the forthcoming RBMP by assessing stakeholders’ perspectives on the second RBMP through a desk-based review and by conducting interviews with nine institutions (14 interviewees). The qualitatively analysed interviews reveal a broad spectrum of actors associated with water management and governance in the Republic of Ireland through a three-tier governance structure that has been delivered (with amendment) through the first two RBMPs. Organisations such as the An Fóram Uisce|The Water Forum, the Environmental Protection Agency, the Local Authority Waters, and the Agricultural Sustainability Support and Advisory Programme have responsibilities designated in the RBMPs to deliver improved water quality, integrated catchment management, community engagement and awareness-raising. Trust has also been building up among these organisations and other agencies in the water sector. Despite these responsibilities and progress, the interviews identified communication lapses, ineffective collaboration and coordination among stakeholders and late implementation to be hampering the successful delivery of the second RBMP, in addition to significant pressures acting on water bodies from agricultural activities and urban wastewater treatment. Towards the third RBMP, the paper concludes that optimised water sector finance, enhanced and well-resourced communications, and improved stakeholder collaboration are needed to foster effective and efficient water services delivery and quality. More so, given the cross-cutting impact of the Sustainable Development Goals on water resources and the interconnected relations among the goals, the paper further recommends the integration of the SDGs in the various plans of actions and a co-benefits approach to derive the triple benefits from biodiversity, climate change initiatives and water quality measures.

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk.

Abstruct Green Human Resource Management (GHRM) has emerged as an essential strategy for achieving environmental sustainability within organizations. However, there remains a significant gap in understanding its direct impact on sustainable performance. This study seeks to address these gaps by investigating the relationship between GHRM and sustainable performance, with a focus on the mediating role of green innovation and the moderating influence of transformational leadership. A cross-sectional study was conducted among Malaysian small and medium-sized enterprises (SMEs) to explore the interrelationships between Green HRM, green process and product innovation, sustainability, and the role of sustainable leadership. The study's findings reveal a positive and significant relationship between green HRM practices and sustainability, encompassing environmental, economic, and social aspects. The findings suggest that management support for environmental initiatives is a critical factor in enhancing the effectiveness and spread of green innovations, emphasizing the importance of GHM in the broader context of organizational change and sustainability. In addition, the study underscores the critical role of transformative leadership in fostering sustainable practices, particularly the significant moderator role of responsible leadership in driving sustainable business practices. In summary, this study provides a roadmap for businesses, particularly SMEs, to leverage HGRM as a strategic tool in their pursuit of sustainability.

The 2030 Agenda for Sustainable Development, the SDGs, are high on the agenda for most countries of the world. In its publication of the SDGs, the UN has provided the goals and target descriptions that, if implemented at a country level, would lead towards a sustainable future. The IAEG (InterAgency Expert Group of the SDGs) was tasked with disseminating indicators and methods to countries that can be used to gather data describing the global progress towards sustainability. However 2030 Agenda leaves it to countries to adopt the targets with each government setting its own national targets guided by the global level of ambition but taking into account national circumstances. At present, guidance on how to go about this is scant, but it is clear that the responsibility is with countries to implement and that it is actions at a country level that will determine the success of the SDGs. SDG reporting by countries takes on two forms 1) global reporting using prescribed indicator methods and data; 2) National Voluntary Reviews where a country reports on its own progress in more detail but is also able to present data that are more appropriate for the country. For the latter, countries need to be able to adapt the global indicators to fit national priorities and context, thus the global description of an indicator could be reduced to describe only what is relevant to the country. Countries may also, for the National Voluntary Review, use indicators that are unique to the country but nevertheless contribute to measurement of progress towards the global SDG target. Importantly, for those indicators that relate to the security of natural resources security (e.g. water) indicators, there are no prescribed numerical targets/standards or benchmarks. Rather countries will need to set their own benchmarks or standards against which performance can be evaluated. This paper presents a procedure that would enable a country to describe national targets with associated benchmarks that are appropriate for the country. The procedure focusses on those SDG targets that are natural resource-security focussed e.g. extent of water-related ecosystems (6.6), desertification (15.3) etc., because the selection of indicator methods and benchmarks is based on the location of natural resources, their use and present state and how they fit into national strategies.

This study explored the intricate nexus between public service provision and land use planning, with a specific focus on the conservation of local plant species in Tambon Khao Phanom, Amphoe Khao Phanom, Krabi Province. Rooted in commitment. The research framework encompasses three fundamental activities: resource survey and collection, resource conservation and utilization, and resource development planning for public services, emphasizing land use planning for local plant species conservation. This multidisciplinary approach integrates the one-map method and draws data from the Department of Land Development to formulate a comprehensive land utilization plan that optimizes land use for biodiversity conservation. This study underscores the critical synergy between public service delivery and land use planning, highlighting their collective potential to advance community well-being and sustainable development.

Along with the economic growth, the companies must contribute to social progress and promote environmental sustainability in equal harmony. Sustainable human resource management (SHRM) strategies make it possible to attain the economic, social and environmental goals of a firm. In this regard, a survey method is discussed using the literature review and online questionnaire to identify the main factors/indicators during the SHRM evaluation of manufacturing firms in India. Uncertainty is commonly occurred in the assessment of SHRM factors. As a generalized version of fuzzy sets, single-valued neutrosophic set (SVNS) has been demonstrated as a valuable tool to illustrate the indeterminate, inconsistent and uncertain data of realistic decision-making problems. Considering the idea of SVNSs, this study develops a hybrid multi-criteria group decision-making (MCGDM) approach for assessing the SHRM of manufacturing firms under uncertainty settings. For this purpose, an SVN-alternative ranking order method accounting for two-step normalization (AROMAN) is proposed based on VIFI-score function-based decision experts’ (DEs’) weighting tool and integrated criteria weight-determining model to solve the MCGDM problems with fully unknown DEs and criteria weights. In this regard, we develop new SVN-distance measure to compute the degree of difference between SVNSs. Some examples are presented to demonstrate the efficacy of developed measure over the existing ones. In addition, new criteria weight-determination model is presented with the integration of objective weights through IVIF-distance measure-based model and subjective weights through ranking comparison (RANCOM) tool on SVNSs. The proposed ranking method is applied to an empirical study of SHRM assessment for manufacturing firms in India, which shows its applicability and feasibility. In this study, the evaluation criteria are characterized into social, environmental and economic aspects with DE’s opinions. Comparative and sensitivity analyses are made to show the strength and steadiness of presented approach. This study provides an innovative MCGDM analysis framework, which makes a significant contribution to the SHRM assessment problem under indeterminate, inconsistent and uncertain setting.

Human factors are the most relevant issues contributing to adverse events in obstetrics. Specific training of Crisis Resource Management (CRM) skills (i.e., problem solving and team management, resource allocation, awareness of environment, and dynamic decision-making) are now widespread and are often based on High Fidelity Simulation. In order to be used as a guideline in simulated scenarios, they need to be translated into specific and observable behavioral markers. To this purpose, we developed a set of observable behaviors related to the main elements of CRM in the delivery room. The observational tool was then adopted in a two-days seminar on obstetric hemorrhage where teams working in obstetric wards of six Italian hospitals took part to simulations. The tool was used as guide for the Io and as a peer-to-peer feedback. It was then rated for its usefulness in facilitating the reflection upon one’s own behavior, its ease of use, and its usefulness for the peer-to-peer feedback. The ratings were highly positive, around 4 in a 5-point scale. The CRM observational tool is therefore a useful, quick and easy solution to facilitate the debriefing, the peer-to-peer feedback and, most of all, the transfer of safe behavior from simulation to everyday practice.

Water management can improve the quality of valuable ecosystem services but can be costly to implement and the management costs are covered by national taxes collected by water users. Based on 30 valuation studies of water quality improvement from the Environmental Valuation Information System (EVIS) database provided by Korea Environment Institute (KEI), a meta-regression analysis was employed to measure the benefits that major river basins provided to the society. We compare these benefits to the costs, namely River Management Funds (RMFs) which are financial resources to support a variety of projects for managing and improving upstream water quality. Based on benefit-cost comparison, this study evaluates the efficiency of water resource management in South Korea. This study also provides policy options that are helpful to maintain the sustainability of water resource by improving the planning and performance of water management in the long run.

The domain of construction engineering and management is evolving rapidly. Optimizing resource positioning is crucial for meeting the challenges of efficiency and productivity. This research presents a novel methodology that incorporates Agent-Based Modeling (ABM), Building Information Modeling (BIM), and Bluetooth Low Energy (BLE) technologies, augmented with a Client Early Warning System (CEWS) and a Decision Support System (DSS). What makes this study unique is that it provides a real-time methodology for resource allocation and project management, tailored to address the complexities of modern construction projects. The application of this integrated technological framework marks a significant leap in construction management, empowering data-driven decision-making and significantly improving project efficiency. Empirically validated through a case study at Helwan University's College of Engineering and substantiated by expert surveys, this research showcases a transformative model for resource management. The findings not only underscore the practical applicability of this approach but also open new horizons for its adoption in global construction practices, offering insights for future research endeavors focused on expanding these methodologies to a wider range of resources.

Current and dynamic developments in green technologies have led to several innovation practices in the manufacturing sector only to become the top approaches used for achieving and accelerating sustainable development (SD) in the current business markets. In addition, manufacturing firms is in need of green innovation to be able to monitor and control their operations and enhance their environmental performance. However, regardless of its many benefits, the level of green innovation adoption and implementation is still lower than expected among manufacturing industries. Thus, this study aimed to minimize the gap by developing and validating a study model underpinned by Resource based view and Institutional theories, along with the Technology-Organization-Environment (TOE) framework in combination to convince firms to adopt green innovation. The study gathered data from 179 respondents using a survey distributed to manufacturing firms, after which data was exposed to Structural Equation Modeling (PLS-SEM) approach, for analysis. Based on the approach deliverables, all the integrated constructs of the model, namely perceived benefits, top management support, coercive pressure, normative pressure and mimetic pressure all predicted green management accounting practices. Moreover, green management accounting practices were found to directly and significantly affect green environmental performance. The developed integrated model provides a clear implication to decision-makers, indicating the importance of adopting and using green practices and innovative technologies for enhancing environmental performance. Based on the results from the reviewed advanced green technologies studies, there is considerable connection between green management accounting practices and environmental performance in the context of developing economies.

Background Lower respiratory tract infections (LRTIs), particularly pneumonia, are a leading cause of morbidity and mortality among children worldwide, especially in low-resource settings. The World Health Organization (WHO) Integrated Management of Childhood Illness (IMCI) guidelines currently recommend antibiotic treatment for all case of with pneumonia based on clinical signs along, which can lead to over-prescription of antibiotics and contribute to antimicrobial resistance (AMR). Point of care Lung Ultrasound (LUS) has emerged as a promising diagnostic tool in this context and has demonstrated potential in distinguishing between bacterial and viral pneumonia, thus enabling more targeted antibiotic use. This study aims to explore whether integrating LUS into the IMCI-based management of clinical pneumonia will better identify those who would benefit from antibiotic treatment, reducing antibiotic overuse without compromising health outcomes. Methods An open label randomized controlled trial will enrol 3500 children aged 60 days to 12 years presenting with IMCI-defined pneumonia. Participants will be enrolled in Senegal, Tanzania, and South Africa. Participants will be randomly assigned in a ratio of 1:1, stratified by site and age group (<5years, 5-12years) to either an intervention group, where LUS will guide antibiotic prescriptions utilising a predefined interpretation framework, or a control group receiving routine IMCI-based care. The co-primary outcomes of this study are the reduction in antibiotic use on the day of enrolment (Day 1) and rate of clinical failure by Day 8, using an acceptable clinical difference of 1.5x as a non-inferiority margin (delta of 2.5%). Secondary outcomes will include adverse drug reactions, cure rates, hospital admissions including in-patient duration, and cost-effectiveness. Data will be collected at baseline (Day 1), Day 8, and Day 29 post-enrolment. Discussion This study seeks to provide evidence on the clinical and public health benefits of using LUS to improve the diagnosis and management of paediatric pneumonia in low-resource settings. If this intervention proves effective it could contribute significantly toward reducing unnecessary antibiotic prescriptions, thereby addressing AMR. The potential for LUS to be integrated into routine clinical practice could revolutionize pneumonia management, especially in areas with limited access to advanced diagnostic tools. Trial Registration This trial will be registered on ClinicalTrials.gov and The South African National Clinical Trials Register (SANCTR) prior to participant enrolment.

The purpose of this article is to investigate and analyze the practices of scientific management within the County Departments of Sports and Youth (CDYS) in Romania. Conducted through a comprehensive research endeavor spanning from January to June 2022, the study utilized a standardized questionnaire as its primary instrument for data collection, distributed among CDYS directors. A notable finding from the study indicates that a significant proportion of respondents (44.9%) perceive notable disparities in the normative-functional elements governing the National Sports System in Romania compared to international standards. Moreover, the majority (51%) consistently adhere to regulatory acts, while a substantial portion (69.4%) underscore the importance of strict adherence to internal regulations in strategy formulation. Furthermore, an overwhelming majority (88.9%) acknowledge the substantial influence of available resources on strategy development, with collaboration with authorities primarily occurring through direct communication channels. Respondents prioritize multiple objectives (44.9%), affirm the necessity of various competencies (53.1%), and identify a lack of human and financial resources (59.2%) as the most significant obstacle. Notable achievements highlighted in the study include the improvement of the county's sports performance (53.1%). Respondents also believe that setting clear and realistic objectives (38.8%) is pivotal to enhancing CDYS management, while considerations in formulating strategies emphasize trends in the sports domain (42.9%) and the specific needs of the local sports community (42.9%). Overall, this research contributes to the understanding and promotion of more efficient practices in managing sports and youth departments within Romania's counties.

The role of human resource management practices and organizational loyalty dealing with employee retention has been widely explored and examined in the existing literature. The majority of human resource management researchers have focused on the role of policies and practices in different sectors related to employee retention. However, less attention has been given to identifying the practices and procedures common across all industries and sectors. The present study attempts to determine and examine the most important constructs like HR practices and organizational loyalty of employee retention management and proposes a comprehensive structural equation model to measure the impact of these constructs on employee retention. After an extensive review of the literature, organizational loyalty, and human resources management practices like recruitment and selection, compensation and benefit, training and development, supervision and evaluation have been identified as four essential practices dealing with employee retention and are common across all industries. These practices constitute the component factors of an employee retention management predicting employee’s turnover intentions. The survey method was adopted, and the original data obtained through the self-administered questionnaire. The valid one hundred and eighty-nine (189) respondents analyzed by using Lisrel 8.7, Structural equation model (SEM) analysis results shows that human resource management practices and organizational loyalty have strong positive impact on employee retention. This study tends to assist human resource managers and decision makers in selecting the appropriate motivating factors to retain and satisfy their employees

Purpose, This research aimed to examine the effect of green logistics (GL) and green human resource management (GHRM) on the performance of environmentally friendly manufacturing industries oriented towards sustainable development of organizations (SDO) through the role of sustainable production (SP) as a mediating variable. Methodology, A quantitative approach was employed through a standardized questionnaire to obtain data from 110 manufacturing industries in Indonesia that implemented environmentally friendly practices. Advanced statistical techniques, such as Structural Equation Modeling (SEM) and data analysis using Smart PLS (Partial Least Square) version 4 were utilized to analyze the collected data. Findings, The results showed that the model supported the statistical significance of all seven hypotheses and confirmed the direct and mediating effects of GL, GHRM, and SP on SDO. Practical implications, This research added critical insights into the theory and practice of GL and GHRM to realize SDO through the role of SP as a mediator in environmentally friendly manufacturing industries. Originality, This research contributd to the existing literature by adding to the effect of SP mediation on the relationship of GL and GHRM on SDO. There was no previous research that discussed the important role of SP mediation in influencing the relationship between GL and GHRM on SDO.

Is it true that the nuclear technology applied to electric energy generation offers a clean, safe, reliable and affordable i.e. sustainable alternative? Yes it is, but its impact on the environment strongly depends on the implementation bearing residual risks due to a human factor, technical failures or natural catastrophes. A full response is therefore difficult and can first be given when the wicked multi-disciplinary problems get well formulated and “solved”. These problems have multi-dimensional nature lying at the interface between: necessary R&D effort, the industrial deployment and the technology impact in view of the environmental sustainability including the management of produced hazardous waste. This enormous complexity indicates that just a description of the problem might represent a problem. The paper proposes a holistic approach to assess the nuclear energy systems potential with respect to sustainable performance applying Multi-criteria decision analysis with a suitable objective tree and a multi-level criteria structure and examines the trading-off techniques for ranking of the alternatives. The framework proposes a multi-criteria and multi-stakeholders treatment which can be used as a pre-decisional support towards an implementation of nuclear fuel cycles adapted to national preferences and priorities. Proposed approach addresses some aspects of the environmental footprint of nuclear energy systems. Advanced nuclear fuel cycles, previously investigated by the NEA/OECD expert group WASTEMAN, are analyzed as a case study. Sustainability facets of waste management, resource utilization and economics are in focus.

Effective and sustainable water resource management requires flexibility and adaptation to local contexts. Our study analyzes how local water self-governing associations have emerged, using an adapted version of the Combined IAD-SES framework, also known as CIS. Through a compara-tive analysis of two distinct Chilean cases, the research highlights the critical role of historical factors alongside institutional support, political landscapes, and financial realities in shaping current water management practices. The findings suggest that when these elements are aligned and supportive of local water users’ associations, positive outcomes emerge, leading to more effi-cient, sustainable, and user-centered water resource management. Furthermore, this study reveals how the experiences and successes of these local user associations have shaped national policies, particularly regarding the development of monitoring mechanisms and the promotion of public-private cooperation in water governance.

Fifth Generation (5G) communication technology is intended to offer higher data rates, outstanding user exposure, power consumption, and extremely short latency. Such cellular networks will implement a diverse multi-layer model comprising of device-to-device networks, macro-cells, and dissimilar categories of small-cells to assist customers with desired quality-of-service (QoS). This multi-layer model affects several studies that confront utilizing interference management and resource allocation in 5G networks. With the growing need and the lack of resources, the resource distribution problem desires to be focused capably to accomplish the traffic and to enhance network working. One of the utmost serious problems is to alleviate the jamming from the network in support of having a better QoS. However, there are limited review papers written on resource distribution, there is no particularize and organized review carry out in 5G resource allocation. Hence, this article covers and evaluates the argument using a classification of existing developing resource allocation schemes in 5G thoroughly by classifying the schemes to enhance the service quality. This survey comprises the discussion based on metrics used to evaluate the performance. It would also permit ahead beyond evidence on resource allocation methods in 5G and empowers the scholars to meet the present research areas to focus on.

The aim of this study is to investigate “resource portfolios” and total capital, as well as the degree of those resources capitalization, which representatives of different social classes in the modern Latvia have at their disposal. The amount and structure of “resource portfolio” and total capital of different social classes studied using the resource-asset-capital approach. The article presents results of the sociological survey of social stratification in modern Latvia on the example of its one region – Latgale (2019, n = 798, representative sample of the adult population), identifying social classes based on two objective (income and education) and one subjective (self-identification of respondents) criteria. Based on the example of the lower working class and the middle class, the authors proved that representatives of these polar social classes have a total capital of different amount, which is determined by two main reasons: 1) the lower working class has statistically significantly smaller “resource portfolio” than the middle class; 2) the lower working class is not so successful as the middle class in activating the resources at their disposal, turning them into their capital. These statistically significant two-level differences have to be considered when pursuing social policies on reducing differences between social classes.

The goods and services produced by forest vegetation ecosystem are the driving force for the rapid, high-quality and sustainable development of human society. In history, with the increase of population and economic development, there is an increasing demand for the quantity and variety of forest vegetation ecosystem products and services in a country or a region. To meet the demands, most of the original forest has become farmland, plantation and grass and crops land. As a result, the plant water relationship changed from equilibrium to non-equilibrium, which easily led to soil drought, soil degradation and vegetation decline in dry years or waste of soil water in rainy years in most of water-limited regions. In order to solve the question and realize the sustainable utilization of soil water resources and the high-quality sustainable development of forest, it is necessary to apply the limit theory of plant utilization of soil water resources and the vegetation carrying capacity theory in the critical period of plant-water relationship regulation to adjust the relationship between plant growth and soil moisture to obtain the maximum yield and benefit and realize high-quality and sustainable development.

This paper contributes to the debate on the impact of economic diversity and the resource curse on economic growth. We use dynamic panel data models on data on Canadian and US sub-national jurisdictions. We find evidence for a positive relationship between diversity and growth. Based on the Krugman Specialization Index, our analysis shows that the required threshold for not having the resource curse is 0.209. Above this threshold, the marginal contribution of natural resources to economic growth is lower for a more diversified regional economy than a less diversified one. We highlight the policy implications of these findings.

Project managers should balance a variety of resource elements in building projects while taking into account many major concerns, including time, cost, quality, risk, and the environment. This study presents a framework for resource trade-offs in project scheduling based on the Building Information Modeling (BIM) methodology and metaheuristic algorithms. First, a new metaheuristic algorithm called Fire Hawk Optimizer (FHO) is used. Using project management software and the BIM process, a 3D model of the construction is created. In order to maximize quality while minimizing time, cost, risk, and CO2 in the project under consideration, an optimization problem is created, and the FHO's capability for solving it is assessed. A predefined stopping condition is taken into account while doing 30 independent optimization runs to obtain the statistical metrics, such as the mean, standard deviation, and the required number of objective function evaluations. The results show that the FHO algorithm is capable of producing competitive and exceptional outcomes when it comes to trade-off various resource options in projects.

Recently, there has been a growing interest in distributed edge computing resource scheduling. For example, application scenarios such as intelligent traffic systems and Internet of Things(IoT) intelligent monitoring require the scheduling and migration of distributed resources. Distributed resource scheduling needs to consider the cost of resource scheduling, with the primary goal of finding the optimal strategy among various feasible solutions. There are different definitions for optimization objectives in different application scenarios, such as cost, transmission delay, energy consumption, etc. Current research mainly considers the optimization problem of local resource scheduling but needs more consideration of global resource scheduling. This paper defines a global resource scheduling problem for distributed edge computing and proves that the problem is NP-Hard. A heuristic solution strategy based on the Ant Colony Algorithm(ACO) was proposed, and Particle Swarm Optimization(PSO) was used to optimize the parameters of the ACO. Finally, an experimental comparative analysis was conducted to demonstrate that the algorithm proposed in this paper has good accuracy and iteration cost performance.

In the past decade, we have witnessed a dramatically increasing volume of data collected from varied sources. The explosion of data has transformed the world as more information is available for collection and analysis than ever before. To maximize the utilization, various machine and deep learning models have been developed, e.g. CNN [1] and RNN [2], to study data and extract valuable information from different perspectives. While data-driven applications improve countless products, training models for hyperparameter tuning are still time-consuming and resource-intensive. Cloud computing provides infrastructure support for the training of deep learning applications. The cloud service providers, such as Amazon Web Services [3], create an isolated virtual environment (virtual machines and containers) for clients, who share physical resources, e.g., CPU and memory. On the cloud, resource management schemes are implemented to enable better sharing among users and boost the system-wide performance. However, general scheduling approaches, such as spread priority and balanced resource schedulers, do not work well with deep learning workloads. In this project, we propose SpeCon, a novel container scheduler that is optimized for shortlived deep learning applications. Based on virtualized containers, such as Kubernetes [4] and Docker [5], SpeCon analyzes the common characteristics of training processes. We design a suite of algorithms to monitor the progress of the training and speculatively migrate the slow-growing models to release resources for fast-growing ones. Specifically, the extensive experiments demonstrate that SpeCon improves the completion time of an individual job by up to 41.5%, 14.8% system-wide and 24.7% in terms of makespan.

Bio-molecular reagents like antibodies required in experimental biology are expensive and their effectiveness, among other things, is critical to the success of the experiment. Although such resources are sometimes donated by one investigator to another through personal communication between the two, there is no previous study to our knowledge on the extent of such donations, nor a central platform that directs resource seekers to donors. In this paper, we describe, to our knowledge, a first attempt at building a web-portal titled Bio-Resource Exchange that attempts to bridge this gap between resource seekers and donors in the domain of experimental biology. Users on this portal can request for or donate antibodies, cell-lines and DNA Constructs. This resource could also serve as a crowd-sourced database of resources for experimental biology. Further, in order to index donations outside of our portal, we mined scientific articles to find instances of donations of antibodies and attempted to extract information about these donations at the finest granularity. Specifically, we extracted the name of the donor, his/her affiliation and the name of the antibody for every donation by parsing the acknowledgements sections of articles. To extract annotations at this level, we propose two approaches – a rule based algorithm and a bootstrapped relation learning algorithm. The algorithms extracted donor names, affiliations and antibody names with average accuracies of 57% and 62% respectively. We also created a dataset of 50 expert-annotated acknowledgements sections that will serve as a gold standard dataset to evaluate extraction algorithms in the future. Contact: madhavi@pitt.edu, madhavi@cs.cmu.edu Database URL: http://tonks.dbmi.pitt.edu/brx Supplementary information: Supplementary data are available at Database online.

The UK economy is overly reliant on unsustainable production and consumption practices, depleting finite resources at rates that will increase production costs, business risk and economic instability. This over-consumption produces emissions and waste that cause climate change and environmental degradation, impacting on the wellbeing of people in the UK and beyond. The Resource Recovery from Waste programme (RRfW) promotes a transition towards waste and resource management in a circular economy that restores the environment, creates societal benefits and promotes clean growth by engaging relevant actors in the transition process. RRfW collaborates with academia, government, and industry to co-produce a shared vision and approach to realise such a transition. Reflecting insights from RRfW’s government engagement, this article presents a positive outlook for changing the UK economy and society. It envisions a long-term future for waste and resource management that maximises the value of materials by circulating them in the economy for as long as possible. Four themes and an approach are proposed, including recommendations for regulatory instruments and a stable policy framework. It recommends further collaborative research to capitalise on opportunities for economic growth, innovation and resilient infrastructure whilst contributing to quality jobs and welfare in all four UK nations.

In recent years, a large-scale afforestation campaign has taken place in Inner Mongolia, China, to control desertification and soil erosion. However, the water consumption associated with large-scale afforestation significantly impacts the water resources in Inner Mongolia, resulting in a substantial ecological risk. This study aimed to evaluate the ecological risk of water resources caused by afforestation in the region. In this study, based on land cover data, Normalized Difference Vegetation Index (NDVI) data, and meteorological data, used trend analysis, water balance equation, and Water resources Security Index (WSI) index to analyze the ecological risks of water resources caused by afforestation in Inner Mongolia from 2000 to 2020. The results show that:(1) The afforestation area in Inner Mongolia was 5.37×104 km2 in 2000-2020; (2) Afforestation in arid and semi-arid areas leads to the reduction of water resources. (3) Afforestation reduces water resources in the study area by 0.76×108m3/year; (4) ~76% of afforestation regions face ecological risks of water resources.

The suppression of natural spaces due to the urban sprawl and increase of the built and agricultural environments has impacted the water resources quality, especially in areas with high population density, as the metropolitan regions. Considering the advance in Brazilian environmental legal framework, the present study aims to verify whether land use has still significantly affected water quality, through a case study in the Stones River watershed, a peri-urban river basin at a metropolitan region, Brazil. Analysis of physical-chemical indicators, collected at several sample points with different land-use (urban areas, commercial forestry, riparian forestry, mixed vegetation, pasture, and sugar cane plantation) at different seasons of the year (dry and rainy) were carried out. As a result, it was verified some statistically significant spatiotemporal effects on the of water quality caused associated to the land-use. In conclusion, in spite of the advances in the Brazilian law, land-use has still significantly affected the water quality, demanding public policies and decisions, so that effective compliance with legal guidelines is ensured.

The Energy Return on Energy Invested, EROEI, is known as an important parameter for evaluating the efficiency of energy-producing technologies. In this paper we examine the concept of EROEI from a general viewpoint, giving insights on a wider range of applications. In general, natural resources can be seen as energy stocks characterized by a “potential” that can be exploited by creating intermediate stocks. This transformation is typical of dissipative systems and for the first time we found that Lotka-Volterra model, usually confined to the study of biology of populations, can represent a powerful tool to estimate EROEI for some such systems, providing an understanding of the reason for the overexploitation phenomenon and, in some cases, the collapse of the exploiting system.

In January 2020, the World Health Organization declared a public health emergency and announced a new coronavirus disease (COVID-19), which would later go on to be declared as a pandemic, changing the global sphere and placing the economies of almost all countries under heavy stress. The airline industry, that had just begun recovering after facing crises one after another in the last two decades, from early 2000 due to 9/11, to the global financial crisis later, is now oce again facing an enormous challenge of closed borders and greater lockdowns due to the pandemic. Borders are closed, with very few planes are in the air, while the rest are grounded. The purpose of this paper is to give a conceptual understanding of the current pandemic situation and its consequences on the airline industry. The paper takes a unique perspective of human resource management (HRM) that is rarely used in the airline industry.

In the last decades, we have witnessed a spectacular information explosion over the Internet. Millions of users are consuming the Internet through various services, such as mobile applications, and online games. The service providers, at the back-end side, are supported by state-of-art infrastructures. Targeting on providing the services at scale, virtualization is one of the emerging technologies used in data centers and cloud environments to improve the quality of services. In this project, we aim to develop a dynamic resource management scheme based on virtual containers. It collects the runtime job progress from the running tasks and allocates the resources dynamically to improve the overall system performance.

Allama Iqbal Open University (AIOU) is the largest distance learning institute of Pakistan and providing education to 1.4 million students. This is fairly a large setup across the country where students are geographically distributed. Currently the system works on a manual approach which is not efficient. Allocation of tutors and study centers to students plays a key role in distance learning for a better learning environment. Assigning tutors and study centers to distance learning students is a challenging task when there is huge geographical spread. The utilization of geospatial technologies in open and distance learning can fix allocation problems. This research analyzes the real data of twin cities Islamabad and Rawalpindi. The results show that the geospatial technologies can be used for efficient and proper resource utilization and allocation, which in turn can save the time and money. The overall idea fits into improved distance learning framework and related analytics.

Integrated landscape approaches (ILA) aim to reconcile multiple, often competing, interests across agriculture, nature conservation, and other land uses. Recognized ILA design principles provide guidance for their implementation, yet application remains challenging, and a strong performance evidence-base is yet to be formed. A comprehensive literature review and focus group discussions with practitioners identified considerable diversity of ILA in actors, temporal, and spatial scales, inter alia. This diversity hampers learning from and steering these integrated planning approaches because of its intractable nature. Therefore, we developed a tool—an ‘ILA mixing board’—to structure the complexity of ILA into selectable and scalable attributes in a replicable way to allow planning, diagnostics, and comparative assessment of ILA. The ILA mixing board tool presents seven qualifiers, each representing a key attribute of ILA design and performance such as project flexibility, inclusiveness of the dialogue, and the centrality of the power distribution. Each qualifier has five (non-normative) outcome indicators that can be registered as present or absent. This process in turn guides planners, evaluators and other participating stakeholders involved in landscape management to diagnose the ILA type, and or its performance. We apply the ILA mixing board as a diagnostic tool to three ILA cases in Nicaragua, Madagascar, and the Congo Basin to show some of the many possible configurations of qualifiers on the mixing board. Overall, the tool allows comparative analyses of the complexity of ILA in a structured and manageable way.

Over the past few decades, aircraft Ground Handling Operations (GHO) have been investigated by numerous researchers. Some aspects of GHO have been more focused on than others due to their importance in the GH processes. For instance, GHO performance has been tackled from different perspectives while workers and vehicles allocation suffer from the lack of research in the area. This paper is a literary review of the research that has been conducted in different areas of GHO. It sheds light on GHO performance, resource allocation and scheduling, operations scheduling, vehicle routing, and workers and vehicles allocation.

Network Slicing serves as an enabler for the new fifth generation (5G) of mobile networks era as it enhances connectivity and diverse network slice requirements across various user devices. Optimally orchestrating these 5G network slices to meet the varying demands of applications with diverse quality of service (QoS) requirements and stringent high performance metric remains a challenge today [1]. In this paper, a smart and novel machine learning (ML)-based framework, which dynamically sub-slice in real time and optimally orchestrate a 5G network slice is proposed. Our proposed framework incorporates series of supervised machine learning algorithms with the use of LazyPredict module to find and suggest the best-fit model and make intelligent decisions for the specific network data environment being monitored in real time or analysed with historical dataset. Additionally, the framework employs reinforcement-learning techniques to predict future network traffic data pattern thereby optimally allocating network resources and orchestrating alternative best fit bespoke network slice. This alternative network sub-slice is orchestrated as required to reduce the risk of denial of service and improve user experience. This ultimately forms the proposed new vertical Enterprise Network as a Service: Company Network as a Service (CNaaS) in this study. The proposed approach is extensively evaluated through simulations using python with MATLAB and Simulink. This identified the important features and performance metrics for the 5G network data and demonstrated the efficiency and improvement in key metrics such as latency, throughput, jitter, packet loss rate, and available network resource utilisation compared to conventional methods [1]. The results highlight the importance of leveraging the right machine-learning algorithm for each unique network as the demand requirements changes. The proposed Enhanced Sub-Slice (eSS) module with embedded series of machine learning algorithms using the Lazypredict shows that one size does not fit all and demonstrates the significance of leveraging advanced ML techniques and ML pipelines in the context of 5G network slice management and orchestration.

How perceptual limits can be overcome has long been examined by psychologists. This study investigated whether visual cues, blindfolding, visual-auditory synesthetic experience and music training could facilitate a smaller frequency difference limen (FDL) in a gliding frequency discrimination test. It was hoped that the auditory limits could be overcome through visual facilitation, visual deprivation, involuntary cross-modal sensory experience or music practice. Ninety university students, with no visual or auditory impairment, were recruited for this one-between (blindfold/visual cue) and one-within (control/experimental session) designed study. A MATLAB program was prepared to test their FDL by an alternative forced-choice task (gliding upwards/gliding downwards/no change) and two questionnaires (Vividness of Mental Imagery Questionnaire & Projector-Associator Test) were used to assess their tendency to synesthesia. Participants with music training showed a significantly smaller FDL; on the other hand, being blindfolded, being provided with visual cues or having synesthetic experience before could not significantly reduce the FDL. However, the result showed a trend of reduced FDLs through blindfolding. This indicated that visual deprivation might slightly expand the limits in auditory perception. Overall, current study suggests that the inter-sensory perception can be enhanced through training but not though reallocating cognitive resources to certain modalities. Future studies are recommended to verify the effects of music practice on other perceptual limits.

In this paper, we present findings from a recently completed research about the use of enterprise resource planning (ERP) in travel, trade, and tourism businesses. Initially, we selected twelve existing travel, trade, and tourism ERP systems and preform practical analysis and testing of these systems via JMETER and Google chrome Lighthouse tools. In addition to that, an online survey was conducted (with an e-mail invitation and telephone call reminder) to collect data from three Pakistani Travel, Trade, and Tourism organizations. Furthermore, we adopted qualitative research practices by going through various research papers and research studies from journals, books, and internet, etc. This study is constructed on only secondary statistics. The goal of this article is to look at ERP systems in terms of their concept and use, as well as how they affect the modern travel business. An ERP system is a business system that integrates several corporate processes into a single database, such as supply chain management, manufacturing, financial management, and project management. ERP systems are utilized in a variety of businesses around the world, including in the manufacturing and service industries. Across a range of industries, service firms have recently spent significant resources on the implementation of Enterprise Resource Planning (ERP) systems to improve the efficiency, cost-effectiveness, and quality of service operations. To assist them to build their businesses, the travel, trade, and tourist industries all rely on an integrated information network. The work focuses on only one industry i-e Travel, Trade, and Tourism. This may limit the applicability of its broad conclusions to other industries or industries. The findings of this study compare and contrast the ERP implementation challenges faced by existing organizations of travel, trade, and tourism companies. This is the first empirical study on the difficulties surrounding ERP installation in travel, trade, and tourism companies. It is also the first study to compare ERP adoption in multiple companies in the areas of travel, trade, and tourism.

Background: Health expenditure in Japan is rising due to an ageing society while simultaneously pressure is mounting to increase spending in other areas such as defense, all in the context of a stagnating economy. Easing pressures on the health care sector through smoking-related cost reductions could thus be a cornerstone to mitigate the looming fiscal threat. Public health policy has encouraged cessation, but this approach has seen limited success. Focusing on cessation alone therefore may not be a sustainable policy strategy and other, new solutions need to be found to reduce smoking-related costs. Methods: Using a status-quo simulation based on hospital resources data derived from the Japanese Ministry of Health, Labour and Welfare, we examine the impact of heated tobacco products (HTPs) on the prevalence of four smoking attributable diseases (chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD), stroke and lung cancer) and related direct health care costs. Base case scenario assumes a switching rate from combustible smokers to HTPs of 50%. A risk reduction rate of 70% was applied. A sensitivity analysis was performed to determine the impact of parameter changes on output. Results: Our results indicate that 50% of smokers switching to use HTPs instead of combustible tobacco products could reduce the number of patients of the four smoking attributable diseases considered by 12 million cases equivalent to 454 billion JPY in savings. Prefectures located in the north and south of Japan would benefit most. Conclusion: Considering the heterogeneous prevalence rates across population groups and prefectures, a one-size-fits-all, centralized approach to tobacco control is not effective. Health authorities should strategically use multiple interventions to address smoking related health risks and costs. Switching to HTPs should be encouraged in parallel to smoking cessation, and educational efforts should aim to increase the awareness on health risks, especially among younger people.

Rice yield underperformance coupled with production instability in Odisha state of India is primarily due to low irrigation potential, the spatiotemporal disparity in rainfall pattern and relentless confrontation with biotic and abiotic stresses. System of Rice Intensification (SRI) was introduced in the state with the promises of higher production horizon. However, the present scenario of patchy adoption pattern has necessitated comprehensive study on dynamics and determinants of adoption of SRI. We found that, 57 percent farmers continued SRI, 28 percent discontinued and rest were practicing conventional rice farming. The focused group discussion among the farmers helped us to triangulate the determinants of SRI adoption.The increased SRI area allocation was observed at the expense of reduced number of adopting farmers. Economic scarcity of skilled labour, difficulties in transplantation and mechanical weeding, low irrigation potential and poor on-farm water management were major constraints in SRI practice. Farmers’ compliance in following different resilient SRI components that also varied spatiotemporally has resulted in realized incremental yield. SRI was proved to be resilient to severe drought during 2015-16 and yielded 27 percentage higher than conventional one with considerable decline in hazardous chemicals usage. Farmers’ decision on SRI adoption/discontinuation was analysed using probit regression analysis. The results indicated that, active social involvements of NGOs, on-farm training and demonstrations, and realized incremental rice income influenced SRI adoption. On the other hand, SRI area expansion hindered mainly because of infeasible land topography, area saturation and lack of farmers’ interest. The sustainability index was developed to analyse SRI’s performance to other conventional methods at farm level. Comparatively, SRI adopters benefitted in terms of incremental net return by enhancing ecological balance and sustained productivity of soil, use of eco-friendly and own farm inputs. Nonetheless, higher women labour displacement observed under SRI can be curtailed by providing training to young farm women.

Frequently Asked Questions (FAQs) systems rely on semantic symmetry similarity measuring between two sentences. To address the challenges of insufficient training data and limited domain-specific understanding in low-resource FAQs, this paper proposes a general framework, PDAMF-FAQ, to solve these issues. Firstly, we propose a paraphrasing-based data augmentation model that integrates syntactic information and edit vectors. Using a rule-based approach, it retrieves template sentences from a corpus and masks relevant words with special characters. Edit vectors between the original and paraphrase sentences are added to a pre-trained model's encoding layer to enhance the model's ability to learn the differences between the original and reference paraphrase sentences. Additionally, this paper presents a mixed-feature semantic matching model based on SimBERT. The model extracts keyword features from the text, replacing these keywords with special characters to construct intent features. These intent features, along with the user question and keyword features, are then concatenated to form the model's input. Experiments were conducted respectively on the paraphrasing-based data augmentation model, mixed-feature semantic matching model and their comprehensive application in a low-resource domain-specific FAQ. The experimental results show that the proposed framework effectively improve the performance of domain-specific FAQ system.

Circular Economy (CE) is noted as an emerging tool or framework to support sustainable production and consumption agenda. In addition, CE is aiming to be a trigger for redefining economic growth pathways to be sustainable, inclusive, and sensitive to ecological and environmental agendas and to focus its operational standards on co-creating societal benefits. Concerning the guiding principles and the standards of practice applied to implement and scale circular economy, this study will provide an overview of the water sector-specific circularity roadmaps and strategies in the Latin American Region. By using a semi-systematic review, document analysis, and qualitative assessment approach, we highlight framings and operational pathways, gaps, and needs within existing practices of circularity in the water sector. The results provide an overview of CE pathways at the national level of selected countries in LAR iterating those nations reflect various levels of advancement (low to high) with CE-focused innovations and policy support structures specific to the water and wastewater sectors. Towards the closing, the study is pointing to the ‘call for action’ to integrate outstanding advances and innovations in the circular economy within sectoral mandates for water and wastewater management, making an argument that circularity in the water sector could serve as an accelerator toward implementing the agenda outlined in Sustainable Development Goals (SDGs) and in particular for SDG 6 (water security for all).

Given that population COVID-19 vaccination does not appreciably reduce SARS-CoV-2 transmission, instead, the potential to reduce hospitalization has been used to justify coercive vaccine passports. We aim to use a recently published research study as an example in order to demonstrate how data can be misinterpreted and result in deriving misleading ethical and policy implications. Bagshaw et al wrote that unvaccinated patients with COVID-19 in Alberta, Canada “had substantially greater rates of ICU admissions, ICU bed days, and ICU related costs than vaccinated patients did. This increased resource use would have been potentially avoidable had these unvaccinated patients been vaccinated.” The authors in Bagshaw et al then concluded that their findings “have important implications for discourse on the relative balance of increasingly stringent public health protection (restrictions), including mandatory vaccination policies, and the sustainability and function of health system infrastructure and capacity during the ongoing COVID-19 pandemic.” Here we show the following. First, the effect of vaccination on intensive care admissions were grossly over-estimated due to several limitations of this and almost all other vaccine studies. Second, an effect of vaccination on access to acute care and on all-cause excess deaths was grossly over-stated due to several more likely causes being omitted from discussion and from the common narrative. Third, policy implications were overstated and at best unclear due to missing consideration of more relevant aspects required to inform policy. Overall, the data cannot support what Bagshaw et al called “increasingly stringent public health protection (restrictions), including mandatory vaccination policies”.

We describe a dataset that contains job description published on a popular online website in the information and technology sector. As the website focus mainly on United Kingdom based jobs, the data have a specific focus on this country. It contains 11.501 job vacancies and 13 related meta data information. The dataset is suitable for HR analysis using machine learning techniques such as natural language processing and neural networks.

Petrochemicals, which convert oil and gas into products such as plastics, are fundamental to modern societies. Chemists recognize their role in the design of materials and the adverse effects that these may have on the environment, preventing sustainable development. Several methodological frameworks and sustainability assessment approaches have been developed to evaluate the resources used in the petrochemical sector in terms of environmental costs. A combination of Life Cycle Assessment and Emergy Accounting - to assess the environmental support for resource use - is applied in this study of the PET production chain in Europe. The Unit Emergy Values of several intermediates are calculated or updated to facilitate discernment of the quality of energy used and the processes' efficiency. Several routes for synthesizing renewable para-xylene and ethylene glycol from biomass are discussed and confronted with the efforts focused on recycling and recovering the final product providing concurrently a procedure and a valuable data set for future CP actions. The results show that understanding the efficiencies changing across the production chain may help stakeholders make wise choices as to where and when interventions to promote a circular economy are most effective along a petrochemical production chain.

Shrinking farm size and fragile farm resources pose a significant challenge to the sustainability of small-scale farms. Efficient resource utilization in small-scale farms is crucial to achieving farm sustainability through endogenous mechanisms. However, the precise mechanisms to integrate physical resources to achieve farm sustainability are not very clear yet. By capturing the interaction among farm resources as a network phenomenon, we identify the discrete resource interactions (RIs) in different types of small-scale farms of Indian Sundarbans, which are associated with higher farm sustainability. Thirty-two linkages, 11 reciprocal linkages, 22 triads, and three ‘core elements’ that occurred and cooccurred on highly sustainable farms are found to be critical in achieving farm sustainability. Using the properties of resource interaction networks as explanators of farm sustainability, we anticipate that sustainability in small-scale farms can be achieved by strategically creating new RIs on the farm. However, there may be limitations to such achievement depending on the nature of RI and type of farm. The analytical approach helps to understand the structural basis of sustainability in small-scale farms, and this approach can be used to achieve farm sustainability through the strategic integration of existing farm resources in the smallholder systems.

The advent of deep learning has completely reshaped our world. Now, our daily life is fulfilled with many well-known applications that adopt deep learning techniques, such as self-driving cars and face recognition. Furthermore, robotics developed more forms of technology which share the same principle with face recognition, such as hand pose recognition and fingerprint recognition. Image recognition technology requires a huge database and various learning algorithms, such as convolutional neural network and recurrent neural network, that requires lots of computational power, such as CPUs and GPUs. Thus, clients could not be satisfied with the computational resource of the local machine. The cloud resource platform emerged at a historic moment. Docker containers play a significant role of microservices-based applications in the next generation. However, it could not guarantee the quality of service. From clients’ perspective, they have to balance the budget and quality of experiences (e.g. response time). The budget leans on individual business owners and the required Quality of Experience (QoE) depends on usage scenarios of different applications, for instance, an autonomous vehicle requires real-time response, but, unlocking your smartphone can tolerate delays. Plenty of on-going projects developed user-oriented optimization resource allocation to improve the quality of the service. Considering the users’ specifications, including accelerating the training process and specifying the quality of experience, this thesis proposes two differentiate containers scheduling for deep learning applications: TRADL and DQoES .

The current paper has examined the effect of both export product diversification and poverty on non-resource tax revenue in developing countries. The analysis has used an unbalanced panel dataset of 111 countries over the period 1980-2014. Based on the Blundell and Bond two-step system Generalized Methods of Moments technique, the empirical analysis has shown interesting findings. Export product concentration and poverty influence negatively non-resource tax revenue over the full sample, but this effect varies across countries in the sample. Furthermore, the effect of export product diversification on non-resource tax revenue performance depends on the level of poverty. It appears that export product diversification influences positively non-resource tax revenue performance in countries that experience lower poverty rates. From a policy perspective, these findings show that policies in favour of diversifying export product baskets and reducing poverty would contribute to enhancing non-resource tax revenue performance in developing countries.

Solar Insolation is the major contributor of earth’s radiation and energy budget. The insolation reaching the surface is a prime input for eco-physiological processes such as evapotranspiration and photosynthesis. Therefore, it is as critical component to assess bio-energy and bio-fuel resources. It is also a crucial input to crop simulation model for yield forecasting and its further applications in solar energy solutions. Although ground observations are better for accuracy purpose, they have challenges of maintenance, regular calibration and upkeeping etc. This call for the continuous spatio-temporal satellite based observations barring the acceptable accuracy. In case of INSAT3D/3DR, Bhattacharya et al. (2015) have derived the surface insolation product which is being used widely. We propose a method of improvement in this product. It is envisaged that a correction applied with the help of ground truth estimates may enhance the utilization of insolation products derived from INSAT3D/3DR datasets. In the present study, surface insolation product derived from INSAT 3D/3DR data at an interval of 30 minutes each (collectively 15 minutes interval) with 4 km spatial resolution was used for duration from May-2017 to Apr-2019 over Nagpur. Ground truth observations for same duration were carried out with CNR4, which were used to correct the INSAT 3D/3DR surface insolation product using the statistical best-fit method. Corrected INSAT 3D/3DR products are found correlating with the ground values well with differences of approximately < 1 W/m2. Best-fit parameters evolved in the present study uses only 2 years of simultaneous ground and satellite data which can be further improved by multi-year data base. We propose better utilization of INSAT 3D/3DR based surface insolation products in the assessment of solar resource mapping over Nagpur (and possibly other regions, such as Bhandara) with the help of best-fit parameters as assessed in the present study.

Objective:The application of information technology (IT( is fundamental in the hospitals to stay competitive.In this regard, recognizing the main risks to the implementation of IT in hospitals can provide vastopportunities to improve its efficiency and help to make strategic decisions. This study aimed tosearch for the main risks of implementation of IT projects in the hospitals of Tehran. Methods: This was a practical and cross-sectional study which was conducted in the 18 hospitals of Tehran,Iran, 2018; in which a sample of 65 members were studied. The required data were collected using a questionnaire to examine seven main risks, including market, project management, human resources, technical, organizational, financial, strategic risks. The collected data were analyzed using SPSS 19.0. Additionally, the method used to test the risks in this study was structural equation modeling, which was ran using LISREL 9.30. Results: The results showed that among the seven main risks of to the implementation of IT in hospitals, the highest and lowest means were related to the human resource risks and the market risks, respectively. Also, according to the SEM, human resource risks and market risks had the highest and lowest effects, respectively. Conclusion: Announcing the use of IT in the hospitals, holding conferences about new IT developments with employees, suitable training, encouraging them to use IT tools, providing a motivating atmosphere to use IT tools for employees, are a few effective ways of overcoming the human resource risks.

The potential of Sorghum bicolor L. (Moench) (Epuripur 1995) to phytoremediate petroleum oil-adulterated soils from an automobile repair workshop and the effect of enhancement factors: NPK fertilizer, cow dung and sewage sludge in in situ phytoremediation of the soil by the plant were assessed in this study. 50kg of petroleum oil-contaminated soil was collected from the workshop and divided into five equal portions. Four portions were potted with four sorghum plants with three subjected to equal amounts of enhancements (5%w/w) under normal growth conditions for 72 days. Representative soil samples were collected from spots at depths of 0–10 cm and 10–20 cm from the potted soils and subjected to Soxhlet oil extraction after 72 days. Experimental results revealed that S. bicolor survived in the petroleum oil-contaminated soils. Amendment of the petroleum oil-vitiated soils with cow dung, sewage sludge and NPK fertilizer augmented the remediation capacity of Epuripur 1995 by 12.5%, 6.3% and 9.1%. Addition of cow dung to crude oil contaminated soils could make such soils fully reestablished for agricultural activities. Further research aimed at determination of the phytoremediation potential of cereals such as corn, barley, rye, millet should be done.

Many species of plants are found in regions to which they are alien. Their global distributions are characterised by a family of exponential functions of the kind that arise in elementary statistical mechanics (an example in ecology is MacArthur's broken stick). We show here that all these functions are quantitatively reproduced by a model containing a single parameter – some global resource partitioned at random on the two axes of species number and site number. A dynamical model generating this equilibrium is a two fold stochastic process and suggests a curious and interesting biological interpretation in terms of niche structures fluctuating with time and productivity; with sites and species highly idiosyncratic. Idiosyncrasy implies that attempts to identify a priori those species likely to become naturalized are unlikely to be successful. Although this paper is primarily concerned with a particular problem in population biology, the two fold stochastic process may be of more general interest.

This paper explores the dynamics of political competition, resource availability, and conflict through a simulation-based approach. Utilizing agent-based models (ABMs) within an evolutionary game theoretical framework, we investigate how individual behaviors and motivations influence collective outcomes in civil conflicts. Our study builds on the theoretical model developed by Basuchoudhary et al. (2023), which integrates factors such as resource availability, state capacity, and political entrepreneurship to explain the evolution of civil conflict. By simulating boundedly rational agents, we demonstrate how changes in resource availability and state capacity can alter the nature of civil conflict, leading to different equilibrium outcomes. The findings highlight the importance of understanding individual motivations and adaptive behaviors in predicting the stability and resolution of conflicts. This research contributes to the growing body of literature on the use of agent-based models in evolutionary game theory and provides valuable insights into the complex interactions that drive civil violence.

Dams significantly impact the environment, industry, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy levels precisely within the framework of Integrated Water Resource Management (IWRM). Our research proposes that combining the physical model of evapotranspiration using the Penman–Monteith equation with data-driven models based on historical reservoir data, weather data, and consumption data is essential for accurately predicting occupancy levels. We implemented various prediction models, including Random Forest, Extra Trees, Long Short-Term Memory, Orthogonal Matching Pursuit CV, and Lasso Lars CV. To strengthen our proposed model with robust evidence, we conducted a statistical test on the MAPE values. We achieved a remarkable accuracy in predicting the occupancy level one month ahead, with an error margin of just 1% using Extra Trees. This study represents a pioneering effort in providing guidance and proposing a hybrid model in this field.

Promoting the sustainable development of resource-based cities is of great significance to the ecological protection and high-quality development of the Yellow River Basin. Combining the environmental status and economic development, we objectively evaluate the ecological safety level of resource-based cities in the Yellow River Basin, compare and analysis the main factors affecting the ecological safety of different resource-based cities, and formulate precise and differentiated ecological safety protection measures in river basins to achieve high-quality development. Taking the Yellow River Basin as an example, this paper analyses the ecological level of the Yellow River Basin based on the theory of sustainable development, calculates the ecological safety level of 30 resource-based cities in the Yellow River Basin from 2006 to 2020 by TOPSIS model, and makes convergence analysis by classification. Through the DID empirical test of sustainable development planning policy on ecological safety factors, distinguish the development characteristics of different resource-based cities. The results show that: (1) the ecological safety level of resource-based cities in the Yellow River Basin is generally developing well, and there are differences among different types of resource-based cities; (2) the kinetic energy of sustainable development of resource-based cities in the Yellow River Basin is mainly industrial transformation, and the power of science and technology is insufficient; (3) local governments pay limited attention to environmental protection, and the sustainable development planning of resource-based cities in 2013 mainly promotes the adjustment of industrial structure of resource-based cities.

Carsharing has become increasingly popular in recent years as a sustainable transportation solution, offering individuals access to shared vehicles on a short-term basis. One-way carsharing, in particular, presents unique challenges due to its flexible nature, allowing users to pick up and drop off vehicles at different locations within a designated service area. This flexibility increases the service ridership but comes at the expense of vehicle imbalance among the stations, as some stations may have excess vehicles while other stations have vehicle shortages. Therefore, carsharing companies need to decide on strategies to ensure a balanced distribution of vehicles among the stations. This is essential as unbalanced vehicle distribution can lead to the unavailability of vehicles when needed or, conversely, result in an increased number of unnecessary rebalancing trips, thereby exacerbating traffic congestion and environmental pollution. Such issues can potentially undermine the overall contribution of carsharing to urban sustainability. To this end, this paper reviews the vehicle imbalance problem that arises in this field and the solution algorithms that solve them.

To explore the forage quality of wild oat populations under different growth environments, the basic nutrient composition and forage quality of five wild oat populations in Israel were compared. Ash, crude protein, and soluble sugar content of oats were significantly (P<0.05) related to some forage quality and possible environment of population origin. Among them, the environmental factors of origin had significant effects on the indicators of ash, crude protein, and soluble sugars of oat straw, but less on the contents of crude fat, total phosphorus, and total potassium. Meanwhile, the annual rainfall and the number of days of rainfall in the country of origin greatly influenced the cellulose content. Finally, we investigated the similarity of feeding values of different populations using PCA analysis. The results showed that, in conclusion, the higher variability of wild oat populations may be a new possibility brought by feeding oats.

As a model for economic and ecological systems, replicator dynamics represents a basic form of agent competition for finite resources. Here, we investigate the effects of stochastic resetting in this kind of processes. Random reset events abruptly lead individual resources to a small value from which dynamics must start anew. Numerical results show that resource distribution over the population of competing agents develops highly nonuniform profiles, exhibiting clustering and fluctuations with anomalous dependence on the population size. This non-standard statistical behavior jeopardizes an analytical treatment based on mean-field assumptions. We propose alternative simplified analytical approaches which provide a stylized description of entropy evolution for the clustered distribution of resources and explain the unusually slow decrease of fluctuations.

The emergence of the circular economy, and the evolving paradigms in the treatment and management of wastewater, have opened up an opportunity for co-digestion of organic waste (i.e., food waste) with sewage sludges to enhance resource recovery at wastewater treatment plants (WWTPs). This paper reviewed the potential for anaerobic co-digestion of food waste and sewage sludges, as well as alternative sustainable food waste handling systems in South Africa. The promotion of the circular economy by the latest national solid waste management strategy and the ongoing efforts for resource recovery by the wastewater sector suggests that anaerobic co-digestion of food waste and sewage sludge is possible in South Africa. Furthermore, an integrated food waste disposer (FWD) system was identified as a sustainable alternative for food waste handling. To formulate a roadmap for future food waste and sewage sludge co-digestion implementation, a multi-disciplinary investigation is required to bridge the literature gap.

Microfluidics technology has not impacted the delivery and accessibility of point of care health services like diagnosis of infectious disease diagnosis, monitoring health or delivering interventions. Most microfluidics prototypes from academic research are not easy to manufacture with industrial scale fabrication techniques and cannot be operated without complex manipulations of supporting equipment and additives such as labels or reagents. We propose a label- and reagent-free inertial spiral microfluidic device to separate red blood, white blood and dendritic cells from blood fluid for applications in health monitoring and immunotherapy. We demonstrate that using larger channel widths in the range of 200 to 600 µm allows separation of cells into multiple streams according to different size ranges and we utilize a novel technique to collect the closely separated focused cell streams without constricting the channel. When tested on actual human blood cells, 77% of dendritic cells were separated and 80% of cells remained viable after our assay. Our contribution is a method to adapt spiral inertial microfluidic designs to separate more than two cell types in the same device which is robust against clogging, simple to operate and suitable for fabrication and deployment in resource-limited populations.

The employee turnover literature to date is teeming with process models that assume only organizational and personal factors contribute to employees’ intentions to leave and stay. However, with the average percentage variance explained still only around 50%, it is clear that other factors play a substantial role in these decision-making processes. One possible explanation is to explore the unique role that clients play in employees’ intentions to stay or leave. This paper extends the conceptualization of the role that clients play on employee’s intentions to stay and leave their roles, particularly within service industries such as health care. This paper presents the results of two qualitative studies that examine employees’ decision-making process for turnover and retention intentions. The first study explores the factors influencing employees’ intentions to stay and leave the Australian aged care sector (N = 420) and the second study explores prison health care nurses in the United States (N = 175). The results of both studies reveal that client-related factors affect the choice to stay within a job and, in some cases, are the favoured aspect of the role. Therefore encouraging client-staff relationships will expand job satisfaction and therefore increase retention of employees within both contexts.

The primary attraction of IaaS is providing elastic resources on demand. It becomes imperative that IaaS-users have an effective methodology for learning what resources they require, how many resources and for how long they need. However, the heterogeneity of resources, the diversity resource demands of different cloud applications and the variation of application-user behaviors pose IaaS-users big challenge. In this paper, we purpose a unified resource demand forecasting model suiting for different applications, various resources and diverse time-varying workload patterns. With the model, taking input from parameterized applications, resources and workload scenarios, the corresponding resources demands during any time interval can be deduced as output. The experiments configure concrete functions and parameters to help understanding the above model.

Mexican yuca (Yucca decipiens Trel.) is native to the semidesert region across north-central Mexico. Based on its empirical medicinal uses, the flour produced from leaves and stems of yuca was evaluated to determine its agro-industrial and medicinal uses (food, beer, and mezcal). The flour was characterized based on the granulometry, rheology, texture, and functional properties, which were analyzed with the RStudio software. The results indicate that the water absorption capacity of yuca flour (data here) is similar to wheat flour; however, it absorbs half the volume of oil than wheat flour. Therefore, it has a hydrophilic behavior similar to that of wheat and a lower oil requirement for formulas. The expansion capacity of yuca flour is like that of wheat flour, showing an ideal gluten content for agro-industrial purposes (food) that require this structural component. The flour had a remarkable foam formation, high stability, and low-fat content. These results are particularly important for foods that require foaming properties. Fermentation matched the parameters of the 56-60 Physicochemical Test Methods of the Cereal &amp; Grains Association; consequently, their flours are classified as equal to flours produced from soft and weak wheat, supporting their use for fermentation processes. The internal friction values (0.85-0.92) suggest a limited flow; however, its high density shows a fine granulometry that facilitates the sacking, handling, and storing of the flour, complying with the Mexican standards, and allowing it to be added to combinations aimed to enrich food.

This research introduces a novel approach to evaluate energy utilization in agribusiness within a cost-based framework, addressing the complexities associated with uncertain input energy content. The study focuses on the agricultural sector in Latin America, with particular emphasis on banana plantations, examining the sector's growing dependence on energy-intensive methods and inputs. We differentiate between technical and allocative efficiency, employing a frontier-based methodology. The analysis encompasses data from 21 state-owned banana plantations in Latin America, revealing significant room for improvement in energy efficiency. Our findings indicate an average Technical Efficiency of 0.69, Allocative Efficiency of 0.91, and overall Energy Efficiency of 0.59, suggesting potential reductions in input and energy consumption of up to 38%. The study underscores the importance of addressing both input mismanagement and misallocation to enhance energy efficiency. While the proposed methodology offers valuable insights for public sector enterprises, particularly in agriculture, further research is necessary to extend this approach to scenarios with uncertain input energy content, potentially incorporating stochastic frontier analysis or fuzzy logic methodologies.

The decline in global pollinator populations has raised serious concerns about the sustainability of ecosystems and agricultural productivity. Urban environments, often characterized by habitat loss and environmental stressors, are considered challenging habitats for pollinators. However, urban green spaces, including street and ornamental trees, can play a crucial role in supporting pollinators by providing essential food resources. This study aimed to evaluate the sugar and amino acid production of eight urban tree species to determine their potential contributions to pollinator nutrition. Our findings revealed significant variability among species regarding nectar volume, sugar content, and amino acid composition, emphasizing the importance of considering multiple factors when assessing the utility of floral resources. Consequently, Tilia amurensis, Heptacodium miconioides, Aesculus turbinata, and Wisteria floribunda demonstrated superior potential in supporting urban pollinator communities. Contrastingly, Sorbus commixta, Styrax japonicus, and Cornus kousa, often favored for their aesthetic appeal, showed less potential. This study underscores the need for urban tree selection that prioritizes species that provide diverse and abundant nutritional resources for pollinators while balancing aesthetic and ecological values. Our results offer a foundation for better-informed urban biodiversity management and contribute to creating pollinator-friendly urban environments.

This study examines the impact of natural resource exploitation, renewable energy consumption, economic growth, biocapacity, and information and communication technologies (ICT) on the ecological footprint of BRICS+ nations using panel data from 1991 to 2019. Advanced econometric techniques, including FMOLS, DOLS, CCR, and GMM, reveal significant findings relevant to sustainability-focused policymakers. The results show that natural resource exploitation significantly contributes to environmental degradation, while renewable energy consumption mitigates these effects to varying degrees across the studied countries. Although economic growth is crucial, it worsens ecological footprints unless accompanied by sustainable practices. ICT and biocapacity emerge as key factors in promoting environmental sustainability, with ICT enhancing innovation and efficiency to reduce ecological impacts. The study emphasizes the need for tailored policy frameworks that align economic growth with environmental sustainability, advocating for the adoption of renewable energy, eco-innovations, and green finance initiatives suited to the unique conditions of BRICS+ nations. The findings aim to guide policymakers in advancing sustainable development and mitigating the adverse environmental impacts of rapid industrialization and urbanization, while contributing to the global discourse on achieving a balance between economic growth and ecological well-being in developing countries.

Agriculture is the largest sector of Nepalese economy among the south-Asian developing countries that contributed 26.98 % share of Gross Domestic Product (GDP) in the year 2018/19. The capacity to derive maximum output by per unit of resource is resource use efficiency. Resource use efficiency of a producer explains the comparison between inputs and output value. Potato is staple vegetable in every parts of Nepal. Cobb-Douglas production function is used to predict the significance of inputs in income from cash crops. Due to lack of optimum utilization of available resources in cultivation of vegetable like lack of technical knowledge, low education level, and extensive service the productivity and production is low in Nepalese economy. It ensures vegetable security in Nepal. Though, there are extensive researches on the resource use efficiency and productivity issues on agriculture, very few papers explored in the case of Sudurpaschim province of Nepal OBJECTIVE: This study had the specific purposes: (1) To identify the factors affecting in resource use efficiency and productivity of potato production (2) to utilize the resource use efficiency and productivity in potato production and (3) to show the type of returns to scale in potato production. METHODS: Applying post-positivist paradigm and quantitative causal survey research design, it explored the resource use efficiency and productivity through the semi-structured questionnaire from 140 households by random sampling. Data analysis was carried out by multiple regression model using Cobb-Douglas production function. SPSS software was used to find quantitative results. From the analysis of data using mean, and SD, t-test, analysis of variance, Durbin-Waston test, Marginal Value Product and marginal Factor cost, findings of this research was emerged. RESULTS AND DISCUSSION: The major findings: education level, extension service, farm size and pesticide cost were significant variables in potato production; seed, hired labor, Family Yard Manure and pesticide cost were overused and needed to decrease their cost for optimum allocation and there was decreasing returns to scale in production. This study emphasized on increasing education level, extensive service and farm size for optimum utilization of resources and increasing returns to scale. SIGNIFICANCE: Barriers to Indian potatoes and increasing returns to scale are only possible through protection policy of government. For optimum use of resources and increasing returns to scale, government can introduce capital intensive technique. This study can be implied in policy making and improving existing situation of agriculture, and allocating budget.

Iron tailings are solid waste generated after the mining of iron mines, which have caused serious damage to the surrounding ecology and environment and have attracted widespread attention. By analyzing the formation causes, main components, and environmental effect mechanisms of iron tailings, the article systematically elaborates on the degree and mechanism of the harm of iron tailings to water bodies, atmosphere, soil, and ecosystems, and evaluates its impact on human health and sustainable development of society. On this basis, specific prevention and control measures have been proposed to reduce iron tailings emissions, realize resource recycling, and restore ecosystems. Research shows that iron tailings containing heavy metals can widely pollute the environment through dissolution and migration, and have significant biological toxicity effects. The research results can provide a theoretical reference for decision-making departments to formulate scientific and reasonable iron tailings disposal and ecological protection policies. It is recommended to strengthen specific research on the infiltration of toxic and harmful components in tailings in the future, and develop efficient and stable solidification technology, and vegetation-microbial joint restoration models to further reduce the ecological and environmental risks of iron tailings.

Real-time data processing and decision-making are increasingly crucial in various applications, driven by the continuous influx of data streams. Event-driven AI workflows within serverless computing environments offer a promising approach to handle these real-time demands efficiently. This paper presents a framework for simulating and analyzing the performance characteristics of such workflows. Our proposed approach utilizes simulated data with varying event rates and durations to investigate the impact on key performance metrics like latency, throughput, and resource utilization. This enables a comprehensive evaluation of the inherent trade-offs within event-driven AI systems. The key findings reveal a trade-off between latency and throughput. As the event rate increases, average processing latency generally increases while average throughput increases. Resource utilization remains relatively stable across different event rates in the simulated scenarios (e.g., 75.55\% at 2 events/second, 74.51\% at 10 events/second). This framework provides a valuable tool for understanding the performance characteristics of event-driven AI workflows and optimizing resource allocation strategies.

This study draws on both the theory of resource-based view and the theory of stakeholders to investigate the effects of Green Human Resource Management (Green HRM) practices on Sustainability and Employee Health Orientation (EHO) through the mediating effect of Environmental Performance (EP). Based on a cross-sectional survey questionnaire, the data were collected from 318 service and manufacturing firms across India. Partial least squares structural equation modeling (PLS-SEM) was applied to determine the link among constructs. The findings of the empirical evidence reveal that Green HRM practices have positive and significant effects on EP, Sustainability and EHO. Also, the findings demonstrate that EP partially mediates the link between Green HRM practices and Sustainability. Similarly, the findings establish that EP partially mediates the link between Green HRM practices and EHO. Finally, the potential implications of this research and future research for practitioners and scholars are addressed.

The healthcare situation in Africa is characterized by limited resources and a high burden of disease, presenting significant challenges to the healthcare systems in the region. The integration of Artificial Intelligence (AI) technologies in African healthcare holds immense potential for addressing these challenges and improving health outcomes. This paper aims to provide a comprehensive understanding of the current healthcare landscape in Africa and the urgent need for AI-driven solutions.In addition to discussing the potential applications of AI in African healthcare, such as disease diagnosis, treatment planning, and medical imaging analysis, this manuscript seeks to shed light on the specific challenges hindering the implementation of AI in the region. By providing detailed examples and case studies, we aim to offer insights into the obstacles that need to be overcome and potential solutions that can be pursued.By leveraging AI technologies, Africa has the opportunity to revolutionize its health systems, bridging the gap in access to quality healthcare across the continent. The objective of this review article is to not only highlight the potential benefits that AI can bring to African healthcare but also to emphasize the urgent need for action and collaboration in implementing and scaling up AI innovations in the region. By doing so, African countries can pave the way for improved health outcomes and ensure that their populations receive the healthcare services they deserve.

Classroom resource allocation is a challenging problem in educational institutions. The use of cloud computing can aid in the optimization of this process by allowing resource providers to allocate resources to users in a timely and efficient manner. In this paper, we propose a modified Salp Swarm Algorithm (SSA) to optimize the allocation of classroom resources. The proposed approach involves generating a population of salps representing potential solutions to the resource allocation problem and updating the positions of the salps iteratively based on a fitness function and mathematical equations. We conducted simulations of the proposed algorithm on various numbers of tasks and sources to evaluate its performance. The results indicate that the modified SSA algorithm can efficiently allocate classroom resources, achieving lower completion time of tasks (makespan) with lower costs. This paper contributes to the literature by introducing an effective approach to solve the classroom resource allocation problem.

Diopatra neapolitana (Delle Chiaje, 1841) is one of the most exploited polychaete species in European waters, particularly in Ria de Aveiro, a coastal lagoon in mainland Portugal, where the overexploitation of this resource has led to a generalized decline of local populations. In an attempt to reduce the impact of harvesting, several management actions were implemented, but, illegal poaching still fuels a parallel economy that threats the sustainable use of this marine resource. The present study evaluated the combination of fatty acid profiles and elemental fingerprints of the whole body and jaws, respectively, of D. neapolitana collected from four harvesting locations within Ria de Aveiro in order to determine if their geographic origin could be correctly assigned post-harvesting. Results showed that both fatty acid profiles and elemental fingerprints differ significantly among locations, discriminating the geographic origin with higher accuracy when combining these two natural barcodes than when employing each individually. The present work can therefore contribute for the implementation of an effective management plan for the sustainable use of this marine resource, making possible to detect if D. neapolitana was sourced from no take zones and if it was collected from the place of origin claimed by live baits traders.

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

Tigrai region is one of the potential beekeeping areas in Ethiopia, which is endowed with diversified honeybee plant species. However, its honeybee plants are deforested and degraded due to natural and anthropogenic pressures. Area exclosures (AEs) has been practicing for the last three decades restore degraded habitats of the plant species in Ethiopia. Empirical data on the effectiveness of AE is still limited for restoration of honey bee plant species; this study was conducted to assess the roles of AE for species composition, density, and diversity and regeneration status of honeybee floras. Sixty quadrats were installed, 36 in AE and 24 in CGL (communal grazing lands) using a systematic sampling method. The study showed that a total of 42 honey bee plant species belonging to 23 families in AE and 33 species belonging to 17 families in CGL were recorded. The study also indicated a significant increase in the abundance, diversity, regeneration and the structure of the honey bee plant species was found in the AEs. The list presented herein provides a guide to stakeholders in establishing of AE for the foraging resource of honeybee in order to enhance the abundance, diversity and regeneration and structure. Thus, AE has made a significant contribution to solving the shortage of honeybee forages in the beekeeping industry. Therefore, the development of beekeeping is integrated with conservation of natural resources.

The circular economy is based on maintaining a circular flow of resources. Policymakers face difficulties in balancing national security and circular economy aspects in the field of mineral resources policy. With a uniquely expanded input-output table, this study estimates the final destination of several critical minerals (lithium, cobalt, yttrium, lanthanum, cerium, neodymium and dysprosium) and base metals (iron, copper and aluminum) in the Japanese economy for 2015. Our model shows quite a good similarity between the estimated composition and the actual data of products. The results reveal a detailed distribution of critical minerals and indicate prioritized implementation for creating and maintaining a circular flow of critical minerals. Developed decision flow guidance provides a comprehensive approach to national security and circular economy aspects for policymakers. For further actions, inclusive indicator development is required for policymakers to support the determination of implementation possibilities from social and technological aspects.

Nature provides plenty of good examples for simple and very efficient joint assemblies. One example is the enormously flexible cervical spine of American barn owls, which consists of 14 cervical vertebrae. Each pair of vertebrae produces a comparatively small individual movement in order to provide a large overall movement of the entire cervical spine. The biomimetic replication of such joints is difficult due to the delicate and geometric unrestricted joint shapes as well as the muscles that have to be mimicked. Using X-ray as well as micro computed tomography images and with the utilisation of additive manufacturing, it is possible to produce the owl neck vertebrae in scaled-up form, to analyse them and then to transfer them into technically usable joint assemblies. The muscle substitution of these joints is realised by smart materials actuators in the form of shape memory alloy wire actuators. This actuator technology is outstanding for its muscle-like movement and for its enormous energy density [1,2]. The disadvantage of this wire actuator technology is the low rate of contraction, which means that a large length of wire has to be installed to generate adequate movement. For this reason, the actuator wires are integrated into additively manufactured carrier components to mimic the biological joints. This results in joint designs that compensate for the disadvantages of the small contraction of the actuators by intelligently installing large wire lengths on comparatively small installation spaces, while also providing a sufficient force output. With the help of a test rig, the developed technical joint variants are examined and evaluated. This demonstrates the technical applicability of this bionic joints.

Background: Cervical cancer is the fourth most common cancer in women globally despite being a largely treatable and preventable malignancy. Developing countries account for over 80% of all new cases of cervical cancer. Women residing in low-resource settings such as those residing in slums have a higher risk of cervical cancer, and lower uptake of cervical cancer screening. Diverse barriers influence the uptake of cervical cancer screening among women in low-resource settings. Objectives: This qualitative study was carried out prior to a cervical cancer screening program and explored women’s knowledge about cervical cancer, and their perceived barriers and recommendations for the program.Method: Four focus group discussions (FGD) were conducted among 35 women between the ages of 21 – 65 years residing in two urban slums in Lagos, Nigeria. Each FGD was limited to 8-10 participants of women of similar ages. Voice recordings were transcribed verbatim and analysis was done using thematic analysis.Results: Most of the women were not aware of cervical cancer and none knew the symptoms or risk factors of cervical cancer. The participants felt that the cervical cancer screening program would be well accepted in the community, however, they expressed concerns about the cost of the screening test and the sex of the person performing the test. The recommendations proffered for a successful cervical cancer screening program include; reducing the cost of the test or providing the test free of charge, having people that speak the local language as part of the team, using female health care providers, using a private location within the community or nearby PHC, and publicizing the program with the use of SMS, phone calls, town crier, and health talks. It was recommended that organizing health education sessions would help improve women’s poorly perceived susceptibility to cervical cancer.Conclusion: Interventions to increase uptake of cervical cancer screening among women in low resource settings need to improve knowledge and understanding of cervical cancer and address the barriers to cervical cancer screening such as cost, distance, and as much as possible, sex of the healthcare provider should be considered.

We present improvements in n-best rescoring of code-switched speech achieved by n-gram augmentation as well as optimised pretraining of long short-term memory (LSTM) language models with larger corpora of out-of-domain monolingual text. In addition, we consider the application of large pretrained transformer-based architectures. Our experimental evaluation is performed on an under-resourced corpus of code-switched speech comprising four bilingual code-switched sub-corpora, each containing a Bantu language (isiZulu, isiXhosa, Sesotho, or Setswana) and English. We find in our experiments that, by combining n-gram augmentation with the optimised pretraining strategy, speech recognition errors are reduced for each individual bilingual pair by 3.51% absolute on average over the four corpora. Importantly, we find that even speech recognition at language boundaries improves by 1.14% even though the additional data is monolingual. Utilising the augmented n-grams for lattice generation, we then contrast these improvements with those achieved after fine-tuning pretrained transformer-based models such as distilled GPT-2 and M-BERT. We find that, even though these language models have not been trained on any of our target languages, they can improve speech recognition performance even in zero-shot settings. After fine-tuning on in-domain data, these large architectures offer further improvements, achieving a 4.45% absolute decrease in overall speech recognition errors and a 3.52% improvement over language boundaries. Finally, a combination of the optimised LSTM and fine-tuned BERT models achieves a further gain of 0.47% absolute on average for three of the four language pairs compared to M-BERT. We conclude that the careful optimisation of the pretraining strategy used for neural network language models can offer worthwhile improvements in speech recognition accuracy even at language switches, and that much larger state-of-the-art architectures such as GPT-2 and M-BERT promise even further gains.