Reliable Provision Research Articles

Omani citizens relied more on international sources of information to inform themselves during the COVID‐19 pandemic

AbstractBackgroundThe COVID‐19 demanded efficient and effective supply of information to the public to help reduce the rate of transmission.ObjectivesThis study aims to analyse Omanis' information behaviour during the COVID‐19 pandemic, to help national authorities to prepare for future health crises or pandemics.MethodsA self‐administered online survey involving a structured open‐ended questionnaire was conducted via the SurveyMonkey software. Snowball and convenience sampling methods were used to recruit potential participants from social media sites like Instagram, Twitter and Facebook. Non‐parametric testing (Mann–Whitney U and Kruskal–Wallis H tests) assisted in analysis of demographic factors. Descriptive statistical analysis identified trends in information needs and seeking behaviour.ResultsOver 6000 responses were obtained. The results revealed that Oman nationals were seeking information on symptoms of COVID‐19, global and national infection rates, preventive measures, treatment and vaccines. Primary sources of information were radio news, Oman TV, international TV, print media, healthcare professionals, international agencies and online news websites.DiscussionThere was little trust of local information sources with many Omanis relying on international information sources such as the WHO and international TV networks.ConclusionPublic health agencies need to prepare for timely, and reliable information provision during health crises.

Reliable and cost-efficient session provisioning in CRNs using spectrum sensing as a service

With the advancement of wireless communication technologies, and the growing number of wireless and IoT applications that demand various types and volumes of data, Sensing as a Service (SaaS) has emerged as a necessary enabling business model for many of those applications. Spectrum Sensing as a Service (SSaaS) has also emerged as a form of SaaS that is concerned with the monitoring of wireless spectrum to facilitate its safe reuse by cognitive radio-enabled wireless users. SSaaS was primarily motivated by the need for a low-cost, accurate, and reliable spectrum sensing service to support a plethora of heterogeneous wireless devices and applications. Under the SSaaS model, clients need to pay the service provider for the sensing service they receive. In this paper, we address the problem of allocating spectrum channels to links of a given communication session in a cognitive radio network (CRN) that utilizes SSaaS. The objective is to allocate channels such that the worst link availability among the session is maximized and the spectrum access cost is minimized. A number of multi-objective evolutionary optimization algorithms (MOEAs) were used to solve this multi-objective optimization problem. Extensive experimentation was conducted to compare between these algorithms and identify the best ones to use. We also propose a post-processing greedy algorithm to further enhance the solution obtained by a MOEA algorithm. Results show that an improvement of up to 20% can be achieved using the proposed greedy algorithm under some network settings.

Hybrid compressed air energy storage system and control strategy for a partially floating photovoltaic plant

For more efficient, reliable, and stable energy provision, energy storage plays a key role in the transition towards renewable energy sources. Compressed air energy storage (CAES) has been recognized as one of the most promising technology due to its high energy capacity, flexibility, scalability, long lifespan, maintainability, economical, and environmental viability. These potentials can be further improved by hybridizing CAES systems with thermal energy storage system. However, to realize the potentials of hybrid CAES systems, a control strategy is essential to manage the energy flow between the system components. Therefore, in this work, a novel energy management strategy is proposed to control a hybrid CAES system for a prototype of a partially floating photovoltaic plant (PFPV). The proposed control strategy is based on the rule-based approach and a mathematical model is presented to evaluate the system performance. The results indicate that, for an average hourly profile of the 5 kW PFPV platform through the year, a system round-trip efficiency of 34.1% can be obtained while the cycle and exergy efficiencies are 37.7% and 41%, respectively. Higher efficiency can be obtained by controlling the compressors operational range and rated power. Therefore, future work includes experimental work for results validation and optimization.

RSMS: Towards Reliable and Secure Metaverse Service Provision

RSMS: Towards Reliable and Secure Metaverse Service Provision

Towards a sustained and fit-for-purpose European ocean observing and forecasting system

The EU funded project EuroSea brought together key actors of the European ocean observing and forecasting communities with key users of the ocean observing products and services in order to better integrate existing ocean observation systems and tools, and to improve the delivery of ocean information to users. EuroSea was constructed around the ocean observing value chain that connects observations to users of ocean information, and, just as intended, the value chain concept was a useful prism to improve the system. In this article, we summarize some of the main take-home messages from EuroSea on the needs for developing the European Ocean Observing System and its links with modeling and forecasting systems. During the project, the challenges and gaps in the design and coordination of the European ocean observing and forecasting system were identified and mapped. Many gaps and challenges related to the observations of physical, chemical and biological Essential Ocean Variables were identified. Some of these gaps are related to technological developments, while others are caused by insufficient and short-term funding leading to a not sustainable system, management, and cooperation between different entities, as well as limitations in foresight activities, policies and decisions. This article represents a compilation of the broader needs for advancing the observing and forecasting system, and is meant as a guide for the community, and to funders and investors to advance ocean observing and the delivery of ocean information in Europe. To enhance the sustainability of ocean observations, which is paramount for a reliable provision of quality oceanographic data and services, several recommendations were compiled for ocean observing networks, frameworks, initiatives, as well as the ocean observing funders within the European nations, and the European Commission.

DPTracer: Integrating Log-Driven Accountability into Data Provision Networks

Emerging applications such as blockchain, autonomous vehicles, healthcare, federated learning, self-consistent large language models (LLMs), and multi-agent LLMs increasingly rely on the reliable acquisition and provision of data from external sources. Multi-component networks, which supply data to the applications, are defined as data provision networks (DPNs) and prioritize accuracy and reliability over delivery efficiency. However, the effectiveness of the security mechanisms of DPNs, such as self-correction, is limited without a fine-grained log of node activities. This paper presents DPTracer: a novel logging system designed for DPNs that uses tamper-evident logging to address the challenges of maintaining a reliable log in an untrusted environment of DPNs. By integrating logging and validation into the data provisioning process, DPTracer ensures comprehensive logs and continuous auditing. Our system uses Process Tree as a data structure to store log records and generate proofs. This structure permits validating node activities and reconstructing historical data provision processes, which are crucial for self-correction and verifying data sufficiency before results are finalized. We evaluate the overheads introduced by DPTracer regarding computation, memory, storage, and communication. The results demonstrate that DPTracer incurs reasonable overheads, making it practical for real-world applications. Despite these overheads, DPTracer enhances security by protecting DPNs from post-process and in-process tampering.

Towards Urban Accessibility: Modeling Trip Distribution to Assess the Provision of Social Facilities

Assessing the accessibility and provision of social facilities in urban areas presents a significant challenge, particularly when direct data on facility utilization are unavailable or incomplete. To address this challenge, our study investigates the potential of trip distribution models in estimating facility utilization based on the spatial distributions of population demand and facilities’ capacities within a city. We first examine the extent to which traditional gravity-based and optimization-focused models can capture population–facilities interactions and provide a reasonable perspective on facility accessibility and provision. We then explore whether advanced deep learning techniques can produce more robust estimates of facility utilization when data are partially observed (e.g., when some of the district administrations collect and share these data). Our findings suggest that, while traditional models offer valuable insights into facility utilization, especially in the absence of direct data, their effectiveness depends on accurate assumptions about distance-related commute patterns. This limitation is addressed by our proposed novel deep learning model, incorporating supply–demand constraints, which demonstrates the ability to uncover hidden interaction patterns from partly observed data, resulting in accurate estimates of facility utilization and, thereby, more reliable provision assessments. We illustrate these findings through a case study on kindergarten accessibility in Saint Petersburg, Russia, offering urban planners a strategic toolkit for evaluating facility provision in data-limited contexts.

Connectivity and climate influence diversity–stability relationships across spatial scales in European butterfly metacommunities

AbstractAimAnthropogenic‐driven biodiversity loss can impact ecosystem stability. However, most studies have only evaluated the diversity–stability relationship at the local scale and we do not fully understand which factors stabilize animal populations and communities across scales. Here, we investigate the role of species dispersal ability, climate, spatial distance and different facets of biodiversity on the stability of butterfly populations and communities across multiple spatial scales.LocationPrimarily Western Europe.Time Period2005–2016.Major Taxa StudiedButterflies (Rhopalocera) of Europe.MethodsWe assembled a continent‐wide database of European butterflies' abundance and used Structural Equation Modelling to evaluate the direct and indirect effects of multiple stabilizing mechanisms. In parallel, we tested the effect of dispersal ability on the stability at multiple spatial scales, using a butterfly mobility index as an indicator of dispersal capacity.ResultsRegional stability strongly reflected local stability, which in turn was driven by both taxonomic and functional α‐diversity. Spatial asynchrony was also important for regional stability and it was driven by both functional β‐diversity and metapopulation asynchrony, which in turn increased with spatial distance among communities. We observed a positive effect of temperature on functional α‐diversity and on local stability, whereas precipitation negatively influenced local diversity. Finally, spatial asynchrony contributed more to the regional stability of less mobile species compared to highly mobile ones, indicating that both extrinsic and intrinsic determinants of connectivity impact regional stability indirectly.Main ConclusionsOur results demonstrate the importance of local and regional processes for regional stability. However, the relative contribution of spatial asynchrony and metapopulation asynchrony increases with connectivity loss, especially for less mobile species, indicating that landscape management should be tailored depending on the dispersal capacity of organisms. Both local biodiversity loss and regional biotic homogenization destabilize metacommunities, with potential implications for the reliable provision of ecosystem functions.

The Dynamic Change in the Reliability Function Level in a Selected Fire Alarm System during a Fire.

This article discusses fundamental issues associated with the functional reliability of selected fire alarm systems (FASs) in operation during building fires. FASs operate under diverse external or internal natural environmental conditions, and the operational process of FAS should take into account the impacts of physical phenomena that occur during fires. Their operation is associated with the constant provision of reliability. FAS designers should also consider the system's reliability when developing fire control matrices, tables, algorithms, or scenarios. All functions arising from an FAS control matrix should be implemented with a permissible reliability level, RDPN(t), prior to, as well as during, a fire. This should be assigned to the controls saved in the fire alarm control unit (FCP). This article presents the process by which high temperatures generated during a fire impact the reliability of FAS functioning. It was developed considering selected critical paths for a specific scenario and the control matrix for an FAS. Such assumptions make it possible to determine the impact of various temperatures generated during a fire on the reliability of an FAS. To this end, the authors reviewed that the waveform of the R(t) function changes for a given FAS over time, Δt, and then determined the fitness paths. The critical paths are located within the fire detection and suppression activation process, using FAS or fixed extinguishing devices (FEDs), and the paths were modeled with acceptable and unacceptable technical states. The last section of this article defines a model and graph for the operational process of a selected FAS, the analysis of which enables conclusions to be drawn that can be employed in the design and implementation stages.

The Latency Performance Analysis and Effective Relay Selection for Visible Light Networks.

In visible light communication (VLC), the precise latency evaluation of wireless access networks and the efficient forwarding strategy of core networks are the crux for end-to-end reliability provisioning. Leveraging martingale theory, an elegant latency-bounded reliability analysis framework is studied for the VLC network. Considering the characteristic that VLC links are easy to block, the martingale of the service process is constructed. Based on the time shift features, the martingale process related to latency is proposed for the VLC system, which models the influence of entanglement between aggregate arrivals and random service on latency. A stopping time event about latency is defined. Renting the stopping time theory, a tight upper bound of the unreliability with regard to latency is derived. In the core network, a dynamic forward backhaul framework is proposed, which uses the relay selection algorithm based on back-pressure theory to improve data transmission quality. The theoretical latency-bounded reliability matches the simulation results well, which verifies the effectiveness of the proposed analysis framework, and the proposed relay selection algorithm can also improve network performance under data-intensive transmission.

Citizen monitoring in environmental disclosure: An economics perspective

Criticism is mounting that market-led and state-led initiatives for environmental impact disclosure are too limited in scope and that they rest on too strong assumptions about the quality and impartiality of monitoring and enforcement, with resulting insufficient effect on environmental sustainability. It has been proposed that citizen monitoring may contribute to counteract this void. However, to our knowledge, policy analysis in general and economics in particular has not paid much attention to this role of citizen monitoring. This paper aims to bridge that gap from an economics lens, by exploring the dynamics of disclosing local environmental impact and the potential role of citizen monitoring in environmental policy. To this end, the paper addresses monopolistic versus pluralistic environmental disclosure, letting citizen monitoring represent the latter. The study uses the mining industry as an illustrative case, because of that sector's particular transparency challenges in international value chains, typically with strong negative local environmental impact. It is shown how pluralistic information provision such as citizen monitoring can contribute to incentivizing more reliable information provision, especially in countries with weak state institutions, which is particularly important in the case of high-risk environmental impact. The findings should be of use for shaping environmental policy, providing valuable insights for both policymakers and scholars.

Research on a Deep Ensemble Learning Model for the Ultra-Short-Term Probabilistic Prediction of Wind Power

An accurate method for predicting wind power is crucial in effectively mitigating wind energy fluctuations and ensuring a stable power supply. Nevertheless, the inadequacy of the stability of wind energy severely hampers the consistent functioning of the power grid and the reliable provision of electricity. To enhance the accuracy of wind power forecasting, this paper proposes an ensemble model named the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and convolutional bidirectional long short-term memory (CNN-BiLSTM), which incorporates a data preprocessing technique, feature selection method, deep ensemble model, and adaptive control. Initially, CEEMDAN is utilized to decompose wind speed and power sequences and hence obtain decomposed subsequences for further analysis. Subsequently, the CNN is used to extract features from each subsequence, whereas each subsequence is processed by BiLSTM to obtain an ultra-short-term deterministic prediction model. Additionally, the adaptive kernel density estimation (AKDE) method is employed to estimate the probabilistic distribution of prediction error, enabling ultra-short-term probabilistic wind power prediction. Finally, based on real datasets, the reliability of the model in probabilistic prediction is verified through the evaluation metrics of multi-step prediction intervals (PIs).

Zero discharge: An optimized strategy to improve the hydric deficit in the Mediterranean area by pumped hydro storage. Case study of Alicante, Spain

Alterations in annual temperature and precipitation patterns are imposing heightened strain on both urban and agricultural systems. The insufficiency of available water resources to satisfy the escalating demand necessitates a concerted effort to optimize alternative sources. In the context of agriculture, the utilization of treated water is currently executed on a sporadic basis. The elevated energy expenditures and the absence of regulatory frameworks in these systems contribute to a substantial portion of these resources being discharged into natural water bodies, or in the case of coastal municipalities, near the sea. This research endeavors to proffer a comprehensive strategy aimed at eliminating the discharge of treated water into the sea. It proposes the annual allocation of 16.7 hm3 of treated water to irrigation communities grappling with acute water deficits. The strategy, when applied to a tangible case study, ensures the reliable provision and distribution of the entire designated volume. This approach takes into account the fluctuating agricultural demands through the implementation of hybrid systems boasting an installed capacity of 15 MW. Additionally, it establishes the deployment of pumped hydro-storage reservoirs to regulate and secure the requisite energy for distribution and the osmosis process involved in treating a portion of the reclaimed water, thereby presenting a high-quality solution amenable to agricultural consumption.

Improving estimation performance of compressive sensing‐based multiple‐input multiple‐output radar using electronic beamsteering

AbstractTwo‐dimensional direction‐of‐arrival (DoA) estimation in azimuth and elevation via radar systems equipped with uniform rectangular arrays (URAs) will play an important role in various application areas—most distinctively in future urban air mobility settings with unmanned aerial vehicles. A key factor is the fast and reliable provision of target detections in terms of range and DoA for safe autonomous operation of the vehicle using on‐board antenna arrays with compact installation size. The authors present a technique for improving the performance of DoA estimation using compressive sensing in conjunction with multiple‐input multiple‐output arrays with electronically steered beams in the transmit direction. The simulation study investigates the impact of different design considerations on radar signal processing performance. An optimisation of a radar system using electronic beamsteering in the transmit domain is presented numerically. Based on the architecture of the URAs used, performance and detection accuracy can be improved.

Digital Twin-Assisted, SFC-Enabled Service Provisioning in Mobile Edge Computing

Mobile Edge Computing (MEC) has been identified as a desirable computing paradigm that provides efficient and effective services for various applications, while meeting stringent service delay requirements. Orthogonal to the MEC computing paradigm, Network Function Virtualization (NFV) technology is another enabling technology that provides the network resource management with great flexibility and scalability, where the instances of Virtual Network Functions (VNFs) are deployed in edge servers as Service Function Chains (SFCs) for SFC-enabled services. Although reliable service provisioning in MEC environments is fundamentally important, the deployed VNF instances usually are not reliable, which can be affected by their software implementation, their execution duration, the workload among edge servers, and so on. Empowered by digital twin techniques, the states of VNF instances can be maintained by their digital twins in a real-time manner and their reliability can be accurately predicted through their digital twins. In this paper, we study digital twin-assisted, SFC-enabled reliable service provisioning in MEC networks by exploiting the dynamics of VNF instance reliability. We concentrate on two novel optimization problems of reliable service provisioning: the service cost minimization problem, and the dynamic service admission maximization problem. We first show their NP-hardness. We then formulate an Integer Linear Program (ILP) solution, and devise an approximation algorithm with a constant approximation ratio for the service cost minimization problem. We thirdly provide an ILP solution to the offline version of the dynamic service admission maximization problem. Built upon this offline ILP solution, we also develop an online algorithm with a provable competitive ratio for the problem, by adopting the primal-dual dynamic updating technique. We finally evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms outperform their comparison benchmarks, and improve the performance of their comparison counterparts by no less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10.2 \%$</tex-math></inline-formula> .

Effectiveness of Clustering Water Utilities on Water Service Delivery Case Study in Iringa Water Supply and Sanitation Authority (Iruwasa)

Reliable and high-quality water service provision is essential for public health, economic growth, and environmental sustainability. However, urban and peri-urban water utilities face significant challenges, including inefficiencies, fragmented service delivery, and limited capacity. This study explores the effectiveness of clustering water utilities in enhancing service delivery, focusing on the Iringa Water Supply and Sanitation Authority (IRUWASA) in Tanzania. Although clustering has been proposed as a solution, empirical evidence on its impact, particularly on institutional capacity, water quality, and customer satisfaction, remains limited. The objective of this study is to evaluate how clustering can improve water service delivery by analyzing operational data, governance structures, and customer feedback within IRUWASA’s expanded service areas. Using a mixed-methods approach, data were collected from IRUWASA staff, community leaders, and service users to measure performance metrics such as operational efficiency, cost recovery, and customer satisfaction. Findings indicate that clustering has contributed to operational improvements, expanded service coverage, and better water quality. However, financial sustainability remains a concern, as revenues from the newly clustered zones do not fully meet the operation and maintenance costs. The study recommends additional strategies, including targeted subsidies and capacity-building initiatives, to support the long-term success of clustering. The research provides practical insights for policymakers and water utility managers, suggesting that while clustering can enhance service delivery, complementary measures are necessary to sustain its benefits. These findings contribute to the broader field of urban water management and provide a basis for further exploration of clustering as a model for water utility reform.

Emulating Empires: Caribbean Free Ports, Economic Dualism, and European Imperial Rivalry, c. 1670s–1760s

ABSTRACTWhen the Dutch West Indies Company (WIC) established the free port of Willemstad, Curaçao after 1674, they launched a model for economic competition that other European empires would emulate. Within a century, the Danish, Spanish, British, and French all established their own Caribbean free ports. Emulation, however, was not replication. This article argues that while the expansion of free ports into different Caribbean empires generated similar forms of economic dualism (prohibitive commercial systems in certain locales with more-open ones in others), they all served varying purposes. The Dutch and Danish maintained or expanded their free-port systems and utilised strategic neutrality to remain active players within an increasingly competitive economic arena. Britain wanted to extend its commercial empire by making foreign realms dependent on its trade. France sought to pacify recalcitrant planters desperate for reliable provisioning. Finally, Spain enacted free ports in Santo Domingo to supply and encourage settlement in a region threatened by French encroachments. Thus, although these empires all established free ports in strategic locales and jealously emulated each other, they did so with unique political-economic aims in mind. Meanwhile, merchants, enslaved Africans, sailors, settlers, and European intellectuals employed Caribbean free ports to accomplish their own ends as well.

Engaging Czech-Slovak Roma women in the co-creation of an antenatal care information resource

Abstract Background Czech-Slovak Roma women in the UK suffer barriers to antenatal care such as stigma contributing to reduced engagement with health services. There is a scarcity of research and intervention studies with this population. This study aims to co-create an antenatal care community information resource with a group of these women through participatory research. Women's stories of their pregnancy journeys would be able to provide an understanding of their maternity needs, to inform design considerations for a prototype of an antenatal care information resource. Objectives 1. To understand Roma women's needs in pregnancy, childbirth, and maternity services through storytelling 2. To share with women publicly available antenatal information resources such as pregnancy apps 3. To co-create a low-resolution paper prototype of an antenatal care information resource that meets their needs Research activities 1. Six two-hour workshops with 6-8 women supported by a peer researcher and an interpreter. 2. Two workshops focused on the co-creation of an antenatal information resource. 3. Design-based participatory methods and thematic analysis, culminating in a community event and webinar for public and professional stakeholders. Findings The women's pregnancy journeys covered several topics such as unplanned pregnancies, nausea and vomiting, pregnancy loss and birth trauma. Through all these, the main message they wished to convey was the need for women in the community to overcome the stigma of seeking help for their mental health. Our information resource developed using a co-creative participatory approach, signposts Roma women to reliable information and healthcare provision potentially reducing poor maternal and perinatal health outcomes. This study could be replicated in other marginalized newly arrived groups of migrant women to facilitate timely uptake of antenatal and mental health support services. Key messages • Czech-Slovak Roma women face stigma in seeking help for their mental health during pregnancy. • Roma women can be supported to overcome stigma through design-based participatory research.

A Performance and Reliability-Guaranteed Predictive Approach to Service Migration Path Selection in Mobile Computing

Mobile Edge Computing (MEC) is a forward-looking technology that provides services through resources to meet the needs of cloud-edge Internet of Things (IoT) devices. It provides computing and storage data facilities for IoT users and further renders services through resources in vicinity to fulfill the needs from IoT devices at the cloud edge. However, a major difficulty in guaranteeing reliable resource provisioning is mobility, which brings in chances of service migrations among difference distributed edge nodes and thus causes potential risks of service failures or disruptions. Existing solutions in this direction can be ineffective since they tend to consider that stability of inter-edge-node data transmission to be irrelevant to user mobility and are thus in lack of a comprehensive model for estimating effectiveness of migration paths selected. In this paper, instead, we consider that the effectiveness of migrations paths to be selected are highly dependent on user mobility as well as inter-edge-node stability propose a novel predictive and mobile track-aware approach to fault-tolerant service migration path selection in MEC (PTSM). It is capable of exploiting uses trajectories for accurate predictions of future tracks and selecting target servers as well as migration paths with guaranteed migration reliability and performance in terms of multiple metrics. We demonstrate with extensive simulations and numerical results that our proposed method outperforms its peers in terms of migration reliability and performance.

Ultra reliability and massive connectivity provision in integrated internet of military things (IoMT) based on tactical datalink

One of the major challenges arising in internet of military things (IoMT) is accommodating massive connectivity while providing guaranteed quality of service (QoS) in terms of ultra-high reliability. In this regard, this paper presents a class of code-domain nonorthogonal multiple accesses (NOMAs) for uplink ultra reliable networking of massive IoMT based on tactical datalink such as Link-16 and joint tactical information distribution system (JTIDS). In the considered scenario, a satellite equipped with Nr antennas servers K devices including vehicles, drones, ships, sensors, handset radios, etc. Nonorthogonal coded modulation, a special form of multiple input multiple output (MIMO)-NOMA is proposed. The discussion starts with evaluating the output signal to interference-plus-noise (SINR) of receiver filter, leading to the unveiling of a closed-form expression for overloading systems as the number of users is significantly larger than the number of devices admitted such that massive connectivity is rendered. The expression allows for the development of simple yet successful interference suppression based on power allocation and phase shaping techniques that maximizes the sum rate since it is equivalent to fixed-point programming as can be proved. The proposed design is exemplified by nonlinear modulation schemes such as minimum shift keying (MSK) and Gaussian MSK (GMSK), two pivotal modulation formats in IoMT standards such as Link-16 and JITDS. Numerical results show that near capacity performance is offered. Fortunately, the performance is obtained using simple forward error corrections (FECs) of higher coding rate than existing schemes do, while the transmit power is reduced by 6 dB. The proposed design finds wide applications not only in IoMT but also in deep space communications, where ultra reliability and massive connectivity is a keen concern.