Search Results (17)

This paper addresses human error in network orchestration systems and the high cost and resource requirements of integrating artificial intelligence (AI) for network orchestration. It proposes a framework for implementing an AI decision-maker and automation. Data are fed into the AI decision-maker to trigger designated automation robots’ tasks or notify IT specialists to gradually implement automated robots, ensuring efficient resource use, reducing costs, and enhancing productivity. We evaluated the proposed method in a simulation with genuinely uncertain outcomes, across 20 independent runs: the AI decision-maker reached 78.3% accuracy against an estimated 79.1% achievable ceiling, and the proposed framework reduced operational cost by 61.4 ± 0.7% relative to fully manual operation—the best of six operating policies in the training environment—while an explicit sensitivity guard, rather than the learned model, accounts for the absence of security incidents; under distribution shift, the framework retains 43.2 ± 0.8% savings, second only to a hand-tuned rule-based router that requires environment-specific threshold calibration. However, the proposed method requires an IT specialist to implement it properly, and the AI model’s accuracy depends on the amount of input data. In the end, we recommend that future work conduct a study focused on AI decision-makers, test the proposed method on real-world companies, and implement AI decision-makers across various departments to cover a broader range of the company’s systems. Full article

The deployment of object detection models on resource-constrained edge devices remains a substantial challenge, primarily because conventional static networks expend the same worst-case computational cost on every input, regardless of intrinsic difficulty. This paper proposes an input-adaptive dynamic neural network architecture for object detection in embedded environments. The present study investigates two orthogonal axes of input-adaptive inference for embedded object detection: The system demonstrates depth adaptivity through the implementation of Early Exit, and width adaptivity via group-wise Adaptive Routing. The proposed framework is constructed on a frozen Ultralytics YOLO26s backbone and incorporates two YOLO-style early-exit heads positioned at approximately 33% and 66% of the backbone depth. Furthermore, the framework incorporates two Straight-Through Gumbel-Softmax routers, which are appended after Layers 4 and 8 with group-wise hard gating. Both axes additionally accept an explicit external control signal that allows the host system to override the input-conditional policy at inference time. The dual-mode design facilitates the functionality of the trained checkpoint as either an input-adaptive policy, in which the depth and width are determined per sample from the input distribution, or an externally controlled policy. The experimental findings demonstrate two strongly asymmetric input-adaptive policies on a frozen YOLO26s backbone. The early-exit profile reduces the compute per sample from 12.739 to 10.532 GFLOPs—a 17.32% reduction according to our in-house Conv2d/Linear MAC-based GFLOPs estimator—while preserving baseline accuracy (mAP50 = 0.1545 vs. baseline = 0.1528; ΔmAP50 = +0.0017, within evaluation noise; mAP50–95 = −0.0033). Evaluating the router-only profile in the same validator pipeline with a sparsity penalty of γ = 0.05 results in a 12.3% decrease in logical GFLOPs (from 12.739 to 11.172), while maintaining an accuracy level that is at or above the PEFT baseline (mAP50 = 0.2324 and mAP50–95 = 0.1040). In our small-domain PEFT setup, training the dynamic-policy modules yields per-checkpoint mAP shifts in this magnitude. Therefore, we interpret the width-axis accuracy result as preservation of the baseline rather than an improvement. Our contribution on the width axis is reducing computing power while maintaining baseline accuracy. Importantly, the router profile’s logical GFLOP savings are not currently reflected in wall-clock latency under our dense-kernel PyTorch implementation. Achieving practical speedup requires sparse-kernel deployment, such as structured-sparse kernels, TensorRT, TVM, or Triton paths. We will address this in future deployment-level work. Our results indicate that the depth axis can yield genuine end-to-end speedup today, while the width axis offers deployment-pending compute reduction. Full article

The rapid growth of video content development requires more effective compression solutions than traditional ones. Although neural video compression has demonstrated impressive advances, the current methods are having a hard time with how to effectively model long-range temporal dependencies and react to different content properties. We introduce STAC (Spatio-Temporal Adaptive Context), a transformer-based neural video compression scheme that does not have these limitations, and makes three original contributions. First, the Adaptive Context Selector (ACS) is the dynamic evaluation and selection of the most informative reference frames, based on learned relevance scoring, in place of the traditional use of predetermined adjacent frame sets. Second, Enhanced Sliding Window Attention (ESWA) is an effective computational model of spatio-temporal correlations by the integration of learnable local bias and temporal gating information into a computationally adjustable attention model. Third, a dual-path entropy model is an adaptively learned fusion gate that combines channel-wise autoregressive prediction with spatio-temporal prediction to produce better probability estimations for entropy coding. Trained on the Vimeo-90k dataset using a four-phase curriculum with the Adam optimiser over approximately 2.2 M total steps. We tested STAC using six benchmark videos, such as UVG, MCL-JCV, and HEVC Class B, C, D and E videos, at varying test settings. The experimental findings prove that STAC, on average, saves a BD-rate of 32.20% in the YUV colourspace with an intra-period of −1. The consistent improvement across both PSNR and MS-SSIM metrics confirms that STAC’s coding gains arise from genuinely improved probability modelling, rather than metric-specific optimisation. Evaluations were performed on six standard benchmarks (UVG, MCL-JCV, and HEVC Classes B, C, D, and E) under 24 experimental configurations (six datasets × 2, and colourspaces × 2 intra-period settings), with all methods tested under identical conditions using the same sequences, frames (96 per sequence), and VTM-17.0 anchor codec. STAC achieves 32.20% average BD-rate savings over VTM under YUV IP = −1, outperforming the prior state-of-the-art DCMVC by 2.70 percentage points. Under IP = 32, STAC achieves −27.01%, with only 5.19 pp degradation versus 6.42 pp for DCMVC. The results generalise to the RGB colourspace (−31.23%) and scale from 240p (−35.19%) to 4K (−36.35%). Full article

The Expectation-Maximisation (EM) algorithm is widely used for maximum likelihood estimation in incomplete data problems such as mixture modelling, but it often converges slowly, particularly when mixture components overlap substantially. This study presents a comprehensive empirical evaluation of simple EM acceleration schemes for Gaussian mixture models, comparing linear (STEM), quadratic (SQUAREM), and greedy (line search, golden section) methods across 240 simulated mixture configurations spanning three dimensionalities, four component counts, five overlap levels, and four sample sizes. A key contribution is the first systematic comparison of the three acceleration parameter estimates (${\alpha }_1$, ${\alpha }_2$, ${\alpha }_3$) in the mixture modelling context: we show that only ${\alpha }_3$, which is derived as the geometric mean estimate of ${\alpha }_1$ and ${\alpha }_2$, provides genuine acceleration, while ${\alpha }_1$ and ${\alpha }_2$ consistently increase iteration counts by 50–110% relative to ${\alpha }_3$, effectively acting as deceleration. With ${\alpha }_3$, SQUAREM reduces iterations by up to 48% with negligible computational overhead, while greedy methods achieve similar iteration reductions but at 50–110% greater wall-clock time due to repeated log-likelihood evaluations. Crucially, acceleration does not degrade parameter estimation quality under any tested combination of initialisation, overlap, dimensionality, or number of components. We further examine the interaction between acceleration and initialisation, finding that k-means benefits most from acceleration (up to 50% time savings), while the REBMIX (Rough-Enhanced-Bayes MIXture estimation) algorithm benefits least as it already starts near the optimum. Among REBMIX configurations, histogram preprocessing with the outliers mode traversing strategy offers the best trade-off between quality and computational cost. The findings are validated on a real-world Backblaze hard drive failure dataset, confirming the practical utility of EM acceleration. All methods are implemented in the free and open-source R package rebmix, accompanied by full source code. Full article

This article examines the territorial production and resource structures in the territory of Asian Russia, which include natural, economic, and social elements. The objective of this study is to assess, typologize, and zone the territorial production–resource structures. The methodological framework of this study is based on calculating an integral development indicator for the regions of Asian Russia, represented by an Adjusted Net Savings index. This index reflects regional wealth according to the World Bank’s expanded concept of national wealth, which includes three types of capital: “reproducible”, “natural”, and “human”. The study also involves a larger typology of the regions, classifying them based on the predominance of these capital forms through the Analytic Hierarchy Process and the triangular illustration method. Regional clustering within the study area was also conducted through the k-means method using Statistica 12 software. Accordingly, adjusted net savings and sectoral contributions to gross regional product were calculated for the regions in this area. A relationship between regional specialization and the level of genuine savings was identified, forming the basis for the grouping of regions. A total of three groups were distinguished: regions with negative, moderate, and the highest levels of genuine savings within the macroregion. A larger typology using dominant, predominant, and complex functions was also developed. Using data on natural, physical, and human capital in Asian Russia, its regions were clustered, resulting in the identification of five clusters. The territory was then zoned based on the clustering results. A total of eight zones were identified, reflecting the size and balance of the three types of capital (human, physical capital, and productive natural), with regions grouped according to their cluster membership and territorial proximity. The findings can help identify pathways for more sustainable development of this territory amidst evolving natural, economic, social, and geopolitical conditions. Full article

Economic theory on sustainable development suggests that resource-rich countries should reinvest the rents from natural resource extraction in other forms of capital to ensure that future consumption level of the economy can be greater than or at least equal to the level of their current consumption. Several seminal papers in the early 2000s indicated that the correlation between genuine savings and future consumption was weaker than theory predicted, at least when genuine savings were measured using the World Bank estimates. This paper revisits the issue and replicates two of these earlier studies to see whether the correlation has become stronger over time, on the back of policy changes in resource-rich countries and of revisions to the World Bank estimates. The results indicate that the correlation between genuine savings and future consumption growth may be stronger for poorer countries than for richer, and for sub-Saharan Africa, the theoretical predictions appear to hold. Full article

Microalgae, renowned for their high photosynthetic efficiency and minimal competition with land-based crops, hold great promise in the biofixation of CO₂ from waste sources, making them valuable for diverse applications, including biofuels, food production, and biomaterials. An innovative technology, the integrated carbonate-based carbon capture and algae biofixation system is emerging as an alternative to traditional carbon capture and sequestration (CCS) methods. This closed-loop system utilizes bicarbonates as inorganic carbon sources, which can directly enter microalgae photosynthesis, subsequently regenerating carbonates for another cycle of carbon capture. This system offers significant advantages, including cost savings in carbon supply, simplified photobioreactor development, and reduced labor and energy requirements. Nevertheless, further research is essential to evaluate the suitability of various microorganisms and search for optimal growth conditions. In this study, we assessed the performance of two strains of Spirulina within the integrated system. Employing a Design of Experiments approach, we simultaneously varied temperature, bicarbonate concentration, and light irradiation while operating within a lab-scale photobioreactor. We achieved remarkable results, with a biomass productivity of 875 mg/L·d and an impressive CO₂ utilization efficiency of 58%. These findings indicate a genuine opportunity for further exploration and scaling of this approach in industrial settings. Full article

Over the last few years, exoskeletons have been demonstrated to be useful tools for supporting the execution of neuromotor rehabilitation sessions. However, they are still not very present in hospitals. Therapists tend to be wary of this type of technology, thus reducing its acceptability and, therefore, its everyday use in clinical practice. The work presented in this paper investigates a novel point of view that is different from that of patients, which is normally what is considered for similar analyses. Through the realization of a technology acceptance model, we investigate the factors that influence the acceptability level of exoskeletons for rehabilitation of the upper limbs from therapists’ perspectives. We analyzed the data collected from a pool of 55 physiotherapists and physiatrists through the distribution of a questionnaire. Pearson’s correlation and multiple linear regression were used for the analysis. The relations between the variables of interest were also investigated depending on participants’ age and experience with technology. The model built from these data demonstrated that the perceived usefulness of a robotic system, in terms of time and effort savings, was the first factor influencing therapists’ willingness to use it. Physiotherapists’ perception of the importance of interacting with an exoskeleton when carrying out an enhanced therapy session increased if survey participants already had experience with this type of rehabilitation technology, while their distrust and the consideration of others’ opinions decreased. The conclusions drawn from our analyses show that we need to invest in making this technology better known to the public—in terms of education and training—if we aim to make exoskeletons genuinely accepted and usable by therapists. In addition, integrating exoskeletons with multi-sensor feedback systems would help provide comprehensive information about the patients’ condition and progress. This can help overcome the gap that a robot creates between a therapist and the patient’s human body, reducing the fear that specialists have of this technology, and this can demonstrate exoskeletons’ utility, thus increasing their perceived level of usefulness. Full article

Weak sustainability, WS, attempts a comprehensive notion of sustainability, sustaining human welfare directly, or equivalently, sustaining inclusive wealth, IW, sufficient to sustain welfare. Sustainability is, in principle, forever, and accordingly, IW is conceived and assessed in a very long-term context. Given that future outcomes are unobservable, IW assessments are conducted in terms of expectations. However, this introduces pervasive circular reasoning: the calculated value of IW assumes that our expectations will be met, but that is the question. Optimistic expectations (for example) increase calculated IW, which, in turn, increases our confidence that our society is on a sustainable path. Given the logical difficulties of projecting IW into the future, analysts resort to tracking IW at regular intervals through the recent past. This reduces, but does not eliminate, the circularity problem. The signals from tracking IW are less than perfect from a policy perspective: they are too aggregate, perhaps masking impending crises regarding particular resources until it is too late; and too dependent on imperfect markets; and they document the recent past, so policy managers are always playing catch-up. WS-based sustainability policy frameworks include WS-plus, which invokes ad hoc strong sustainability, SS, patches to address threatened resource crises. It may also be possible to allow a degree of WS flexibility for individual jurisdictions within the constraints of a global safe operating space, SOS. Full article

Speeding (travelling at speeds above the speed limit) is proven to be a major contributor to serious crashes, and speed management interventions including speed cameras are shown to reduce speeds, crashes, and trauma. However, the present review identifies that the range of outcomes reported in evaluations of speed cameras is large, complicating the understanding of effects, and inviting scepticism about the value of speed cameras despite the large numbers of reported successes, as well as systematic reviews and meta-analyses that demonstrate their life- and injury-saving value. Therefore, this review is focused on the factors that contribute to the large range of findings, including reasons for genuine differences in the outcomes delivered by different camera programs and variations in evaluation methodology that influence the extent to which real benefits are detected. Finally, recommendations are offered to maximise the safety benefits of speed-camera programs (including ensuring the full chain of requirements for general deterrence is met; strong communications about new programs and expansions at least several weeks in advance of implementation; and unpredictability of enforcement versus signposted cameras) and to improve evaluation methods (especially around determining the road lengths/locations assumed to be treated by the cameras and use of control locations). Full article

The actual development challenges impose new criteria of national performance evaluation, the concept of wellbeing tending to be measured not just in terms of economic and social dimensions, but also vs. the environment. Accordingly, considering the national environmental performance among the EU countries in 2006–2019 period, we grouped them and concentrated on the clusters registering the highest and lowest levels, analyzing how the components of the human and economic dimensions influence it. Applying panel data models, our main results emphasized that, firstly, for the countries with a better environmental performance, sufficient drinking water, safe sanitation, education, gender equality, and good governance were significant; in the countries with the lowest levels of environmental wellbeing, sufficient food, sufficient to drink, education, and income distribution were insignificant, while the remaining components were relevant. Secondly, in both groups of countries, organic farming and public debt were significant; nevertheless, differences were observed for genuine savings and employment, for which the peculiarities of economic activities seemed to be materialized as different influences upon environmental wellbeing. Our study draws alarm signals regarding the development patterns applied in the EU, seeming to have results that strengthen the sustainable goals, but not sufficient for exceeding the traditional growth-oriented model. Full article

The analysis of sustainable economic growth and development often focuses on how to control the market process through coercive state intervention. While state interventionism may play a significant role in countries’ progress, entrepreneurship is the driving force behind sustainable growth and development. Entrepreneurship is the people’s judgment on ideas, plans, and projects, which promises profit in uncertain times. Its effects are the creation and transmission of information and social coordination as a dynamic process of identifying and solving human problems. Sustainable development is the widening range of entrepreneurial alternatives open to people, and sustainable growth is a phase of sustainable development that depends on genuine savings to finance increasingly capital-intensive production structures. The degree to which people are entrepreneurs and the direction genuine savings take depend on institutional arrangements. Some institutions are more conducive to sustainable growth and development than others. After reviewing principles of growth and development sustainability, how coercive state intervention influences economic performance is discussed, proposing novel policy conclusions and research avenues to cultivate entrepreneurship and genuine savings in a post-COVID-19 world. Full article

Biometric authentication systems raise certain concerns with regard to security, violation of privacy, and storage issues of biometric templates. This paper proposes a protection approach of biometric templates storage in a multimodal biometric system while ensuring both the cancelability of biometric templates and the efficiency of the authentication process. We propose applying a chaotic maps-based transform on the biometric features to address the cancelability issue. We used Logistic map and Torus Automorphism to generate cancelable biometric features of the face and fingerprint minutia points, respectively. Both transformed features would be concatenated and saved in the database of the system instead of the original features. In the authentication stage, the similarity scores of both transformed face and fingerprint templates are computed and fused using the weighted sum rule. The results of the experimentation, conducted using images from the ORL face and FVC2002 DB1 fingerprint databases, demonstrated the higher performance of the proposed approach achieving a genuine accept rate equal to $100\%$. Moreover, the obtained results confirmed the soundness of the proposed cancelable technique to satisfy the biometric systems’ requirements (i.e., security, revocability, and diversity). Full article

This article sheds light on the sophisticated funeral process set up by the Cao Đài religion (or Caodaism), combining both a theological and an ethnographical analysis. After introducing how Cao Đài theology represents both the body and the spiritual components of each individual in the specific millenarian conception of existence that characterizes Cao Đài, we trace the ritual process of funerals from the altar and coffin preparation to the collection of prayers and talismanic rituals conveyed to save souls in a Cao Đài manner. Read together, these sources present a genuine project and spirit of reform in the ideas, imaginaries, and practices related to death in Vietnam from the 1920s onwards, crystallizing a specific Cao Đài identity in Vietnamese and also East Asian redemptive societies’ deathscapes. Full article

A major obstruction in the development of sustainable agriculture is the weakness of the financial and banking sectors in supporting smallholder farming. While farmers need to invest in their farms, they struggle to find credit schemes adapted to their specific needs. This study explores the literature on a range of credit systems applied in different geographical and historical contexts to analyse the underlying drivers of their successes or otherwise. In light of this review, the study investigates a farmers’ association, Alfredo Namitete (AN), in Mozambique, offering a range of agroecology credit modalities. It is then assessed as to whether a new business model initiated with seed funding could be self-managed by the association itself and lead to greater autonomy. The AN pilot tested three schemes between 2015 and 2019. Based on the findings, i.e., better production, increased revenue and greater self-determination, the study combines elements for a new business model for small-scale lending. It concludes that to be effective, a credit scheme needs to meet several conditions simultaneously: believe in the genuine will to repay, abolish the lender–borrower distance, ensure a role for women in decision making, add a savings mechanism, combine individual and collective investments and, finally, reserve funds for solidarity and climate issues. Full article