Robust Measure Research Articles

Associations of Usual and Fast Gait Speed With Physical Performance and Balance Confidence in Community-Dwelling Older Adults: Implications for Assessment.

Gait speed is a robust measure that offers many advantages clinically. However, decisions concerning its utilization exist, including whether to assess usual or fast gait speed. The current study aimed to identify whether usual or fast gait speed was more strongly associated with physical performance measures and balance confidence. A secondary aim was to explore these relationships within subgroups based on fall risk status. This was an observational study with a cross-sectional design involving 57 community-dwelling older adults (77.2% female; mean age = 68.8, SD = 6.5 years, range = 60-87 years) who were assessed on the following variables: usual and fast gait speed, knee extension strength, step execution time, 6-minute walk test (6MWT), and Activities-specific Balance Confidence Scale (ABC-6). Spearman ρ correlations were computed to determine bivariate associations of usual and fast gait speed with physical performance measures and balance confidence for the whole sample and within subgroups based on fall risk [lower fall risk (n = 28) vs higher fall risk (n = 29)]. Multiple linear regression models were estimated with either usual or fast gait speed as key predictors of knee extension strength, step execution time, 6MWT, and ABC-6. Stronger correlations were observed for fast gait speed compared with usual gait speed with all physical performance measures and balance confidence for the entire sample and within the higher fall risk group. Multiple regression results indicated that models with fast rather than usual gait speed as the key predictor explained more of the variance in 6MWT ( R2 = 64.5% vs 45.6%), ABC-6 ( R2 = 28.5% vs 25.4%), step execution time ( R2 = 24.9% vs 19.0%), and knee extension strength ( R2 = 15.7% vs 7.2%). Fast gait speed showed stronger associations and better predictive capabilities compared with usual gait speed with physical performance measures and balance confidence in older adults. Despite being measured less often than usual gait speed, fast gait speed assessment warrants additional consideration.

MOIRE: A software package for the estimation of allele frequencies and effective multiplicity of infection from polyallelic data.

Malaria parasite genetic data can provide insight into parasite phenotypes, evolution, and transmission. However, estimating key parameters such as allele frequencies, multiplicity of infection (MOI), and within-host relatedness from genetic data has been challenging, particularly in the presence of multiple related coinfecting strains. Existing methods often rely on single nucleotide polymorphism (SNP) data and do not account for within-host relatedness. In this study, we introduce a Bayesian approach called MOIRE (Multiplicity Of Infection and allele frequency REcovery), designed to estimate allele frequencies, MOI, and within-host relatedness from genetic data subject to experimental error. Importantly, MOIRE is flexible in accommodating both polyallelic and SNP data, making it adaptable to diverse genotyping panels. We also introduce a novel metric, the effective MOI (eMOI), which integrates MOI and within-host relatedness, providing a robust and interpretable measure of genetic diversity. Using extensive simulations and real-world data from a malaria study in Namibia, we demonstrate the superior performance of MOIRE over naive estimation methods, accurately estimating MOI up to 7 with moderate sized panels of diverse loci (e.g. microhaplotypes). MOIRE also revealed substantial heterogeneity in population mean MOI and mean relatedness across health districts in Namibia, suggesting detectable differences in transmission dynamics. Notably, eMOI emerges as a portable metric of within-host diversity, facilitating meaningful comparisons across settings, even when allele frequencies or genotyping panels are different. MOIRE represents an important addition to the analysis toolkit for malaria population dynamics. Compared to existing software, MOIRE enhances the accuracy of parameter estimation and enables more comprehensive insights into within-host diversity and population structure. Additionally, MOIRE's adaptability to diverse data sources and potential for future improvements make it a valuable asset for research on malaria and other organisms, such as other eukaryotic pathogens. MOIRE is available as an R package at https://eppicenter.github.io/moire/.

Evidential Multi-Source-Free Unsupervised Domain Adaptation.

Multi-Source-Free Unsupervised Domain Adaptation (MSFUDA) requires aggregating knowledge from multiple source models and adapting it to the target domain. Two challenges remain: 1) suboptimal coarse-grained (domain-level) aggregation of multiple source models, and 2) risky semantics propagation based on local structures. In this article, we propose an evidential learning method for MSFUDA, where we formulate two uncertainties, i.e. Evidential Prediction Uncertainty (EPU) and Evidential Adjacency-Consistent Uncertainty (EAU), respectively for addressing the two challenges. The former, EPU, captures the uncertainty of a sample fitted to a source model, which can suggest the preferences of target samples for different source models. Based on this, we develop an EPU-Based Multi-Source Aggregation module to achieve fine-grained, instance-level source knowledge aggregation. The latter, EAU, provides a robust measure of consistency among adjacent samples in the target domain. Utilizing this, we develop an EAU-Guided Local Structure Mining module to ensure the trustworthy propagation of semantics. The two modules are integrated into the Evidential Aggregation and Adaptation Framework (EAAF), and we demonstrated that this framework achieves state-of-the-art performances on three MSFUDA benchmarks.

Measurement-device-independent quantum random number generation over 23 Mbps with imperfect single-photon sources

Quantum randomness relies heavily on the accurate characterization of the generator implementation, where the device imperfection or inaccurate characterization can lead to incorrect entropy estimation and practical bias, significantly affecting the reliability of the generated randomness. Measurement-device-independent (MDI) quantum random number generation (QRNG) endeavors to produce certified randomness, utilizing uncharacterized and untrusted measurement devices that are vulnerable to numerous attack schemes targeting measurement loopholes. However, existing implementations have shown insufficient performance thus far. Here, we propose a high-speed MDI-QRNG scheme based on a robust measurement tomography approach against the imperfection of single-photon sources. Compared with the conventional approach, the decoy-state method is introduced to obtain more accurate tomography results and a tighter lower bound of randomness. Finally, by using a high-speed time-bin encoding system, we experimentally demonstrated the scheme and obtained a reliable min-entropy lower bound of 7.37×10−2 bits per pulse, corresponding to a generation rate over 23 Mbps, which substantially outperforms the existing realizations and makes a record in discrete-variable semi-device-independent QRNGs.

Looking back, looking forward: lessons from COVID-19 communication measurement, evaluation and learning (MEL).

Communication is an integral component of an emergency response, including to the coronavirus disease (COVID-19) pandemic. Designing effective communication requires systematic measurement, evaluation and learning. In the Western Pacific Region, the World Health Organization (WHO) responded to the COVID-19 pandemic by using the Communication for Health (C4H) approach. This included the development and application of a robust measurement, evaluation and learning (MEL) framework to assess the effectiveness of COVID-19 communication, and to share and apply lessons in real time to continuously strengthen the pandemic response. MEL was applied during the planning, implementation and summative evaluation phases of COVID-19 communication, with evidence-based insights and recommendations continuously integrated in succeeding phases of the COVID-19 response. This article captures good practices that helped WHO to implement MEL during the COVID-19 pandemic. It focuses on lessons from the evaluation process, including the importance of planning, data integration, collaboration, partnerships, piggybacking, using existing data and leveraging digital media. Despite some limitations, the systematic application of MEL to COVID-19 communication shows its value in the planning and implementation of effective, evidence-based communication to address public health challenges. It enables the evaluation of outcomes and reflection on lessons identified to strengthen the response to the current pandemic and future emergencies.

Attack as Detection: Using Adversarial Attack Methods to Detect Abnormal Examples

As a new programming paradigm, deep learning (DL) has achieved impressive performance in areas such as image processing and speech recognition, and has expanded its application to solve many real-world problems. However, neural networks and DL are normally black-box systems; even worse, DL-based software are vulnerable to threats from abnormal examples, such as adversarial and backdoored examples constructed by attackers with malicious intentions as well as unintentionally mislabeled samples. Therefore, it is important and urgent to detect such abnormal examples. Although various detection approaches have been proposed respectively addressing some specific types of abnormal examples, they suffer from some limitations; until today, this problem is still of considerable interest. In this work, we first propose a novel characterization to distinguish abnormal examples from normal ones based on the observation that abnormal examples have significantly different (adversarial) robustness from normal ones. We systemically analyze those three different types of abnormal samples in terms of robustness and find that they have different characteristics from normal ones. As robustness measurement is computationally expensive and hence can be challenging to scale to large networks, we then propose to effectively and efficiently measure robustness of an input sample using the cost of adversarially attacking the input, which was originally proposed to test robustness of neural networks against adversarial examples. Next, we propose a novel detection method, named attack as detection (A 2 D for short), which uses the cost of adversarially attacking an input instead of robustness to check if it is abnormal. Our detection method is generic, and various adversarial attack methods could be leveraged. Extensive experiments show that A 2 D is more effective than recent promising approaches that were proposed to detect only one specific type of abnormal examples. We also thoroughly discuss possible adaptive attack methods to our adversarial example detection method and show that A 2 D is still effective in defending carefully designed adaptive adversarial attack methods—for example, the attack success rate drops to 0% on CIFAR10.

Synergistic use of deep TMS therapy with IV ketamine infusions for major depressive disorder: a pilot study.

Major Depressive Disorder (MDD) is a pervasive psychiatric condition effecting approximately 21 million adults in the U.S. (8.4%). An estimated 30-60% of patients are resistant to traditional treatment approaches (medications and talk-therapy), alluding to the need for additional options. Two promising treatment modalities include transcranial magnetic stimulation (TMS) and ketamine infusions; both have shown efficacy in standalone studies but have scarcely been investigated synergistically in the same group of participants. In the current study, 169 participants with treatment-resistant MDD received 36 treatments of Deep TMS-only (H1 + H7 protocols), while 66 received 36 treatments of Deep TMS (H1 + H7 protocols) and 6 IV infusions of ketamine over the course of 9weeks. Depressive symptoms were compared pre- and -post treatment in both conditions using the PHQ-9. In both treatment groups, depressive symptoms were significantly reduced from pre-to-post and there were no significant differences in response between the TMS + ketamine condition and the TMS-only condition. The TMS + ketamine condition had an 80.30% response rate (53 out of 66) and 43.42% remission rate (28 out of 66) compared to a 76.92% response (130 out of 169) and 39.64% remission (67 out of 16) in the TMS-only condition. These results support the notion that TMS treatments yield high response rates in treatment-resistant cases; however, in this investigation there was no added benefit for including 6 sessions of IV ketamine in conjunction with TMS. Future investigations using randomized-control designs and robust outcome measures are warranted.

EMERGING TRENDS IN CYBERSECURITY FOR CRITICAL INFRASTRUCTURE PROTECTION: A COMPREHENSIVE REVIEW

As critical infrastructure becomes increasingly interconnected and digitized, the need for robust cybersecurity measures to safeguard essential systems is more pressing than ever. This review article explores the dynamic landscape of cybersecurity for critical infrastructure, focusing on emerging trends, current challenges, and future prospects. The historical overview delves into the evolution of cyber threats, emphasizing the need for adaptive security measures. Key components of critical infrastructure are examined, elucidating the specific challenges each sector faces. The current state of critical infrastructure cybersecurity is analyzed, with a spotlight on frameworks that guide organizations in bolstering their defenses. The heart of the review explores emerging trends in cybersecurity, covering artificial intelligence and machine learning for threat detection, IoT security, blockchain applications, and advancements in cloud computing security. Challenges and threats on the horizon, including advanced persistent threats and quantum computing implications, are scrutinized to provide insights into potential vulnerabilities.
 Keywords: Cybersecurity; Critical Infrastructure; Artificial Intelligence; Internet-of-Things; Blockchain.

Leveraging the potential of artificial intelligence (AI) in exploring the interplay among tax revenue, institutional quality, and economic growth in the G-7 countries

AbstractDue to G-7 countries' commitment to sustaining United Nations Sustainable Development Goal 8, which focuses on sustainable economic growth, there is a need to investigate the impact of tax revenue and institutional quality on economic growth, considering the role of artificial intelligence (AI) in the G-7 countries from 2012 to 2022. Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL) technique is used to analyze the data. The study's findings indicate a long-run equilibrium relationship among the variables under examination. The causality results can be categorized as bidirectional, unidirectional, or indicating no causality. Based on the CS-ARDL results, the study recommends that G-7 governments and policymakers prioritize and strengthen the integration of AI into their institutions to stimulate growth in both the short- and long-term. However, the study cautions against overlooking the interaction between AI and tax revenue, as it did not demonstrate support for economic growth. While the interaction between AI and institutional quality shows potential for contributing to growth, it is crucial to implement robust measures to mitigate any potential negative effects that may arise from AI's interaction with tax systems. Therefore, the study suggests the development of AI-friendly tax policies within the G-7 countries, considering the nascent nature of the AI sector/industry.

Deciphering and Measures of Robustness and Resilience in Farm Animal: A Review

The ability of an animal to adapt to transient environmental difficulties and regain its pre-challenge state is known as resilience, and it is a dynamic and multifaceted characteristic. The ability of the herd to adapt to often shifting and uncertain environmental conditions can be strengthened by resilient animals. The capacity of contemporary technologies to capture many performance metrics of individual animals in real time is a significant advancement in assessing the resilience of farm animals. Resilience, however, cannot be measured directly; instead, quantitative resilience indicators must be derived from mathematical models with biologically meaningful characteristics. This work aimed to review and demonstrate, via examples, several modeling methodologies used with this new type of data (high-frequency recording) in order to identify and measure animal reactions to disturbances.

Exploring the Future of Indoor Navigation: AR Technology

Indoor navigation within complex environments remains a recurring challenge in the age of rapid technological advancement. Traditional methods often fall short when applied to intricate indoor spaces like shopping malls, airports, and corporate campuses. This paper introduces ARNav, an innovative Augmented Reality (AR) based indoor navigation system designed to enhance indoor navigation accuracy and user experience. ARNav utilizes AR technology to overlay digital information, such as arrows, markers, and directional cues, onto the real-time camera view of users' devices. This dynamic overlay simplifies navigation while offering an immersive experience. The system achieves precise indoor positioning through sensor fusion and mapping techniques, boasts an intuitive user-centric design, and implements robust data security measures. Energy efficiency optimization ensures longer battery life, and the system seamlessly integrates with external data sources, offering real-time information on points of interest and events within indoor spaces. A user feedback mechanism allows for continuous improvement, placing user needs at the forefront of development. This paper discusses the design and implementation of ARNav, highlighting the technologies and mathematical models for accurate positioning and AR overlay. ARNav represents a promising solution for indoor wayfinding, combining AR technology with precise indoor navigation to enhance user experiences. The paper explores potential future developments, including advanced AR features, machine learning integration, and compatibility with various devices and platforms, thus contributing to the field of indoor navigation and AR technology.

Simplified equations for wet bulb globe temperature estimation in Bangladesh

AbstractThe increasing temperatures and shifts in meteorological conditions have heightened the vulnerability of Bangladesh's densely populated regions, known for their high heat and humidity, to potential health hazards. While the Wet Bulb Globe Temperature (WBGT) is a widely acknowledged and robust measure for evaluating heat stress in various occupational and outdoor environments, its widespread application is impeded by the complexity of calculations, the substantial computational resources required, and the need for specialized expertise, particularly in developing nations. Therefore, this study aimed to develop simplified equations for estimating WBGT in Bangladesh using meteorological variables. This study applied Liljegren's model on high‐resolution reanalysis data of the European Centre for Medium‐Range Weather Forecasts (ERA5) to calculate WBGT from 1979 to 2021. Subsequently, linear and nonlinear regressions were used to derive simplified equations for estimating Liljegren WBGT in Bangladesh. The quadratic regression models offer simplified equations for WBGT estimation. The model with only temperature (Ta) as an input estimates WBGT with an R2 of 0.967 and an RMSE of 0.716, effectively capturing a significant portion of WBGT variability. The inclusion of solar radiation (SR) with Ta improved the performance, with an R2 of 0.996 and an RMSE of 0.242. The best parsimonious model, with an R2 of 0.986 and an RMSE of 0.471, is derived when wind speed is considered with Ta and SR inputs, achieving an R2 of 0.993 and an RMSE of 0.331. Comprehensive graphical and statistical analyses confirm the high accuracy of all models. The three‐input model notably demonstrates exceptional performance, attaining optimal values for critical metrics, including extreme WBGT. The results affirm the practical suitability of the derived models for accurate estimations of WBGT in Bangladesh. These equations provide simplified tools for assessing heat stress conditions, contributing to public health initiatives, occupational safety guidelines, and climate change adaptation strategies.

Potential Reductions in the Environmental Impacts of Agricultural Production in Hubei Province, China

Quantifying potential reductions in environmental impacts for multi-crop agricultural production is important for the development of environmentally friendly agricultural systems. To analyze the spatial differences in the potential reduction in nitrogen (N) use, we provided a framework that comprehensively assesses the potential of improving N use efficiency (NUE) and mitigating environmental impacts in Hubei Province, China, for multiple crops including rice, wheat, maize, tea, fruits, and vegetables, by considering N and its environmental indicators. This framework considers various sources such as organic N fertilizers and synthetic fertilizers, along with their respective environmental indicators. We designed different scenarios assuming varying degrees of improvement in the NUE for cities with a low NUE. By calculating the N rate, N surplus, N leaching, and greenhouse gas (GHG) emissions under different scenarios, we quantified the environmental mitigation potential of each crop during the production process. The results showed that when the NUE of each crop reached the average level in Hubei Province, the improvement in environmental emissions is favorable compared to other scenarios. The N rate, N surplus, N leaching, and GHG emissions of grain (cash) crops could be reduced by 25.87% (41.26%), 36.07% (38.90%), 49.47% (36.14%), and 51.52% (41.67%), respectively. Overall, improving the NUE in cash crops will result in a greater proportionate reduction in environmental impacts than that in grain crops, but grain crops will reduce the total amount of GHG emissions. Our method provides a robust measure to assess the reduction potential of N pollution and GHG emissions in multi-crop production systems.

Development of a functional outcome measure for riboflavin transporter deficiency.

Riboflavin transporter deficiency (RTD) is a progressive inherited neuropathy of childhood onset, characterised clinically by pontobulbar palsy, sensory ataxia, sensorineural deafness, muscle weakness, optic atrophy and respiratory failure. A robust and responsive functional outcome measure is essential for future clinical trials of disease-modifying therapies including genetic therapies. The Charcot-Marie-Tooth disease Pediatric Scale (CMTPedS) is a well-validated outcome measure for CMT and related neuropathies, and might have utility for measuring disease progression in individuals with RTD. However, the CMTPedS requires modifications to account for phenotypic differences between children with CMT and RTD. The aim of this study was to develop a functional outcome measure based on the CMTPedS for specific use in individuals with RTD. The CMTPedS data collected over the last 10 years in individuals with RTD attending the Peripheral Neuropathy Management Clinic at the Children's Hospital at Westmead (Sydney, Australia) were reviewed to evaluate each item within the CMTPedS. A literature review of articles published until September 2021 for functional outcome measures generated an item pool for pilot testing. The results of this pilot testing, alongside analysis of existing CMTPedS item scores in the RTD cohort, informed the modification of the CMTPedS. CMTPedS data were reviewed for eight individuals over the past 10 years. Two items were identified as requiring modification or removal and additional items of proximal strength and function needed to be considered. Six studies were identified in the literature review, and five items were selected for pilot testing. 'Shoulder internal rotation' and the '30-s sit to stand test' were added as proximal measures of strength and function. The composite balance item comprising nine tasks in the CMTPedS showed a ceiling effect and was replaced with the single 'Feet apart on a line eyes open' balance item. 'Pinprick sensation' was removed due to a floor effect. This study provides preliminary evidence that the Riboflavin Transporter Deficiency Pediatric Scale (RTDPedS) is a functional outcome measure covering strength, upper and lower limb function, balance and mobility for individuals with RTD to assess disease severity and progression in clinical trials and cohort studies.

Sensor-Based Measurement Method to Support the Assessment of Robot-Assisted Radiofrequency Ablation.

Digital surgery technologies, such as interventional robotics and sensor systems, not only improve patient care but also aid in the development and optimization of traditional invasive treatments and methods. Atrial Fibrillation (AF) is the most common cardiac arrhythmia with critical clinical relevance today. Delayed intervention can lead to heart failure, stroke, or sudden cardiac death. Although many advances have been made in the field of radiofrequency (RF) catheter ablation (CA), it can be further developed by incorporating sensor technology to improve its efficacy and safety. Automation can be utilized to shorten the duration of RF ablation, provided that the interactions between the tissue and the RF tools are well understood and adequately modeled. Further research is needed to develop the optimal catheter design. This paper describes the systematic methodology developed to support robot-assisted RF CA characterization measurements. The article describes the custom instruments developed for the experiments, particularly the contact force limiter, the measurement procedure, and the evaluation of the results, as enablers for new results. The aim was to establish an objective, repeatable, robust measurement method and adjacent procedure.

Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing.

The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust QA measures with quality control (QC) procedures that encompass (1) acceptance testing (AT) before clinical use, (2) continuous QC monitoring, and (3) adequate user training. The discussion also covers essential components of AT and QA, illustrated with real-world examples. We also highlight what we see as the shared responsibility of manufacturers or vendors, regulators, healthcare systems, medical physicists, and clinicians to enact appropriate testing and oversight to ensure a safe and equitable transformation of medicine through AI.

Electrochemical DNA-based sensors for measuring cell-generated forces

Mechanical forces play an important role in cellular communication and signaling. We developed in this study novel electrochemical DNA-based force sensors for measuring cell-generated adhesion forces. Two types of DNA probes, i.e., tension gauge tether and DNA hairpin, were constructed on the surface of a smartphone-based electrochemical device to detect piconewton-scale cellular forces at tunable levels. Upon experiencing cellular tension, the unfolding of DNA probes induces the separation of redox reporters from the surface of the electrode, which results in detectable electrochemical signals. Using integrin-mediated cell adhesion as an example, our results indicated that these electrochemical sensors can be used for highly sensitive, robust, simple, and portable measurements of cell-generated forces.

The link among X-ray spectral properties, AGN structure, and the host galaxy

In this work, we compare the supermassive black hole (SMBH) and host galaxy properties of X-ray obscured and unobscured AGN. For that purpose, we used ∼35 000 X-ray detected AGN in the 4XMM-DR11 catalogue for which there are available measurements for their X-ray spectral parameters, such as the hydrogen column density, NH, and photon index, Γ, from the XMM2Athena Horizon 2020 European project. We constructed the spectral energy distributions (SEDs) of the sources, and we calculated the host galaxy properties via SED fitting analysis, utilising the CIGALE code. We applied strict photometric requirements and quality selection criteria to include only sources with robust X-ray and SED fitting measurements. Our sample consists of 1443 AGN. In the first part of our analysis, we used different NH thresholds (1023 cm−2 or 1022 cm−2) while also taking into account the uncertainties associated with the NH measurements in order to classify these sources as obscured and unobscured (or mildly obscured). We find that obscured AGN tend to live in more massive systems (by ∼0.1 dex) that have a lower star-formation rate, SFR, (by ∼0.25 dex) compared to their unobscured counterparts. However, only the difference in stellar mass, M*, appears statistically significant (> 2σ). The results do not depend on the NH threshold used to classify AGN. The differences in M* and SFR are not statistically significant for luminous AGN (log (LX,2−10 KeV/erg s−1) > 44). Our findings also show that unobscured AGN have, on average, higher specific black hole accretion rates, λsBHAR, compared to their obscured counterparts, a parameter which is often used as a proxy of the Eddington ratio. In the second part of our analysis, we cross-matched the 1443 X-ray AGN with the SDSS DR16 quasar catalogue of Wu and Shen to obtain information on the SMBH properties of our sources. This resulted in 271 type 1 AGN at z < 1.9. Our findings show that type 1 AGN with increased NH (> 1022 cm−2) tend to have higher black hole masses, MBH, compared to AGN with lower NH values at similar M*. The MBH/M* ratio remains consistent for NH values below 1022 cm−2, but it exhibits signs of increasing at higher NH values. Finally, we detected a correlation between Γ and Eddington ratios, but only for type 1 sources with NH < 1022 cm−2.

Analysis of Potential Environmental Risks in the Hydraulic Fracturing Operation in the “La Luna” Formation in Colombia

This article presents an in-depth analysis of potential environmental risks associated with hydraulic fracturing operations within the “La Luna” formation in Colombia. Using the Conesa methodology, it assesses the environmental impacts of unconventional reservoir production in Colombia, including water usage, chemical additives, air emissions, and the potential for groundwater contamination. This study incorporates comprehensive data on geological characteristics, operational procedures, and environmental conditions specific to the region. This analysis highlights the need for a proactive approach to managing potential environmental risks associated with hydraulic fracturing in Colombia. The findings underscore the importance of implementing robust regulatory measures, comprehensive monitoring systems, and the industry’s best practices to mitigate and prevent adverse environmental impacts. This research contributes to the ongoing global dialogue on the environmental implications of hydraulic fracturing in regions with sensitive ecological conditions. The environmental impacts of unconventional reservoirs are widely recognized as a general consensus; however, the absence of applied studies with the rigor of an explicit methodology in Andean countries highlights the need for specific research in this region. This article addresses this gap by proposing a detailed and structured methodology to assess and mitigate environmental impacts in unconventional reservoirs. Emphasizing the importance of knowing the characteristics of reservoir fluids, this research highlights that this critical information is only revealed by drilling exploration wells and PVT (pressure, volume, and temperature) analysis. Obtaining this data is crucial for shaping specific mitigation measures, thus allowing for the formulation of a robust environmental management plan tailored to the conditions of the reservoirs in the Andean region. This precise and contextualized approach contributes to closing the knowledge gap and promotes more sustainable practices in the exploitation of unconventional reservoirs in this specific geographical context. The methodology used in this study proved its effectiveness by accurately quantifying the risks associated with each of the environmental alterations inherent to the hydraulic fracturing process in the La Luna formation. The results obtained allowed for the identification of the critical points most susceptible to environmental impacts, serving as a solid basis for the elaboration of an environmental management plan. This strategic approach not only enabled the delineation of specific mitigation measures but also facilitated the selection of the most appropriate locations for the implementation of the plan, maximizing the effectiveness of corrective actions. It is noteworthy that the successful application of the Conesa methodology in this unconventional reservoir context evidences the versatility and applicability of this approach, consolidating it as a valuable tool for environmental assessment and effective planning in the hydraulic fracturing industry.

Anthropogenic climate change has influenced global river flow seasonality.

Riverine ecosystems have adapted to natural discharge variations across seasons. However, evidence suggesting that climate change has already impacted magnitudes of river flow seasonality is limited to local studies, mainly focusing on changes of mean or extreme flows. This study introduces the use of apportionment entropy as a robust measure to assess flow-volume nonuniformity across seasons, enabling a global analysis. We found that ~21% of long-term river gauging stations exhibit significant alterations in seasonal flow distributions, but two-thirds of these are unrelated to trends in annual mean discharge. By combining a data-driven runoff reconstruction with state-of-the-art hydrological simulations, we identified a discernible weakening of river flow seasonality in northern high latitudes (above 50°N), a phenomenon directly linked to anthropogenic climate forcing.