Search Results (1,989)

The macro-economic growth rate of Peru is analyzed for sensitivity to climatic conditions. Year-on-year fluctuations in the inflation-adjusted gross domestic product (GDP) per capita over the period 1970–2024 are subjected to correlation and composite statistical methods. Upturns relate to cool east Pacific La Niña, downturns with warm El Niño. Composites are analyzed by subtracting upper and lower terciles, representing a difference of ~USD 40 B at current value. These reveal how the regional climate exerts a partial influence among external factors. During the austral summer with southeasterly winds over the east Pacific, sea temperatures undergo a 2.5 °C cooling. Consequently, atmospheric subsidence draws humid air from the Amazon toward the Peruvian highlands, improving crop production. Dry weather along the coast sustains transportation networks and urban infrastructure, ensuring good economic performance over the year. The opposing influence of El Niño is built into the statistics. A multi-variate algorithm is developed to predict changes in the Peru growth rate. Austral summer winds and subsurface temperatures over the tropical east Pacific account for a modest 23% of year-on-year variance. Although external factors and the varied landscape weaken macro-economic links with climate, our predictors significantly improve on traditional indices: SOI and Nino3. Adaptive measures are suggested to take advantage of Southern Oscillation’s influence on Peru’s economy. Full article

In this work, a radar imagery-based signal processing technique to eliminate dynamic clutter interference in Structural Health Monitoring (SHM) is proposed. This can be considered an application of a joint communication and sensing telecommunication infrastructure, leveraging a base-station as ground-based radar. The dynamic clutter is considered to be a fast moving road user, such as car, truck, or moped. The proposed technique is suitable in case of a dynamic clutter, such that its Doppler contribute alias and falls over the 0 Hz component. In those cases, a standard low-pass filter is not a viable option. Indeed, an excessively shallow low-pass filter preserves the dynamic clutter contribution, while an excessively narrow low-pass filter deletes the displacement information and also preserves the dynamic clutter. The proposed approach leverages the Time Domain Backprojection (TDBP), a well-known technique to produce radar imagery, to transfer the dynamic clutter from the data domain to an image plane, where the dynamic clutter is maximally compressed. Consequently, the dynamic clutter can be more effectively suppressed than in the range-Doppler domain. The dynamic clutter cancellation is performed by coherent subtraction. Throughout this work, a numerical simulation is conducted. The simulation results show consistency with the ground truth. A further validation is performed using real-world data acquired in the C-band by Huawei Technologies. Corner reflectors are placed on an infrastructure, in particular a bridge, to perform the measurements. Here, two case studies are proposed: a bus and a truck. The validation shows consistency with the ground truth, providing a degree of improvement within respect to the corrupted displacement on the mean error and its variance. As a by-product of the algorithm, there is the capability to produce high-resolution imagery of moving targets. Full article

Acoustic scattering is a highly effective tool for non-destructive control and structural analysis. In many real-world applications, understanding acoustic scattering is essential for accurately detecting and characterizing defects, assessing material properties, and evaluating structural integrity without causing damage. One of the most critical aspects of characterizing targets—such as plates, cylinders, and tubes immersed in water—is the analysis of the phase and group velocities of antisymmetric circumferential waves (A₁). Phase velocity helps identify and characterize wave modes, while group velocity allows for tracking energy, detecting, and locating anomalies. Together, they are essential for monitoring and diagnosing cylindrical shells. This research employs a Sugeno fuzzy inference system (SFIS) combined with a Fuzzy Subtractive Clustering (FSC) identification technique to predict the velocities of antisymmetric (A₁) circumferential signals propagating around an infinitely long cylindrical shell of different b/a radius ratios, where a is the outer radius, and b is the inner radius. These circumferential waves are generated when the shell is excited perpendicularly to its axis by a plane wave. Phase and group velocities are determined by using resonance eigenmode theory, and these results are used as training and testing data for the fuzzy model. The proposed approach demonstrates high accuracy in modeling and predicting the behavior of these circumferential waves. The fuzzy model’s predictions show excellent agreement with the theoretical results, as confirmed by multiple error metrics, including the Mean Absolute Error (MAE), Standard Error (SE), and Mean Relative Error (MRE). Full article

As artificial intelligence (AI) reshapes production, its integration into manufacturing offers gains in precision, efficiency, and sustainability. Globally, AI supports additive, subtractive, and forming processes through optimization, monitoring, defect detection, and design innovation. In Latin America, however, adoption is limited and uneven, with most evidence from surveys, policy reports, and pilot projects rather than large-scale implementations. This review addresses that gap by examining the global landscape of AI in manufacturing and the specific conditions influencing its adoption in the region. The study is guided by the question: What structural conditions are required to enable successful and sustainable AI integration in Latin American manufacturing? To answer, it applies the Triadic Integration Framework, which identifies three pillars: digital infrastructure, policy and governance, and socio-industrial capacity. The analysis highlights barriers, including fragmented regulation, skills shortages, cybersecurity risks, and cost–benefit uncertainties, while also pointing to opportunities in various industrial sectors. To translate insights into practice, a phased roadmap is proposed, outlining short-term, medium-term, and long-term actions, along with the responsible stakeholders and the necessary resources. As an integrative review, the study synthesizes existing knowledge to build a framework, defining directions for future research, emphasizing that successful adoption requires technical progress, inclusive governance, and regional coordination. Full article

Background/Objectives: Recently, a novel CT perfusion (CTP) parameter, the compensation index (COMPI; ratio of 4 s delayed perfusion to 6 s delayed perfusion), was shown to correlate more strongly with digital subtraction angiographic collaterals than the cerebral blood volume index (CBVI) and hypoperfusion intensity ratio. Methods: We retrospectively analyzed all anterior circulation large vessel occlusion patients treated at multiple thrombectomy centers from January to December 2024 to determine the relationship of COMPI and other CTP parameters with the primary outcome: a 90-day modified Rankin Scale (mRS) score of 0–2. Univariable logistic regression was performed to assess the association between each CTP parameter and the primary outcome in the full cohort and in those achieving endovascular reperfusion (modified treatment in cerebral ischemia 2b-3). Multivariable logistic regression was performed to determine factors independently associated with a 90-day mRS score of 0–2. Results: 323 subjects (median age 69 [57–78] years, median of National Institutes of Health Stroke Scale 15 [10–19.5]) were included, of whom 146/302 (48.3%) were functionally independent at 90 days. The COMPI was not associated with the primary outcome in the univariate analysis. CBVI was the only CTP parameter independently associated with a 90-day mRS score of 0–2 in the full cohort (per 0.1-point increase, odds ratio 1.349, 95% confidence interval 1.099–1.655, p = 0.004) and in those achieving reperfusion. Conclusions: The COMPI was not associated with a 90-day mRS score of 0–2. CBVI was associated with independent neurological function in the full cohort and in reperfused patients, supporting its role as a CTP collateral biomarker and potential risk stratification tool before thrombectomy. Full article

Due to its unique benefits over conventional subtractive manufacturing, additive manufacturing methods continue to attract interest in both academia and industry. One such method is called Cold Spray Additive Manufacturing (CSAM), a solid-state coating deposition technology to manufacture repair metallic components using a gas jet and powder particles. CSAM offers low heat input, stable phases, suitability for heat-sensitive substrates, and high deposition rates. However, persistent challenges include porosity control, geometric accuracy near edges and concavities, anisotropy, and cost sensitivities linked to gas selection and nozzle wear. Interdisciplinary research across manufacturing science, materials characterisation, robotics, control, artificial intelligence (AI), and machine learning (ML) is deployed to overcome these issues. ML supports quality prediction, inverse parameter design, in situ monitoring, and surrogate models that couple process physics with data. To demonstrate the impact of AI and ML on CSAM, this study presents a systematic literature review to identify, evaluate, and analyse published studies in this domain. The most relevant studies in the literature are analysed using keyword co-occurrence and clustering. Four themes were identified: design for CSAM, material analytics, real-time monitoring and defect analytics, and deposition and AI-enabled optimisation. Based on this synthesis, core challenges are identified as small and varied datasets, transfer and identifiability limits, and fragmented sensing. Main opportunities are outlined as physics-based surrogates, active learning, uncertainty-aware inversion, and cloud-edge control for reliable and adaptable ML use in CSAM. By systematically mapping the current landscape, this work provides a critical roadmap for researchers to target the most significant challenges and opportunities in applying AI/ML to industrialise CSAM. Full article

Students with autism spectrum disorder (ASD) often face difficulties in solving arithmetic word problems, particularly in transitioning from informal counting strategies to more efficient methods based on number facts and formal operations. This study examined the development of problem-solving strategies in a single student with ASD and intellectual disability across two sequential single-case experiments using multiple baseline designs. Study 1 (age 13 years 9 months; 17 sessions) employed Modified Schema-Based Instruction (MSBI) to teach addition and subtraction change problems, while Study 2 (age 14 years 10 months; 18 sessions) utilized the Conceptual Model-based Problem Solving (COMPS) approach for multiplication and division equal-group problems. Success was defined as both correctness of the response and correctly identifying the required operation. Results indicated that the student’s performance improved in all problem types in both studies, with maintenance observed 8 weeks after Study 1 and 5 weeks after Study 2. Instruction effects generalized to two-step addition and subtraction problems in Study 1, and to two-step addition and multiplication problems in Study 2. The findings indicate that both MSBI and COMPS facilitated the student’s shift from informal strategies to efficient operation-based problem solving. Implications for practice include the need for individualized reinforcements, careful adaptation of instruction, and providing teachers with a variety of problems and knowledge of these teaching methods to support students with ASD in developing advanced problem-solving skills. Full article

Satellite remote sensing has emerged as a reliable and cost-effective approach for monitoring inland water quality, offering spatial and temporal advantages over traditional in situ methods. Lake Victoria, the largest tropical lake and a critical freshwater resource for East Africa, faces increasing eutrophication driven by nutrient inflows from agriculture, urbanization, and industrial activities. This study assessed the spatiotemporal dynamics of water quality along Uganda’s Lake Victoria coast by integrating field measurements (2014–2024) with Landsat 8/9 imagery. Chlorophyll-a, a proxy for algal blooms, and Secchi disk depth, an indicator of water clarity, were selected as key parameters. Cloud-free satellite images were processed using the Dark Object Subtraction method, and spectral reflectance values were correlated with field data. Linear regression models from single bands and band ratios showed strong performance, with adjusted R² values of up to 0.88. When tested on unseen data, the models achieved R² values above 0.70, confirming robust predictive ability. Results revealed high algal concentrations for nearshore and clearer offshore waters. These models provide an efficient framework for monitoring eutrophication, guiding restoration priorities, and supporting sustainable water management in Lake Victoria. Full article

Distinct PAEs are implicated in breast cancer progression through multiple molecular pathways. This study aims to elucidate the potential mechanisms in common by which PAEs promote breast cancer progression. Dibutyl phthalate (DBP), benzyl butyl phthalate (BBP), and diethylhexyl phthalate (DEHP) were selected as representative PAE compounds. Network toxicology guided the construction of a regulatory network centered on five key transcription factor-associated genes: TP53, CTNNB1, PPARA, ESR1, and CDKN2A. Differential expression and survival analyses confirmed the significant impact of these hub genes on breast cancer (p < 0.05). Molecular docking results revealed direct interactions between the three PAEs and hub targets, while BBP had the strongest PAE-hub gene interaction and DEHP had the weakest one. Computer-aided molecular design (CAMD), combined with molecular docking, found the importance of alkyl chains and phenyl in PAE-hub gene interaction. A group addition/subtraction controlled experiment revealed that the binding affinities of modified BBP variants to hub genes are all weaker than the unmodified parent. The drop was significant whether the C17 alkyl chain was lengthened to match DEHP (p = 0.026) or the phenyl group was removed (p = 0.022). The findings provide novel insights into the mechanism in common of PAE-promoting breast cancer and offer a foundation for the unified intervention strategies and the design of safer plasticizer alternatives. Full article

Rapidly pinpointing the origin of accidental chemical gas releases is essential for effective response. Prior vision pipelines—such as 3D CNNs, CNN–LSTMs, and Transformer-based ViViT models—can improve accuracy but often scale poorly as the temporal window grows or winds meander. We cast recursive backtracking of concentration fields as a finite-horizon, multi-step spatiotemporal sequence modelling problem and introduce Recursive Backtracking with Visual Mamba (RBVM), a Visual Mamba-inspired, directionally gated state-space backbone. Each block applies causal, depthwise sweeps along ${\mathrm{H}}^{\pm }$, ${\mathrm{W}}^{\pm }$, and ${\mathrm{T}}^{\pm }$ and then fuses them via a learned upwind gate; a lightweight MLP follows. Pre-norm LayerNorm and small LayerScale on both branches, together with a layer-indexed, depth-weighted DropPath, yield stable stacking at our chosen depth, while a 3D-Conv stem and head keep the model compact. Computation and parameter growth scale linearly with the sequence extent and the number of directions. Across a synthetic diffusion corpus and a held-out NBC_RAMS field set, RBVM consistently improves Exact and hit $\le 1$ over strong 3D CNN, CNN–LSTM, and ViViT baselines, while using fewer parameters. Finally, we show that, without retraining, a physics-motivated two-peak subtraction on the oldest reconstructed frame enables zero-shot dual-source localization. We believe RBVM provides a compact, linear-time, directionally causal backbone for inverse inference on transported fields—useful not only for gas–release source localization in CBRN response but more broadly for spatiotemporal backtracking tasks in environmental monitoring and urban analytics. Full article

Online customer reviews contain rich sentimental expressions of customer preferences on products, which is valuable information for analyzing customer preferences in product design. The adaptive neuro fuzzy inference system (ANFIS) was applied to the establishment of customer preference models based on online reviews, which can address the fuzziness of customers’ emotional responses in comments and the nonlinearity of modeling. However, due to the black box problem in ANFIS, the nonlinearity of the modeling cannot be shown explicitly. To solve the above problems, a chaos-driven ANFIS approach is proposed to develop customer preference models using online comments. The model’s nonlinear relationships are represented transparently through the fuzzy rules obtained, which provide human-readable equations. In the proposed approach, online reviews are analyzed using sentiment analysis to extract the information that will be used as the data sets for modeling. After that, the chaos optimization algorithm (COA) is applied to determine the polynomial structure of the fuzzy rules in ANFIS to model the customer preferences. Using laptop products as a case study, several approaches are evaluated for validation, including fuzzy regression, fuzzy least-squares regression, ANFIS, ANFIS with subtractive cluster, and ANFIS with K-means. Compared to the other five approaches, the values of mean relative error, variance of error, and confidence interval of validation error are improved based on the proposed approach. Full article

Osseodensification (OD) has emerged as a favorable osteotomy preparation technique that preserves and compacts autogenous bone along the osteotomy walls during site preparation, enhancing primary stability and implant osseointegration. While OD has demonstrated promising results in low-density trabecular bone, especially when used in conjunction with acid-etched (AE) implant surfaces, its efficacy in high-density cortical bone remains unclear—particularly in the context of varying implant surface characteristics. In this study, Grade V titanium alloy implants (Ti-6Al-4V, 4 mm × 10 mm) with deep threads, designated bone chambers and either as-machined (Mach) or AE surfaces were placed in 3.8 mm diameter osteotomies in the submandibular region of 16 adult sheep using either OD or conventional (Reg) drilling protocols. Insertion torque values (N·cm) were measured at the time of implant placement to evaluate primary stability. Mandibles were harvested at 3-, 6-, 12-, or 24-weeks post-implantation (n = 4 sheep/time point), and histologic sections were analyzed to quantify bone-to-implant contact (BIC) and bone area fractional occupancy (BAFO). Qualitative histological analysis confirmed successful osseointegration among all groups at each of the healing time points. No statistically significant differences were observed between OD and conventional drilling techniques in insertion torque (p > 0.628), BIC (p > 0.135), or BAFO (p > 0.060) values, regardless of implant surface type or healing interval. The findings indicate that neither drilling technique nor implant surface treatment significantly influences osseointegration in high density cortical bone. Furthermore, as the osteotomy was not considerably undersized, the use of OD instrumentation showed no signs of necrosis, inflammation, microfractures, or impaired osseointegration in dense cortical bone. Both OD and Reg techniques appear to be suitable for implant placement in dense bone, allowing flexibility based on surgeon preference and clinical circumstances. Full article

Polymeric optical waveguides represent an essential component in photonic technology thanks to their ability to guide light through controlled structures, enabling applications in telecommunications, sensors, and integrated devices. With the development of new materials and increasingly versatile manufacturing methods, these structures are being integrated into various systems at a rapid pace, while their dimensions are constantly being reduced. This article explores the main fabrication methods for polymeric optical waveguides, such as traditional and maskless photolithography, laser ablation, hot embossing, nanoimprint lithography, the Mosquito method, inkjet printing, aerosol jet printing, and electrohydrodynamic (EHD) printing. The operating principle of each method, the equipment and materials used, and their advantages, limitations, and practical applications are evaluated, in addition to the propagation losses and characterization of the waveguides obtained with each method. Full article

Over millions of years of evolution, nature provided tools to optimize different functions in animals and plants. Different strategies observed in nature serve as models for solving complex engineering problems. Additive manufacturing (AM), also known as 3D printing, enables us to produce shapes that would not be possible with traditional subtractive manufacturing. In this way, it is possible to produce complex detailed shapes using an automatic process. Biomimetics involves drawing inspiration from nature and applying it to solve specific engineering challenges, often with the goal of optimization and enhanced performance. Three-dimensional printing enables the replication of complex natural shapes, opening new avenues for innovation. In this paper, we review the state of the art in biomimetics, including studies on mechanical properties, design strategies, manufacturing techniques, and the use of composites. Full article

This essay examines mass castration allegations within the North Indian guru movement Dera Sacha Sauda. Drawing on court records, public commentary, and prior fieldwork, it traces how surgical procedures served as a mechanism of enforced proximity and devotional binding. Castration here functions less as renunciation than as anatomical control within a system of engineered devotion that sutures followers into machinic forms of loyalty. The essay situates these acts within a broader politics of sacrificial excess, linking them to hijra initiation, Mughal-coded sovereignty, and strategies of masculine containment. What emerges is a devotional regime of irreversible subtraction and a sovereignty staged through ritual overreach. Full article