Ideal Case Research Articles

Incremental Trainable Parameter Selection in Deep Neural Networks.

This article explores the utilization of the effective degree-of-freedom (DoF) of a deep learning model to regularize its stochastic gradient descent (SGD)-based training. The effective DoF of a deep learning model is defined only by a subset of its total parameters. This subset is highly responsive or sensitive toward the training loss, and its cardinality can be used to govern the effective DoF of a model during training. To this aim, the incremental trainable parameter selection (ITPS) algorithm is introduced in this article. The proposed ITPS algorithm acts as a wrapper over SGD and incrementally selects the parameters for updation that exhibit the maximum sensitivity toward the training loss. Hence, it gradually increases the DoF of the model during training. In ideal cases, the proposed algorithm arrives at a model configuration (i.e., DoF) optimum for the task at hand. This whole process results in a regularization-like behavior induced by a gradual increment of the DoF. Since the selection and updation of parameters is a function of the training loss, the proposed algorithm can be seen as a task and data-dependent regularization mechanism. This article exhibits the general utility of ITPS by evaluating it on various prominent neural network architectures such as CNNs, transformers, recurrent neural networks (RNNs), and multilayer perceptrons. These models are trained for image classification and healthcare tasks using the publicly available CIFAR-10, SLT-10, and MIMIC-III datasets.

Hadamard Product of Monomial Ideals and the Hadamard Package

In this paper, we generalize and study the concept of Hadamard product of ideals of projective varieties to the case of monomial ideals. We have a research direction similar to the one of the join of monomial ideals contained in a paper of Sturmfels and Sullivant. In the second part of the paper, we give a brief tutorial on the Hadamard.m2 package of Macaulay2.

Women's Empowerment and Infant Mortality: Evidence from Rwanda.

Rwanda stands out in East Africa with the lowest infant mortality ratio at 29 per 1000 live births. It also leads in gender equality on the African continent, ranking sixth globally according to the 2022 Global Gender Gap Report. This makes Rwanda an ideal case for studying the link between women's empowerment and infant mortality. This study aims to assess the impact of women's empowerment on infant mortality using data obtained from the Rwanda Demographic and Health Survey conducted in 2005, 2010, and 2015. A three-category women's empowerment index was created using the principal component analysis method. The statistical analysis employed in this study is multivariate binary logistic regression. RESULTS: Results demonstrate a significant impact of women's empowerment on reducing infant mortality, considering regional and residential inequalities, bio-demographic factors, and healthcare variables. The findings contribute to existing literature and highlight the importance of empowering women to improve maternal and child health outcomes.Such empowerment not only enhances health but also supports sustainable development and social progress. Policymakers, healthcare providers, and organizations should prioritize investing in women's empowerment to achieve maternal and child health goals, as empowered women play a pivotal role in driving positive change for a healthier and more equitable society.

The influence of microwave pulse conditions on enantiomer-specific state transfer

We report a combined experimental and theoretical study on the influence of microwave pulse durations on enantiomer-specific state transfer. Two triads of rotational states within a chiral molecule (1-indanol) are selected to address the possible scenarios. In the triad connected to the absolute ground state, the simplest triad that exists for all chiral molecules, the enantiomer-specific state transfer process simplifies into a sequence of two-level transitions. The second triad, including higher rotational states, represents a more generic scenario that involves multiple Rabi frequencies for each transition. Our study reveals that the conventional π2−π−π2 pulse sequence is not the optimal choice, except for the ideal case when in the simplest triad only the lowest state is initially populated. We find that employing a shorter duration for the first and last pulse of the sequence leads to significantly higher state-specific enantiomeric enrichment, albeit at the expense of overall population in the target state. Our experimental results are in very good agreement with theory, substantiating the quantitative understanding of enantiomer-specific state transfer.

Towards Understanding of the Plastic Zone Thermal Residual Stresses in Functionally Graded Materials

Abstract: Functionally graded materials (FGM’s) are advanced composites consisting of two or more materials whose composition and structure changes gradually over the volume, leading to gradual change in its property. Typically one component is a ceramic, while the other is metal or metal alloy. In common embodiments, the composition can change gradually from all ceramic on one side to all metal on the other side, the ceramic contributing high resistance to temperature with the metal contributing high ductility. It’s a way to incorporate favorable properties of two materials into different locations of a single structure. In ideal cases the composition changes gradually but for manufacturing reasons, the changes occurs in homogeneous layers. The boundaries between the layers cause thermal residual stresses to arise, especially during material processing and in cases of cyclic loading. These thermal residual stresses are a result of differences in the thermal expansion coefficient of the varying compositions of materials. Thermal residual stresses may lead to cracking and ultimate failure of FGM’s. This study investigates the formation of thermal residual stresses for a Nickel-Alumina Ni-Al₂O₃ FGM. The Ni-Al₂O₃ system is chosen because it is one of the most common systems used in practice. This work explores the formation, impact and minimization of thermal residual stresses for a number of practical conditions that may arise during processing. All relevant properties are calculated using the rule of mixture equations.

Minimizing the Formation of Thermal Residual Stresses of the Elastic Zone in Functionally Graded Materials

Abstract: Functionally graded materials (FGM’s) are advanced composites consisting of two or more materials whose composition and structure changes gradually over the volume, leading to gradual change in its property. Typically one component is a ceramic, while the other is metal or metal alloy. In common embodiments, the composition can change gradually from all ceramic on one side to all metal on the other side, the ceramic contributing high resistance to temperature with the metal contributing high ductility. It’s a way to incorporate favorable properties of two materials into different locations of a single structure. In ideal cases the composition changes gradually but for manufacturing reasons, the changes occurs in homogeneous layers. The boundaries between the layers cause thermal residual stresses to arise, especially during material processing and in cases of cyclic loading. These thermal residual stresses are a result of differences in the thermal expansion coefficient of the varying compositions of materials. Thermal residual stresses may lead to cracking and ultimate failure of FGM’s. This study investigates the formation of thermal residual stresses for a Nickel-Alumina Ni-Al₂O₃ FGM. The Ni-Al₂O₃ system is chosen because it is one of the most common systems used in practice. This work explores the formation, impact and minimization of thermal residual stresses for a number of practical conditions that may arise during processing. All relevant properties are calculated using the rule of mixture equations. Abaqus, a non-linear FEA solver, is used for all the simulation work. In addition, the study highlights the need for examining elasto-plastic analysis to illuminate the process of crack development which is our future research goal.

Finite Element Modeling of the Plastic Zone Thermal Residual Stresses in Functionally Graded Materials

Abstract: Functionally graded materials (FGM’s) are advanced composites consisting of two or more materials whose composition and structure changes gradually over the volume, leading to gradual change in its property. Typically one component is a ceramic, while the other is metal or metal alloy. In common embodiments, the composition can change gradually from all ceramic on one side to all metal on the other side, the ceramic contributing high resistance to temperature with the metal contributing high ductility. It’s a way to incorporate favorable properties of two materials into different locations of a single structure. In ideal cases the composition changes gradually but for manufacturing reasons, the changes occurs in homogeneous layers. The boundaries between the layers cause thermal residual stresses to arise, especially during material processing and in cases of cyclic loading. These thermal residual stresses are a result of differences in the thermal expansion coefficient of the varying compositions of materials. Thermal residual stresses may lead to cracking and ultimate failure of FGM’s. This study investigates the formation of thermal residual stresses for a Nickel-Alumina Ni-Al₂O₃ FGM. The Ni-Al₂O₃ system is chosen because it is one of the most common systems used in practice. This work explores the formation, impact and minimization of thermal residual stresses for a number of practical conditions that may arise during processing. All relevant properties are calculated using the rule of mixture equations. Abaqus, a non-linear FEA solver, is used for all the simulation work. In addition, the study includes an elasto-plastic analysis to illuminate the process of crack development.

Monsoon Marauders and Summer Violence: Exploring the Spatial Relationship between Climate Change and Terrorist Activity in India

Climate change is a global phenomenon that has been associated with a growing list of concerns in society today, often leaving more questions than answers. Thus, it is no surprise that questions are forming regarding the effects of climate change on global security, and more specifically, terrorism. India is the ideal case study for investigating the relationship between climate change and extremism, with average temperatures in the country reaching record highs as well as having 9,096 terrorist incidents occur during our 20-year study period between 1998 and 2017. Using daily temperature, precipitation, elevation, and distance to the equator data from the National Climatic Data Center and terrorist incidents from the Global Terrorism Data base (GTD), this study assesses the spatial relationship between these factors through geospatial analyses. Suitability analyses indicate that all the climatological variables tested—temperature, precipitation, and elevation—relate to shifting patterns of terrorist activity. We also found that beyond intensity, seasons result in a shifting of patterns in terrorist behavior to other locales. Implications for the global community and for India specifically are discussed.

Robots for Forest Maintenance

Forest fires are becoming increasingly common, and they are devastating, fueled by the effects of global warming, such as a dryer climate, dryer vegetation, and higher temperatures. Vegetation management through selective removal is a preventive measure which creates discontinuities that will facilitate fire containment and reduce its intensity and rate of spread. However, such a method requires vast amounts of biomass fuels to be removed, over large areas, which can only be achieved through mechanized means, such as through using forestry mulching machines. This dangerous job is also highly dependent on skilled workers, making it an ideal case for novel autonomous robotic systems. This article presents the development of a universal perception, control, and navigation system for forestry machines. The selection of hardware (sensors and controllers) and data-integration and -navigation algorithms are central components of this integrated system development. Sensor fusion methods, operating using ROS, allow the distributed interconnection of all sensors and actuators. The results highlight the system’s robustness when applied to the mulching machine, ensuring navigational and operational accuracy in forestry operations. This novel technological solution enhances the efficiency of forest maintenance while reducing the risk exposure to forestry workers.

H3DM: A High-bandwidth High-capacity Hybrid 3D Memory Design for GPUs

Graphics Processing Units (GPUs) are widely used for modern applications with huge data sizes. However, the performance benefit of GPUs is limited by their memory capacity and bandwidth. Although GPU vendors improve memory capacity and bandwidth using 3D memory technology (HBM), many important workloads with terabytes of data still cannot fit in the provided capacity and are bound by the provided bandwidth. With a limited GPU memory capacity, programmers should handle the data movement between GPU and host memories by themselves, causing a significant programming burden. To improve programming ease, GPUs use a unified address space with the host that allows over-subscribing GPU memory, but this approach is not effective in terms of performance once GPUs encounter memory page faults. Many recent works have tried to remedy capacity and bandwidth bottlenecks using dense non-volatile memories (NVMs) and true-3D stacking. However, these works mainly focus on one bottleneck or do not provide a scalable solution that fits future requirements. In this paper, we investigate true-3D stacking of dense, low-power, and refresh-free non-volatile phase change memory (PCM) on top of state-of-the-art GPU configurations to provide higher capacity and bandwidth within the available area and power budget. The higher density and lower power consumption of PCM provide higher capacity through integrating more cells in each 3D layer and enabling stacking more layers. However, we observe that stacking more than six layers of pure-PCM memory violates the thermal constraint and severely harms the performance and power efficiency due to its higher write latency and energy. Further, it degrades the lifetime of GPU to less than one year. Utilizing a hybrid architecture that leverages the benefits of both DRAM and PCM memories has been widely studied by prior proposals; however, true-3D integration of such a hybrid memory architecture especially on top of state-of-the-art powerful GPU architecture has not been investigated yet. We experimentally demonstrate that by covering 80% of write requests in DRAM and eliminating refresh overhead, true-3D stacking of eight 32GB layers of PCM along with two 8GB layers of DRAM is possible resulting in a total of 272GB memory capacity. Based on the explored design requirements, We propose a 3D high-bandwidth high-capacity hybrid memory (H3DM) system utilizing a hybrid-3D (H3D)-aware remapping scheme to reduce expensive PCM writes to under 20% while avoiding DRAM refresh overhead. H3DM improves the performance up to 291% compared to the baseline GPU architecture while remaining within only 3% of an ideal case with DRAM-like access latency, on average. Moreover, by increasing the dataset size above the baseline GPU memory space, H3DM improves performance and power up to 648% and 87% compared to the baseline GPU architecture since it avoids expensive data transfers through off-chip communication links.

Conservation of a Spectral Asymmetry Invariant in Optical Fiber Four‐Wave Mixing

AbstractThe conservation of spectral asymmetry is a fundamental feature of the ideal four‐wave mixing process as it exists in a medium combining quadratic chromatic dispersion and third‐order nonlinearity. The robustness of this invariant in an experimental configuration where the excitation conditions of an optical fiber are sequentially updated, mimicking infinite propagation is tested in this paper. This theoretical and experimental study reveals the high sensitivity of the asymmetry to very slight deviations from the ideal case, and the idealized system behaves as an intermediate case between the ideal case of noncascaded four‐wave mixing and propagation in a system governed by the nonlinear Schrödinger equation is shown.

The Statistical Mechanics of Ideal Magnetohydrodynamic Turbulence and a Solution of the Dynamo Problem

We review and extend the theory of ideal, homogeneous, incompressible, magnetohydrodynamic (MHD) turbulence. The theory contains a solution to the ‘dynamo problem’, i.e., the problem of determining how a planetary or stellar body produces a global dipole magnetic field. We extend the theory to the case of ideal MHD turbulence with a mean magnetic field that is aligned with a rotation axis. The existing theory is also extended by developing the thermodynamics of ideal MHD turbulence based on entropy. A mathematical model is created by Fourier transforming the MHD equations and dynamical variables, resulting in a dynamical system consisting of the independent Fourier coefficients of the velocity and magnetic fields. This dynamical system has a large but finite-dimensional phase space in which the phase flow is divergenceless in the ideal case. There may be several constants of the motion, in addition to energy, which depend on the presence, or lack thereof, of a mean magnetic field or system rotation or both imposed on the magnetofluid; this leads to five different cases of MHD turbulence that must be considered. The constants of the motion (ideal invariants)—the most important being energy and magnetic helicity—are used to construct canonical probability densities and partition functions that enable ensemble predictions to be made. These predictions are compared with time averages from numerical simulations to test whether or not the system is ergodic. In the cases most pertinent to planets and stars, nonergodicity is observed at the largest length-scales and occurs when the components of the dipole field become quasi-stationary and dipole energy is directly proportional to magnetic helicity. This nonergodicity is evident in the thermodynamics, while dipole alignment with a rotation axis may be seen as the result of dynamical symmetry breaking, i.e., ‘broken ergodicity’. The relevance of ideal theoretical results to real (forced, dissipative) MHD turbulence is shown through numerical simulation. Again, an important result is a statistical solution of the ‘dynamo problem’.

Regional and fine-scale local adaptation in salinity tolerance in Daphnia inhabiting contrasting clusters of inland saline waters.

Understanding the spatial scales at which organisms can adapt to strong natural and human-induced environmental gradients is important. Salinization is a key threat to biodiversity, ecosystem functioning and the provision of ecosystem services of freshwater systems. Clusters of naturally saline habitats represent ideal test cases to study the extent and scale of local adaptation to salinization. We studied local adaptation of the water flea Daphnia magna, a key component of pond food webs, to salinity in two contrasting landscapes-a dense cluster of sodic bomb crater ponds and a larger-scale cluster of soda pans. We show regional differentiation in salinity tolerance reflecting the higher salinity levels of soda pans versus bomb crater ponds. We found local adaptation to differences in salinity levels at the scale of tens of metres among bomb crater pond populations but not among geographically more distant soda pan populations. More saline bomb crater ponds showed an upward shift of the minimum salt tolerance observed across clones and a consequent gradual loss of less tolerant clones in a nested pattern. Our results show evolutionary adaptation to salinity gradients at different spatial scales, including fine-tuned local adaptation in neighbouring habitat patches in a natural landscape.

Non-Invasive Survey Techniques to Study Nuragic Archaeological Sites: The Nanni Arrù Case Study (Sardinia, Italy)

The Italian territory of Sardinia Island has an enormous cultural and identity heritage from the Pre-Nuragic and Nuragic periods, with archaeological evidence of more than 7000 sites. However, many other undiscovered remnants of these ancient times are believed to be present. In this context, it can be helpful to analyze data from different types of sensors on a single information technology platform, to better identify and perimeter hidden archaeological structures. The main objective of the study is to define a methodology that through the processing, analysis, and comparison of data obtained using different non-invasive survey techniques could help to identify and document archaeological sites not yet or only partially investigated. The non-invasive techniques include satellite, unmanned aerial vehicle, and geophysical surveys that have been applied at the nuraghe Nanni Arrù, one of the most important finds in recent times. The complexity of this ancient megalithic edifice and its surroundings represents an ideal use case. The surveys showed some anomalies in the areas south–east and north–east of the excavated portion of the Nanni Arrù site. The comparison between data obtained with the different survey techniques used in the study suggests that in areas where anomalies have been confirmed by multiple data types, buried structures may be present. To confirm this hypothesis, further studies are believed necessary, for example, additional geophysical surveys in the excavated part of the site.

Critical insecurities? The European Union’s strategy for a stable supply of minerals

Abstract The green transition is creating exponential needs for critical minerals. As demand currently exceeds supply for many of these minerals, governments worldwide are devising strategies to secure stable procurements of the minerals they lack as well as to ensure that they reap the benefits of their own natural wealth. The European Union (EU) is in a difficult position in this regard due to limited domestic reserves and, thus, serves as an ideal case study to explore developments in international economic law on this issue. Internally, the EU has been reforming its financing mechanisms, developing risk management tools, and enhancing recycling. Externally, the EU has been using international trade tools to focus on access to supply rather than traditional market access issues while also developing new investment rules to open doors and protect its foreign investors in extractive industries. Both internal and external actions are being coordinated with allies through new cooperative mechanisms. However, challenges abound. These strategies will face resistance from resource-abundant economies in view of their growing demand for policy space to protect their own mineral sector. Such strategies are also inherently competitive and, hence, might lead to growing tensions among resource-seeking economies themselves.

Optimization of Component Assembly in Automotive Industry

Abstract This article is devoted to the positioning of glued parts by robots in the process of manufacturing automotive headlights, with the possibility of generalization to the mutual positioning of any 3D object. The authors focused on the description of the mathematical method that leads to the optimization of the robot arm setting and ensures the closest contact of the glued parts. The contact surfaces of the two joined parts are, in the ideal case, identical in shape and their optimal alignment is considered to best align the position of the nominal points on the base part with the position of the control (measured) points on the part manipulated by the robot.

The Failure of Silicon Valley Bank and the Panic of 2023

The failure of Silicon Valley Bank on March 10, 2023 brought attention to significant weaknesses across the banking system, leading to a panic that spread to other vulnerable banks. With subsequent failures of Signature Bank and First Republic Bank, the United States had three of the four largest bank failures in its history occur over a two-month period. Several features of the Silicon Valley Bank failure make it an ideal teaching case for explaining the underlying economics of banking (in general) and banking crises (specifically). This paper tries to do that.

Taming assembly bias for primordial non-Gaussianity

Primordial non-Gaussianity of the local type induces a strong scale-dependent bias on the clustering of halos in the late-time Universe. This signature is particularly promising to provide constraints on the non-Gaussianity parameter f NL from galaxy surveys, as the bias amplitude grows with scale and becomes important on large, linear scales. However, there is a well-known degeneracy between the real prize, the f NL parameter, and the (non-Gaussian) assembly bias i.e., the halo formation history-dependent contribution to the amplitude of the signal, which could seriously compromise the ability of large-scale structure surveys to constrain f NL. We show how the assembly bias can be modeled and constrained, thus almost completely recovering the power of galaxy surveys to competitively constrain primordial non-Gaussianity. In particular, studying hydrodynamical simulations, we find that a proxy for the halo properties that determine assembly bias can be constructed from photometric properties of galaxies. Using a prior on the assembly bias guided by this proxy degrades the statistical errors on f NL only mildly compared to an ideal case where the assembly bias is perfectly known. The systematic error on f NL that the proxy induces can be safely kept under control.

Comparing the performance potential of speckle contrast optical spectroscopy and diffuse correlation spectroscopy for cerebral blood flow monitoring using Monte Carlo simulations in realistic head geometries.

The non-invasive measurement of cerebral blood flow based on diffuse optical techniques has seen increased interest as a research tool for cerebral perfusion monitoring in critical care and functional brain imaging. Diffuse correlation spectroscopy (DCS) and speckle contrast optical spectroscopy (SCOS) are two such techniques that measure complementary aspects of the fluctuating intensity signal, with DCS quantifying the temporal fluctuations of the signal and SCOS quantifying the spatial blurring of a speckle pattern. With the increasing interest in the use of these techniques, a thorough comparison would inform new adopters of the benefits of each technique. We systematically evaluate the performance of DCS and SCOS for the measurement of cerebral blood flow. Monte Carlo simulations of dynamic light scattering in an MRI-derived head model were performed. For both DCS and SCOS, estimates of sensitivity to cerebral blood flow changes, coefficient of variation of the measured blood flow, and the contrast-to-noise ratio of the measurement to the cerebral perfusion signal were calculated. By varying complementary aspects of data collection between the two methods, we investigated the performance benefits of different measurement strategies, including altering the number of modes per optical detector, the integration time/fitting time of the speckle measurement, and the laser source delivery strategy. Through comparison across these metrics with simulated detectors having realistic noise properties, we determine several guiding principles for the optimization of these techniques and report the performance comparison between the two over a range of measurement properties and tissue geometries. We find that SCOS outperforms DCS in terms of contrast-to-noise ratio for the cerebral blood flow signal in the ideal case simulated here but note that SCOS requires careful experimental calibrations to ensure accurate measurements of cerebral blood flow. We provide design principles by which to evaluate the development of DCS and SCOS systems for their use in the measurement of cerebral blood flow.

The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case

Profile measurements play a crucial role in operational weather forecasting across diverse scales and latitudes. However, assimilating tropospheric wind and temperature profiles remains a challenging endeavor. This study assesses the influence of profile measurements on numerical weather prediction (NWP) using the weather research and forecasting (WRF) model coupled to the parallel data assimilation framework (PDAF) system. Utilizing the local error-subspace transform Kalman filter (LESTKF), observational temperature and wind profiles generated by WRF are assimilated into an idealized tropical cyclone. The coupled WRF-PDAF system is adopted to carry out the twin experiments, which employ varying profile densities and localization distances. The results reveal that high-resolution observations yield significant forecast improvements compared to coarser-resolution data. A cost-effective balance between observation density and benefit is further explored through the idealized tropical cyclone case. According to diminishing marginal utility and increasing marginal costs, the optimal observation densities for U and V are found around 26–27%. This may be useful information to the meteorological agencies and researchers.