Peruvian University of Applied Sciences

Soil microbial responses to long-term agricultural practices under no-till: Insights from depth-resolved sampling of shallow soil layers

Abstract Fungal and bacterial communities were characterized at three soil depths (0–3.5 cm, 3.5–7.5 cm, and 7.5–15 cm) in a 15-year no-till field experiment involving diversified crop rotations with two levels of nitrogen fertilization and soybean monoculture. We hypothesized th...

Synthesis, DFT studies, and molecular docking of novel pyrazole semicarbazones targeting GPb, PPA-II, and DPP-4 enzymes

Abstract Six new phenyl-1 H -pyrazole-carbaldehyde semicarbazone derivatives ( 1 – 6 ) have been synthesized and characterized by FT-IR, NMR ( 1 H and 13 C), and mass spectrometry. NMR analysis indicated that these compounds in DMSO-d 6 exhibit a cisE isomeric form. This evidence...

Psychosocial and behavioral factors associated with temporomandibular disorder symptoms in university athletes: a cross-sectional study

Abstract Background Temporomandibular disorder symptoms are common in young adults and may be influenced by psychosocial and behavioral factors. This study estimated the prevalence of self-reported TMD symptoms and examined associated factors among university athletes in Lima, Pe...

The Probability Changes With the Model: What Density Estimation Actually Believes

Probability models are often described as if they reveal how likely observations truly are. In practice, a density is conditional on a representation, model family, complexity, bandwidth, covariance rule, reference population, and measure. Even the numerical value of a continuous...

The Probability Changes With the Model: What Density Estimation Actually Believes

Probability models are often described as if they reveal how likely observations truly are. In practice, a density is conditional on a representation, model family, complexity, bandwidth, covariance rule, reference population, and measure. Even the numerical value of a continuous...

The Outlier Changes With the Detector: What Anomaly Detection Actually Flags

Anomaly detection often converts an unlabeled score into a factual-sounding label: normal or anomalous. However, that label is conditional on the reference population, representation, detector family, neighborhood or kernel scale, and the rule used to convert continuous scores in...

There Are No Patterns Before a Representation: What Unsupervised Learning Actually Sees

Unsupervised learning is often described as discovering hidden structure in unlabeled data. That description omits the analytical object that makes discovery possible: a representation, a scale, a dissimilarity rule, and a neighborhood definition. Before an algorithm can identify...

There Are No Patterns Before a Representation: What Unsupervised Learning Actually Sees

Unsupervised learning is often described as discovering hidden structure in unlabeled data. That description omits the analytical object that makes discovery possible: a representation, a scale, a dissimilarity rule, and a neighborhood definition. Before an algorithm can identify...

The Curve Is an Average of Worlds: What Partial Dependence Actually Shows

Partial dependence plots are often interpreted as direct response curves: set a feature to a value, average the predictions, and read the resulting line as an explanation. This paper evaluates a narrower object. A marginal partial-dependence curve averages a frozen predictive mod...

The Curve Is an Average of Worlds: What Partial Dependence Actually Shows

Partial dependence plots are often interpreted as direct response curves: set a feature to a value, average the predictions, and read the resulting line as an explanation. This paper evaluates a narrower object. A marginal partial-dependence curve averages a frozen predictive mod...

Each Tree Corrects the Last: What Gradient Boosting Actually Learns

Gradient boosting is commonly described as a sequence of weak trees in which each new learner corrects errors left by the current ensemble. This description is computationally accurate but scientifically incomplete. Every correction is conditioned on a declared loss function, an...

Each Tree Corrects the Last: What Gradient Boosting Actually Learns

Gradient boosting is commonly described as a sequence of weak trees in which each new learner corrects errors left by the current ensemble. This description is computationally accurate but scientifically incomplete. Every correction is conditioned on a declared loss function, an...

The Path to the Minimum Matters: What an Optimizer Actually Changes

An optimizer does not ordinarily redefine the statistical target encoded by a loss function, but it determines which solution is reached, how closely it is approached, how stable that approach is, and how much computational work is required. Therefore, a valid objective does not...

The Metric Chooses the Winner: What Model Evaluation Actually Rewards

A model is not inherently “best” independently of the rule used to evaluate it. Evaluation metrics determine which errors receive greater weight, which properties of a prediction system become visible, and which model appears superior. Consequently, model selection is incomplete...

The Metric Chooses the Winner: What Model Evaluation Actually Rewards

A model is not inherently “best” independently of the rule used to evaluate it. Evaluation metrics determine which errors receive greater weight, which properties of a prediction system become visible, and which model appears superior. Consequently, model selection is incomplete...

The Path to the Minimum Matters: What an Optimizer Actually Changes

An optimizer does not ordinarily redefine the statistical target encoded by a loss function, but it determines which solution is reached, how closely it is approached, how stable that approach is, and how much computational work is required. Therefore, a valid objective does not...