Alternative Objective Research Articles

Algorithmic Risk Assessment in the Hands of Humans

We evaluate the impacts of adopting algorithmic risk assessments in sentencing. We find that judges changed sentencing practices in response to the risk assessment, but that discretion played a large role in mediating its impact. Judges deviated from the recommendations associated with the algorithm in systematic ways, suggestive of alternative objectives. As a result, risk assessment did not lead to detectable gains in terms of public safety or reduced incarceration rates. Using simulations, we show that strict adherence to the sentencing recommendations associated with the algorithm would have had benefits (less incarceration) but also some costs (increased sentences for youth). (JEL D81, D91, H76, K41, K42)

EXPRESS: Distributionally Robust Appointment Scheduling That Can Deal with Independent Service Times

Consider a single server that should serve a given number of customers during a fixed period. The Appointment Scheduling Problem (ASP) determines the schedule of planned appointments that minimizes some cost function that accounts for both the cost of idle times and the cost of waiting. When service time distributions are fully specified, the ASP presents a much investigated computationally challenging stochastic program. When service time distributions are only partially specified, one can apply distributionally robust optimization (DRO) to find the schedule that minimizes costs in worst-case circumstances. We assume that only the mean, mean absolute deviation and range of the service times are known, and develop a DRO method that finds the optimal (minimax) schedule. For independent service times, the minmax problem becomes nonlinear and difficult, if not impossible, to solve exactly. Existing DRO methods for ASP with partial information (such as mean and variance) therefore consider relaxations that allow correlations between service times. Such relaxations have major repercussions, as the worst-case scenario will then be highly correlated. Our method thus deals with independent service times and finds the robust schedule as the solution to a linear program. We identify several new structural features of optimal robust schedules. We also apply the method to model extensions including sequencing and alternative objective functions.

“Salt preserves”: A curriculum of salt in The Autobiography of Mary Prince

In the US curriculum, The History of Mary Prince (Prince, 1831) is an under-recognized account of Black enslavement and the salt industry in the 19th century. Mary Prince, a Black enslaved woman and salt laborer, is the author of the earliest known anti-slavery, anti-colonial autobiography written by a self-manumitted Black woman. Bermuda, the place of Prince’s story, serves as an alternative to the American continent as a center of Black enslavement and salt as an alternative object to the sugar and cotton of the curricular imagination in studies of Western colonization. With salt as its organizing material, this article responds to the call from Christina Sharpe’s (2016) In the Wake to foreground Prince’s autobiography with parts of my own autobiography, in which salt serves as an extension of, and an addition to, Sharpe’s notion of “wake work.” Thinking of curriculum as the study of educational experience (Pinar, 2004/2012) and using Prince’s narrative as a primal scene, I examine Prince’s consciousness of salt through wake-fullness. This enables me to explore saltings as told from the coordinate middle of the Black Atlantic and to uncover a postcolonial theorization of coloniality, its knowledge, and racial capitalism through the oedemic, excoriating, and extimate workings of salt on the Black body.

Exploring the relationship between color and taste: Unveiling critical color and taste compounds of Qingxiang Tieguanyin tea infusion

The color and taste of tea infusion play crucial roles in assessing tea quality. While color can be easily quantified, taste perception is subjective in nature. Can the color of tea infusion directly reflect its taste? In this study, spectroscopy and metabolomics techniques combined with machine learning algorithms, were used to identify the key compounds that responsible for both color and taste in Qingxiang Tieguanyin (QXTGY) tea infusion. The results showed strong correlations between almost all of 46 differential compounds and the taste profile of the tea infusion. Notably, quercetin 3-O-glucosyl rutinoside, rutin, and quercetin 3-O-glucoside were found to establish a connection between the color and taste of QXTGY tea infusion. Specifically, within a certain range, these compounds promoted a darker color (deepening the yellowness) in the tea infusion while simultaneously reducing its umami and brisk taste. This indicates that the color of tea infusion can reflect its taste to some extent. Furthermore, quercetin 3-O-glucosyl rutinoside had the most significant impact on the color of the tea infusion. The findings of this study offer an alternative objective approach to evaluate the taste of tea infusion by objectively determining its color.

Exploring VAE-driven implicit parametric unit cells for multiscale topology optimization

The simulation and optimization of metamaterial, known for their engineered properties in various applications, encounter intricate challenges due to complex microstructure interactions, extensive design spaces, and substantial computational requirements. To address these challenges, our research introduces a novel data-driven framework utilizing deep generative modeling to enhance the design process of metamaterials.Focusing on composite metamaterial with double negative coefficients of thermal expansion and Poisson's ratio, we apply an Alternative Active Phase and Objective functions (AAPO) method for deciding the initial metamaterial dataset. To enhance dataset diversity, a distortion filter is applied, broadening the range of design possibilities. Subsequently, we utilize a Variational Autoencoder (VAE), integrated with a regressor, to train on this diversified database. This training effectively maps complex unit cell geometries to a coherent latent space, simultaneously correlating them with continuous material properties.Our approach demonstrates robustness in multi-phase and multi-physics optimization as well as efficiency in generating specialized databases of unit cells. This framework is pivotal in systematically designing unit cells and multiscale systems, specifically aiming for distinct thermo-mechanical behavior targets.To mitigate the computational demands encountered during multiple design meta-materials via gradient-based topology optimization, we have integrated high-performance methods and automatic differentiation. This integration marks a significant advancement in the data-driven design of metamaterials, offering substantial practical and theoretical benefits in the field.

Alternative icons: rethinking symbols of power in ‘The World of Stonehenge’

Current debates surrounding decolonisation and the democratisation of display are a critical issue for prehistoric collections as well as more recent material. The objects most likely to symbolise prehistory in museum displays, and thus in the popular imagination—those made of precious, skilfully worked materials—are a restricted group of iconic things, often interpreted as reflective of social status rather than anything more personal or spiritual. To contextualise this debate, the authors outline public reaction to the display of alternative objects with more representative messages within The World of Stonehenge exhibition, which was held at the British Museum in 2022.

A theory of multivariate stress testing

We present a theoretical framework for stressing multivariate stochastic models. We consider a stress to be a change of measure, placing a higher weight on multivariate scenarios of interest. In particular, a stressing mechanism is a mapping from random vectors to Radon–Nikodym densities. We postulate desirable properties for stressing mechanisms addressing alternative objectives. Consistently with our focus on dependence, we require throughout invariance to monotonic transformations of risk factors. We study in detail the properties of two families of stressing mechanisms, based respectively on mixtures of univariate stresses and on transformations of statistics we call Spearman and Kendall’s cores. Furthermore, we characterize the aggregation properties of those stressing mechanisms, which motivate their use in deriving new capital allocation methods, with properties different to those typically found in the literature. The proposed methods are applied to stress testing and capital allocation, using the simulation model of a UK-based non-life insurer.

An adaptable scheduling model for mixed flexible flow line with production continuity and processing capacity

In industrial production, in order to maximize demand fulfillment with limited processing capacity, manufacturers introduce a mixed-model flexible flow line (MFFL), which allows concurrent processing of multiple SKUs and produces more products in both quantity and category. In this study, we develop an adaptive mixed-integer linear programming (MILP) scheduling model for the MFFL problem. The model captures two key constraints in industrial production: production continuity and process capability. For the MFFL problem, we also propose a five-level scheduling strategy that integrates on-time and in-full (OTIF), early production, late delivery, using safety stock, and finally cutting demand. The scheduling strategy and model are demonstrated by real cases with high efficiency. Interestingly, the proposed scheduling strategy and model also achieve machine load balancing in production. Furthermore, we explore the impact of production continuity and introduce alternative objective functions to reduce overproduction with acceptable increases in solving time. This study is helpful for manufacturers to satisfy the demand as much as possible under the constraint of processing capacity and the requirement of production continuity.

Mental Contrasting Strategies Promote the Pursuit of Difficult Goals: Japanese Cultural Context.

We examined whether mental contrasting inhibits the pursuit of difficult goals in an Eastern culture-Japan-rooted in self-improvement. Our pilot study found that, compared with American participants, Japanese participants did not perceive a difficult situation as a cue to abandon their goal and pursue alternative objectives. Studies 1a-1c found that mental contrasting encouraged Japanese participants to pursue difficult goals. When Japanese participants perceived their own goals as unattainable, they were more likely to pursue these goals if they mentally contrasted their desired future with the inhibiting reality than if they simply imagined their desired future. Study 2 showed that mental contrasting encouraged Japanese (but not American) participants to pursue difficult goals. Study 3 evidenced the causal effect of beliefs about difficulties on the impact of mental contrasting on motivation to pursue difficult goals. Culturally formed beliefs about difficulties underlie the effect of mental contrasting on difficult goal pursuit.

Dorsal brain activity reflects the severity of menopausal symptoms.

The severity of menopausal symptoms, despite being triggered by hormonal imbalance, does not directly correspond to hormone levels in the blood; thus, the level of unpleasantness is assessed using subjective questionnaires in clinical practice. To provide better treatments, alternative objective assessments have been anticipated to support medical interviews and subjective assessments. This study aimed to develop a new objective measurement for assessing unpleasantness. Fourteen participants with menopausal symptoms and two age-matched participants who visited our outpatient section were enrolled. Resting-state brain activity was measured using magnetoencephalography. The level of unpleasantness of menopausal symptoms was measured using the Kupperman Kohnenki Shogai Index. The blood level of follicle-stimulating hormone and luteinizing hormone were also measured. Correlation analyses were performed between the oscillatory power of brain activity, index score, and hormone levels. The level of unpleasantness of menopausal symptoms was positively correlated with high-frequency oscillatory powers in the parietal and bordering cortices (alpha; P = 0.016, beta; P = 0.015, low gamma; P = 0.010). The follicle-stimulating hormone blood level was correlated with high-frequency oscillatory powers in the dorsal part of the cortex (beta; P = 0.008, beta; P = 0.005, low gamma; P = 0.017), whereas luteinizing hormone blood level was not correlated. Resting-state brain activity can serve as an objective measurement of unpleasantness associated with menopausal symptoms, which aids the selection of appropriate treatment and monitors its outcome.

Identifying, Mitigating, and Anticipating Bias in Algorithmic Decisions

Today's machine learning (ML) applications predominantly adhere to a standard paradigm: the decision maker designs the algorithm by optimizing a model for some objective function. While this has proven to be a powerful approach in many domains, it comes with inherent side effects: the power over the algorithmic outcomes lies solely in the hands of the algorithm designer, and alternative objectives, such as fairness, are often disregarded. This is particularly problematic if the algorithm is used to make consequential decisions that affect peoples lives. My research focuses on developing principled methods to characterize and address the mismatch between these different objectives.

Italy for Sale: Alternative objects – alternative markets

Italy for Sale: Alternative objects – alternative markets

Multi-objective assessment of hydrological model performances using Nash–Sutcliffe and Kling–Gupta efficiencies on a worldwide large sample of watersheds

We introduce a new diagnosis tool that is well suited to analyzing simulation results over large samples of watersheds. It consists of a modification of the classical Taylor diagram to simultaneously visualize several error components (based on bias, standard deviation or squared errors) that are commonly used in efficiency criteria (such as the Nash–Sutcliffe efficiency (NSE) or the Kling–Gupta efficiency (KGE)) to evaluate hydrological model performance. We propose a methodological framework that explicitly links the graphical and numerical evaluation approaches, and show how they can be usefully combined to visually interpret numerical experiments conducted on large datasets. The approach is illustrated using results obtained by testing two rainfall-runoff models on a sample of 2050 watersheds from 8 countries and calibrated with two alternative objective functions (NSE and KGE). The assessment tool clearly highlights well-documented problems related to the use of the NSE for the calibration of rainfall-runoff models, which arise due to interactions between the ratio of simulated to observed standard deviations and the correlation coefficient. We also illustrate the negative impacts of classical mathematical transformations (square root) applied to streamflow when employing NSE and KGE as metrics for model calibration.

Exploration of study objects on the potential of wild mushrooms growing on oil palm empty bunches for learning in the subject of IPAS project at vocational school

The IPAS Project has a study object in the form of concrete objects and social phenomena found in nature, and it is developed based on empirical experience. This study aimed to find out various alternative study objects regarding the potential of wild mushrooms growing on oil palm empty fruit bunches (OPEFBs) piled at oil palm plantations. The research was descriptive exploratory research. Data were collected through field study, documentation, and literature study. The collected data were analyzed by using thematic analysis from Philip Marrying. The result of the field study found 12 types of wild mushrooms growing on the decayed OPEFBs. Those mushrooms were then identified based on their morphological characteristics. In this study, we found three alternative study objects towards mushrooms growing on OPEFB, which can be developed in the IPAS Project learning: (1) mushroom cultivation using OPEFB waste which includes 4 aspects (living things and their environment, substance, and their changes, earth and space, economic behavior and welfare), (2) morphological characterization for classification of mushroom including 3 aspects (living things and their environment, substance and their changes, social interaction and social dynamics), and (3) detection of chemical compounds present in mushroom, covering 3 aspects (living things and their environment, substance and their changes, economic behavior and welfare).

Black literature in the English language classroom: teaching English and building the individual as a critical and sensitive social being with the short story “Girl” by Jamaica Kincaid

For some time in traditional foreign language teaching, literature occupied the place of teaching object used in the translation method. With the changes in teaching methodology, the literary text has lost its importance in English classrooms. However, literature is resurging and asking for space in this context in conformity with the official documents of education that attribute to the teaching of foreign languages the development of knowledge for the exercise of citizenship. This research aims to analyze the use of literary texts in the English language classroom that through this literacy process facilitates the construction of the student's formative character, collaborating in the reflection about his social practices and actions as a critical and sensitive individual. The short story "Girl" by the black writer Jamaica Kincaid is presented as an alternative object of study, bringing attention to relevant discussions of post-modernity, such as the colonization process, which is responsible for a great part of the identity formation of several peoples. Based on the assumptions of literature as a "humanizing" text (CANDIDO, 2012), transgressive (COSSON, 2016; HOOKS, 2017), and transformative (GONÇALVES; SPECHT, 2018) the research highlights linguistic, cultural, social, and spiritual learning factors of literary texts, contributing to the formation of a society based on equity, solidarity, and respect.

The Missing Alternative Objection to Criminal Law Abolitionism

Criminal law abolitionists claim that legal punishment cannot be morally justified and that we should therefore abolish criminal law. While this is still a minority position in the current debate, the number of proponents has been increasing, and even opponents have developed a certain degree of sympathy for such claims in recent years. Yet one of the reasons many remain hesitant regarding the abolition of criminal law appears to be the lack of a thought-through alternative, in addition to abolitionists disagreeing considerably amongst themselves on what an alternative should look like. I will call this the missing alternative objection. To address this central concern, I will argue in this paper that the most prominent versions of abolitionism actually converge on the same alternative core to criminal law — even though they are driven by vastly different motivations. This core that current abolitionist theories converge on is two-fold: first, the claim that the state should compel offenders to provide restitution for the victim; second, the claim that restorative processes should be used wherever possible when addressing criminal wrongdoing. This common core is enough to reject the missing alternative objection.

Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review.

Depression is one of the most common mental disorders that affects >300 million people worldwide. There is a shortage of providers trained in the provision of mental health care, and the nursing workforce is essential in filling this gap. The diagnosis of depression relies heavily on self-reported symptoms and clinical interviews, which are subject to implicit biases. The omics methods, including genomics, transcriptomics, epigenomics, and microbiomics, are novel methods for identifying the biological underpinnings of depression. Machine learning is used to analyze genomic data that includes large, heterogeneous, and multidimensional data sets. This scoping review aims to review the existing literature on machine learning methods for omics data analysis to identify individuals with depression, with the goal of providing insight into alternative objective and driven insights into the diagnostic process for depression. This scoping review was reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Searches were conducted in 3 databases to identify relevant publications. A total of 3 independent researchers performed screening, and discrepancies were resolved by consensus. Critical appraisal was performed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies. The screening process identified 15 relevant papers. The omics methods included genomics, transcriptomics, epigenomics, multiomics, and microbiomics, and machine learning methods included random forest, support vector machine, k-nearest neighbor, and artificial neural network. The findings of this scoping review indicate that the omics methods had similar performance in identifying omics variants associated with depression. All machine learning methods performed well based on their performance metrics. When variants in omics data are associated with an increased risk of depression, the important next step is for clinicians, especially nurses, to assess individuals for symptoms of depression and provide a diagnosis and any necessary treatment.

Optimizing Freight Vehicle Routing in Dynamic Time-Varying Networks with Carbon Dioxide Emission Trajectory Analysis

In this study, we formulate a freight vehicle path-planning model in the context of dynamic time-varying networks that aims to capture the spatial and temporal distribution characteristics inherent in the carbon dioxide emission trajectories of freight vehicles. Central to this model is the minimization of total carbon dioxide emissions from vehicle distribution, based on the comprehensive modal emission model (CMEM). Our model also employs the freight vehicle travel time discretization technique and the dynamic time-varying multi-path selection strategy. We then design an improved genetic algorithm to solve this complicated problem. Empirical results vividly illustrate the superior performance of our model over alternative objective function models. In addition, our observations highlight the central role of accurate period partitioning in time segmentation considerations. Finally, the experimental results underline that our multi-path model is able to detect the imprint of holiday-related effects on the spatial and temporal distribution of carbon dioxide emission trajectories, especially when compared to traditional single-path models.

Diagnosis of Ulnar Neuropathy at the Elbow Using Ultrasound — A Comparison to Electrophysiologic Studies

Given the relatively high false negative rate of electrodiagnostic studies (EDX) in patients with clinically diagnosed ulnar neuropathy at the elbow (UNE), we sought to determine whether an alternative objective test could more effectively detect UNE. Additionally, we proposed to determine the relationship between the cross-sectional area (CSA) of the ulnar nerve on ultrasound(US), EDX, and clinical symptoms. This was a retrospective study of patients presenting with symptomatic UNE. The performance characteristics of EDX versus ultrasound were calculated using the clinical diagnosis of UNE as the reference standard. Standard EDX studies and US of the ulnar nerve were analyzed. Maximal CSA of the ulnar nerve and EDX severity were analyzed for patients with each combination of US-positive/negative and EDX-positive/negative findings. Analysis was performed on 89 patients and 115 nerves with signs and symptoms of cubital tunnel syndrome. In total, 56 (49%) nerves were diagnosed as mild UNE, 32 (28%) nerves were diagnosed as moderate UNE, 17 (15%) nerves were diagnosed as severe UNE, and 10 (8%) nerves were negative for UNE by EDX. Maximal-maximal CSA was highly correlated with disease severity as determined by nerve conduction studies/electromyography. Compared with EDX+/US+, patients with EDX-/US+ showed higher rates of ulnar sensory loss and elbow tenderness with similar rates of positive Tinel and intrinsic muscle atrophy. In this sample of patients with clinically diagnosed UNE, 91.3% of the patients demonstrated positive EDX studies, whereas 94.8% had a positive US. Ultrasound is an alternative to EDX that could be incorporated clinically in the diagnosis and management of UNE. Ultrasound was able to consistently detect clinically positive cubital tunnel syndrome demonstrating its utility as a confirmatory or supplemental test to the clinical assessment if one is required. Ultrasound additionally may be able to better identify patients with early stages of UNE with negative EDX findings. Diagnostic IV.

A Deep Learning Approach for Precision Viticulture, Assessing Grape Maturity via YOLOv7.

In the viticulture sector, robots are being employed more frequently to increase productivity and accuracy in operations such as vineyard mapping, pruning, and harvesting, especially in locations where human labor is in short supply or expensive. This paper presents the development of an algorithm for grape maturity estimation in the framework of vineyard management. An object detection algorithm is proposed based on You Only Look Once (YOLO) v7 and its extensions in order to detect grape maturity in a white variety of grape (Assyrtiko grape variety). The proposed algorithm was trained using images received over a period of six weeks from grapevines in Drama, Greece. Tests on high-quality images have demonstrated that the detection of five grape maturity stages is possible. Furthermore, the proposed approach has been compared against alternative object detection algorithms. The results showed that YOLO v7 outperforms other architectures both in precision and accuracy. This work paves the way for the development of an autonomous robot for grapevine management.