Texas A&M University – Corpus Christi

Abstract What is a human right? How can we tell whether a proposed human right really is one? How do we establish the content of particular human rights, and how do we resolve conflicts between them? These are pressing questions for philosophers, political theorists, jurisprudents, international lawyers, and activists. This book offers answers in its investigation of human rights. The term ‘natural right’, in its modern sense of an entitlement that a person has, first appeared in the late Middle Ages. When during the 17th and 18th centuries the theological content of the idea was abandoned in stages, nothing was put in its place. The secularized notion that we were left with at the end of the Enlightenment is still our notion today: a right that we have simply in virtue of being human. During the 20th century, international law has contributed to settling the question of which rights are human rights, but its contribution has its limits. The notion of a human right that we have inherited suffers from no small indeterminateness of sense. The term has been left with so few criteria for determining when it is used correctly that we often have a plainly inadequate grasp on what is at issue. This book takes on the task of showing the way towards a determinate concept of human rights, based on their relation to the human status that we all share. The book works from certain paradigm cases, such as freedom of expression and freedom of worship, to more disputed cases such as welfare right — for instance the idea of a human right to health. The goal is a substantive account of human rights; an account with enough content to tell us whether proposed rights really are rights. The book emphasizes the practical as well as theoretical urgency of this goal: as the United Nations recognized in 1948 with its Universal Declaration, the idea of human rights has considerable power to improve the lot of humanity around the world.

This article empirically investigates the competitiveness and stability of family-owned firms relative to firms owned by diverse shareholders. Founding families are present in about one-third of the S&P 500—the sample of this study. Data gathered over the 1992—2002 period confirm that family firms tend to experience higher employment and revenue growth over time and are more profitable. Regression analysis also supports that firm performance improves when founding family members are involved in management. Although evidence on the relative stability in employment among family firms over the long run is tenuous, data from the most recent recession support the role that founding families play in maintaining employment stability during temporary market downturns.

Recent years have witnessed significant advancement in computer vision research based on deep learning. Success of these tasks largely depends on the availability of a large amount of training samples. Labeling the training samples is an expensive process. In this paper, we present a simulated deep convolutional neural network for yield estimation. Knowing the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits or flowers by workers is a very time consuming and expensive process and it is not practical for big fields. Automatic yield estimation based on robotic agriculture provides a viable solution in this regard. Our network is trained entirely on synthetic data and tested on real data. To capture features on multiple scales, we used a modified version of the Inception-ResNet architecture. Our algorithm counts efficiently even if fruits are under shadow, occluded by foliage, branches, or if there is some degree of overlap amongst fruits. Experimental results show a 91% average test accuracy on real images and 93% on synthetic images.

This paper presents an integrated conceptual model of supply chain flexibility. It examines flexibility classification schemes and the commonalities of flexibility typologies published in the literature to create a theoretical foundation for analyzing the components of supply chain flexibility. Even though there has been a tremendous amount of research on the topic of flexibility, most of it has been confined to intra‐firm flexibility concerns. As supply chain management goes beyond a firm’s boundaries, the flexibility strategies must also extend beyond the firm. This paper identifies the cross‐enterprise nature of supply chain flexibility and the need to improve flexibility measures across firms. Opportunities are identified for future cross‐functional research that builds on this theoretical foundation and leads to more effective formulation of supply chain strategies.

Restriction-site associated DNA sequencing (RADseq) has become a powerful and useful approach for population genomics. Currently, no software exists that utilizes both paired-end reads from RADseq data to efficiently produce population-informative variant calls, especially for non-model organisms with large effective population sizes and high levels of genetic polymorphism. dDocent is an analysis pipeline with a user-friendly, command-line interface designed to process individually barcoded RADseq data (with double cut sites) into informative SNPs/Indels for population-level analyses. The pipeline, written in BASH, uses data reduction techniques and other stand-alone software packages to perform quality trimming and adapter removal, de novo assembly of RAD loci, read mapping, SNP and Indel calling, and baseline data filtering. Double-digest RAD data from population pairings of three different marine fishes were used to compare dDocent with Stacks, the first generally available, widely used pipeline for analysis of RADseq data. dDocent consistently identified more SNPs shared across greater numbers of individuals and with higher levels of coverage. This is due to the fact that dDocent quality trims instead of filtering, incorporates both forward and reverse reads (including reads with INDEL polymorphisms) in assembly, mapping, and SNP calling. The pipeline and a comprehensive user guide can be found at http://dDocent.wordpress.com.

A comprehensive seafloor biomass and abundance database has been constructed from 24 oceanographic institutions worldwide within the Census of Marine Life (CoML) field projects. The machine-learning algorithm, Random Forests, was employed to model and predict seafloor standing stocks from surface primary production, water-column integrated and export particulate organic matter (POM), seafloor relief, and bottom water properties. The predictive models explain 63% to 88% of stock variance among the major size groups. Individual and composite maps of predicted global seafloor biomass and abundance are generated for bacteria, meiofauna, macrofauna, and megafauna (invertebrates and fishes). Patterns of benthic standing stocks were positive functions of surface primary production and delivery of the particulate organic carbon (POC) flux to the seafloor. At a regional scale, the census maps illustrate that integrated biomass is highest at the poles, on continental margins associated with coastal upwelling and with broad zones associated with equatorial divergence. Lowest values are consistently encountered on the central abyssal plains of major ocean basins The shift of biomass dominance groups with depth is shown to be affected by the decrease in average body size rather than abundance, presumably due to decrease in quantity and quality of food supply. This biomass census and associated maps are vital components of mechanistic deep-sea food web models and global carbon cycling, and as such provide fundamental information that can be incorporated into evidence-based management.

As an emerging service architecture, microservice enables decomposition of a monolithic web service into a set of independent lightweight services which can be executed independently. With mobile edge computing, microservices can be further deployed in edge clouds dynamically, launched quickly, and migrated across edge clouds easily, providing better services for users in proximity. However, the user mobility can result in frequent switch of nearby edge clouds, which increases the service delay when users move away from their serving edge clouds. To address this issue, this article investigates microservice coordination among edge clouds to enable seamless and real-time responses to service requests from mobile users. The objective of this work is to devise the optimal microservice coordination scheme which can reduce the overall service delay with low costs. To this end, we first propose a dynamic programming-based offline microservice coordination algorithm, that can achieve the globally optimal performance. However, the offline algorithm heavily relies on the availability of the prior information such as computation request arrivals, time-varying channel conditions and edge cloud's computation capabilities required, which is hard to be obtained. Therefore, we reformulate the microservice coordination problem using Markov decision process framework and then propose a reinforcement learning-based online microservice coordination algorithm to learn the optimal strategy. Theoretical analysis proves that the offline algorithm can find the optimal solution while the online algorithm can achieve near-optimal performance. Furthermore, based on two real-world datasets, i.e., the Telecom's base station dataset and Taxi Track dataset from Shanghai, experiments are conducted. The experimental results demonstrate that the proposed online algorithm outperforms existing algorithms in terms of service delay and migration costs, and the achieved performance is close to the optimal performance obtained by the offline algorithm.

Sequencing reduced-representation libraries of restriction site-associated DNA (RADseq) to identify single nucleotide polymorphisms (SNPs) is quickly becoming a standard methodology for molecular ecologists. Because of the scale of RADseq data sets, putative loci cannot be assessed individually, making the process of filtering noise and correctly identifying biologically meaningful signal more difficult. Artefacts introduced during library preparation and/or bioinformatic processing of SNP data can create patterns that are incorrectly interpreted as indicative of population structure or natural selection. Therefore, it is crucial to carefully consider types of errors that may be introduced during laboratory work and data processing, and how to minimize, detect and remove these errors. Here, we discuss issues inherent to RADseq methodologies that can result in artefacts during library preparation and locus reconstruction resulting in erroneous SNP calls and, ultimately, genotyping error. Further, we describe steps that can be implemented to create a rigorously filtered data set consisting of markers accurately representing independent loci and compare the effect of different combinations of filters on four RAD data sets. At last, we stress the importance of publishing raw sequence data along with final filtered data sets in addition to detailed documentation of filtering steps and quality control measures.

Modern agriculture and food production systems are facing increasing pressures from climate change, land and water availability, and, more recently, a pandemic. These factors are threatening the environmental and economic sustainability of current and future food supply systems. Scientific and technological innovations are needed more than ever to secure enough food for a fast-growing global population. Scientific advances have led to a better understanding of how various components of the agricultural system interact, from the cell to the field level. Despite incredible advances in genetic tools over the past few decades, our ability to accurately assess crop status in the field, at scale, has been severely lacking until recently. Thanks to recent advances in remote sensing and Artificial Intelligence (AI), we can now quantify field scale phenotypic information accurately and integrate the big data into predictive and prescriptive management tools. This review focuses on the use of recent technological advances in remote sensing and AI to improve the resilience of agricultural systems, and we will present a unique opportunity for the development of prescriptive tools needed to address the next decade's agricultural and human nutrition challenges.

Student evaluation of teaching (SET) is important to faculty because SET ratings help faculty improve performance and are often used as the basis for evaluations of teaching effectiveness in administrative decisions (e.g., tenure). Researchers have conducted over 2,000 studies on SET during the past 70 years. However, despite the explosive growth in online education during the past decade, researchers have largely neglected the use of SET to evaluate teaching effectiveness in online courses. This exploratory study analyzed the actual SET data collected during a single semester at a large mid-western college that offers over 250 online/Web-based classes. The data included five dependent and eighteen independent measures of teaching effectiveness. The results indicate that average SET ratings in online classes are significantly lower than the average ratings in on-campus classes across all five dependent measures. This finding offers preliminary empirical support for anecdotal evidence cited by earlier authors in this field. Furthermore, regression analysis of the full model for each dependent variable indicated that the independent variables explained a significant portion of the variance in SET ratings. Examination of the standardized beta coefficients revealed that the strength and significance of the independent variables varied across the five dependent measures. Findings also indicate that organization of the course materials had a strong impact on all five measures of overall teaching effectiveness. Other variables including clarity of the instructor’s writing, timeliness in providing feedback, and interest in whether students learned were also significant factors in models that measured instructor effectiveness (as opposed to models that measured quality of course content). The paper concludes with a discussion of the implications of this study for administrators, faculty, and researchers.

Abstract Wavelet analysis, although used extensively in disciplines such as signal processing, engineering, medical sciences, physics and astronomy, has not fully entered the economics discipline yet. In this survey article, wavelet analysis is introduced in an intuitive manner, and the existing economics and finance literature that utilizes wavelets is surveyed and explored. Extensive examples of exploratory wavelet analysis are given, most using Canadian, US and Finnish industrial production data. Finally, potential and possible future applications for wavelet analysis in economics are discussed.

A.4 Constraining the flux in the ND A.4.1 Neutrino-electron elastic scattering A.4.2 The low- method A.4.3 Coherent neutrino-nucleus scattering A.4.4 Beam e content A.5 Movable components of the ND and the DUNE-PRISM program A.5.1 Introduction to DUNE-PRISM A.5.2 LArTPC component in the DUNE ND: ArgonCube A.5.3 Multipurpose detector A.5.4 The DUNE-PRISM program A.6 Fixed on-axis component of the DUNE ND A.6.1 Motivation and introduction A.6.2 Three-dimensional projection scintillator tracker spectrometer A.7 Meeting the near detector requirements A.7.1 Overarching requirements A.7.2 Event rate and flux measurements A.7.3 Control of systematic errors B ND hall and construction C Computing roles and collaborative projects C.1 Roles C.2 Specific collaborative computing projects C.2.1 LArSoft for event reconstruction C.2.2 WLCG/OSG and the HEP Software Foundation C.2.3 Evaluations of other important infrastructure

The smart community (SC), as an important part of the Internet of Energy (IoE), can facilitate integration of distributed renewable energy sources and electric vehicles (EVs) in the smart grid. However, due to the potential security and privacy issues caused by untrusted and opaque energy markets, it becomes a great challenge to optimally schedule the charging behaviors of EVs with distinct energy consumption preferences in SC. In this paper, we propose a contract-based energy blockchain for secure EV charging in SC. First, a permissioned energy blockchain system is introduced to implement secure charging services for EVs with the execution of smart contracts. Second, a reputation-based delegated Byzantine fault tolerance consensus algorithm is proposed to efficiently achieve the consensus in the permissioned blockchain. Third, based on the contract theory, the optimal contracts are analyzed and designed to satisfy EVs' individual needs for energy sources while maximizing the operator's utility. Furthermore, a novel energy allocation mechanism is proposed to allocate the limited renewable energy for EVs. Finally, extensive numerical results are carried out to evaluate and demonstrate the effectiveness and efficiency of the proposed scheme through comparison with other conventional schemes.

Many approaches have been used in the effective management of type 2 diabetes mellitus. A recent paradigm shift has focused on the role of adipose tissues in the development and treatment of the disease. Brown adipose tissues (BAT) and white adipose tissues (WAT) are the two main types of adipose tissues with beige subsets more recently identified. They play key roles in communication and insulin sensitivity. However, WAT has been shown to contribute significantly to endocrine function. WAT produces hormones and cytokines, collectively called adipocytokines, such as leptin and adiponectin. These adipocytokines have been proven to vary in conditions, such as metabolic dysfunction, type 2 diabetes, or inflammation. The regulation of fat storage, energy metabolism, satiety, and insulin release are all features of adipose tissues. As such, they are indicators that may provide insights on the development of metabolic dysfunction or type 2 diabetes and can be considered routes for therapeutic considerations. The essential roles of adipocytokines vis-a-vis satiety, appetite, regulation of fat storage and energy, glucose tolerance, and insulin release, solidifies adipose tissue role in the development and pathogenesis of diabetes mellitus and the complications associated with the disease.

Comparative analysis of the relative victimization of 1,030 adult male prisoners and 500 adult female prisoners in Texas reveals significant gender differences in childhood and adult maltreatment and subsequent substance use and criminality. Female inmates report significantly more maltreatment as children than do male inmates. Moreover, the maltreatment of women increases when they become adults, whereas the maltreatment of men drops sharply. The study found childhood maltreatment to be more strongly associated with adult depression and substance dependence among women than among men. The severity of substance misuse and problems associated with it are stronger predictors of female rates of criminal activity than male rates. Recent literature from the social sciences is presented to account for the findings. A female empowerment treatment model to help women attain control over their lives is suggested.

The development and bicultural validation of the New Sexual Satisfaction Scale (NSSS)--a 20 item, multidimensional, composite measure of sexual satisfaction--is presented. The development of the scale was based on a five-dimension, conceptual model that emphasized the importance of multiple domains of sexual behavior including sexual sensations, sexual awareness and focus, sexual exchange, emotional closeness, and sexual activity. Scale construction and validation were carried out using seven independent samples with over 2,000 participants aged 18 to 55 in Croatia and the United States. Primary data collection was completed using online survey tools. Analyses did not confirm the proposed conceptual framework but suggested a two-dimensional structure focusing on self ("ego-centered") and the other (a "partner- and sexual activity-centered" factor) domains, each containing items representing all five conceptual dimensions. Scale reliability (k = 20) was satisfactory for all samples, and construct validity was confirmed in both cultures. The NSSS was also found to have acceptable one-month stability. It is suggested that the NSSS may be a useful tool for assessing sexual satisfaction regardless of a person's gender, sexual orientation, and relationship status.

Mobile edge computing (MEC) provides a promising approach to significantly reduce network operational cost and improve quality of service (QoS) of mobile users by pushing computation resources to the network edges, and enables a scalable Internet of Things (IoT) architecture for time-sensitive applications (e-healthcare, real-time monitoring, and so on.). However, the mobility of mobile users and the limited coverage of edge servers can result in significant network performance degradation, dramatic drop in QoS, and even interruption of ongoing edge services; therefore, it is difficult to ensure service continuity. Service migration has great potential to address the issues, which decides when or where these services are migrated following user mobility and the changes of demand. In this paper, two conceptions similar to service migration, i.e., live migration for data centers and handover in cellular networks, are first discussed. Next, the cutting-edge research efforts on service migration in MEC are reviewed, and a devisal of taxonomy based on various research directions for efficient service migration is presented. Subsequently, a summary of three technologies for hosting services on edge servers, i.e., virtual machine, container, and agent, is provided. At last, open research challenges in service migration are identified and discussed.

Viola and Jones [9] introduced a method to accurately and rapidly detect faces within an image. This technique can be adapted to accurately detect facial features. However, the area of the image being analyzed for a facial feature needs to be regionalized to the location with the highest probability of containing the feature. By regionalizing the detection area, false positives are eliminated and the speed of detection is increased due to the reduction of the area examined.

Abstract This study evaluates the simulation of the Madden–Julian oscillation (MJO) and convectively coupled equatorial waves (CCEWs) in 20 models from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and compares the results with the simulation of CMIP phase 3 (CMIP3) models in the IPCC Fourth Assessment Report (AR4). The results show that the CMIP5 models exhibit an overall improvement over the CMIP3 models in the simulation of tropical intraseasonal variability, especially the MJO and several CCEWs. The CMIP5 models generally produce larger total intraseasonal (2–128 day) variance of precipitation than the CMIP3 models, as well as larger variances of Kelvin, equatorial Rossby (ER), and eastward inertio-gravity (EIG) waves. Nearly all models have signals of the CCEWs, with Kelvin and mixed Rossby–gravity (MRG) and EIG waves being especially prominent. The phase speeds, as scaled to equivalent depths, are close to the observed value in 10 of the 20 models, suggesting that these models produce sufficient reduction in their effective static stability by diabatic heating. The CMIP5 models generally produce larger MJO variance than the CMIP3 models, as well as a more realistic ratio between the variance of the eastward MJO and that of its westward counterpart. About one-third of the CMIP5 models generate the spectral peak of MJO precipitation between 30 and 70 days; however, the model MJO period tends to be longer than observations as part of an overreddened spectrum, which in turn is associated with too strong persistence of equatorial precipitation. Only one of the 20 models is able to simulate a realistic eastward propagation of the MJO.

Abstract This paper reexamines the determinants of firm performance and, in particular, the role that firm size plays in profitability. A fixed‐effects dynamic panel data model for over 7,000 US publicly‐held firms during the period 1987–2006 provides evidence that profit rates are positively correlated with firm size in a non‐linear manner, holding an array of firm‐ and industry‐specific characteristics constant. In addition, industry‐specific fixed effects play a negligible role in the presence of firm‐specific fixed effects.