The shepherd metaphor in the Old Testament, and its use in pastoral and leadership models

Bibliography: leaves 441-459The shepherd metaphor is a prominent and significant one in the Old Testament. However, it has shifted from an agrarian context, of shepherd and sheep in the literal sense, to a socio-political context, of rulers and people in the political sense: a king is a shepherd to the people. A careful review of the given metaphor raises the question whether the metaphor should be the basis of the pastoral and leadership models that are derived from the image of the shepherd, and whether such models can be enriched by the analysis of the said metaphor as applied to the implementation of the shepherding responsibility described in the Old Testament. This research aims to examine various pastoral and leadership models and their use of the shepherd metaphor in the light of the significance of the said metaphor in the Old Testament. It utilises rhetorical criticism in consultation with metaphorical theory to examine the given metaphor used in the models of pastoral and leadership roles and their relationship with the shepherd metaphor in the New Testament. The objective is threefold: (1) exploring the use of the shepherd metaphor in the Old Testament; (2) examining the use of the shepherd metaphor in pastoral and leadership models, which could include pointing out that some of these models rely heavily on their understanding of New Testament uses of this metaphor; and (3) comparing the Old Testament and pastoral/leadership models’ uses of the shepherd metaphor and drawing conclusions based on this comparison. To achieve that end, the discussion also includes the ancient Near Eastern literature and deuterocanonical texts. The thesis shows that a careful analysis of the uses of the shepherd metaphor in the Old Testament could enrich the literature on Christian leadership as well as pastoral models that use this metaphor as their point of departure.Old Testament and Ancient Near Eastern StudiesD. Phil. (Old Testament

The metaphor of the shepherd in Zechariah 11:4–17

This study examines the metaphor of the shepherd in Zechariah 11:4-17, which is a prominent and significant one in the Hebrew Bible. It defines Yahweh‟s relationship with the nation of Israel and those who have faith in him. But Zechariah 11:4-17 presents a shepherd image which contradicts to the basic metaphor in the Hebrew Bible. The thesis of this study argues that the differing shepherd image in Zechariah 11:4-17 is the result of the rejection by the people of the responsible shepherd, which caused Yahweh to surrender his shepherd responsibility. It is a metaphor designed to punish an unrepentant Israel. Zechariah 11:4-17 furnishes an example of a situation where Yahweh surrendered his shepherding responsibilities to those irresponsible shepherds. This example should be incorporated into the said metaphor, so as an objective and comprehensive meaning may be achieved, and one should consider this metaphorical meaning in the study of the subject.Old Testament and Ancient Near Eastern StudiesM. Th. (Old Testament

A newly identified population of Gambusia affinis (Baird and Girard, 1853), a non-native invasive species, in Lake Kenyir, Malaysia: Implications for management

Gambusia affinis (Baird and Girard, 1853), a notorious non-native invasive fish species, has negatively impacted aquatic ecosystems around the world. This species was recently identified in Lake Kenyir, one of the largest impoundments in SouTheast Asia, using DNA barcoding. The coxI sequence of Gambusia caught in Lake Kenyir was compared with the sequences of topotypic voucher specimens of G. affinis and two other candidate Poeciliidae. The species was found to cluster with G. affinis but not with monophyletic clades of either G. holbrooki or P. reticulata thus confirming species identity. The fish is yet to be widely established in the lake with the current distribution limited to areas of anthropogenic disturbance

Online Self-Supervised Thermal Water Segmentation for Aerial Vehicles

We present a new method to adapt an RGB-trained water segmentation network to target-domain aerial thermal imagery using online self-supervision by leveraging texture and motion cues as supervisory signals. This new thermal capability enables current autonomous aerial robots operating in near-shore environments to perform tasks such as visual navigation, bathymetry, and flow tracking at night. Our method overcomes the problem of scarce and difficult-to-obtain near-shore thermal data that prevents the application of conventional supervised and unsupervised methods. In this work, we curate the first aerial thermal near-shore dataset, show that our approach outperforms fully-supervised segmentation models trained on limited target-domain thermal data, and demonstrate real-time capabilities onboard an Nvidia Jetson embedded computing platform. Code and datasets used in this work will be available at: https://github.com/connorlee77/uav-thermal-water-segmentation.Comment: 8 pages, 4 figures, 3 table

Cooperative Multiple Dynamic Object Tracking on Moving Vehicles Based on Sequential Monte Carlo Probability Hypothesis Density Filter

This paper proposes a generalized method for tracking of multiple objects from moving, cooperative vehicles -- bringing together an Unscented Kalman Filter for vehicle localization and extending a Sequential Monte Carlo Probability Hypothesis Density filter with a novel cooperative fusion algorithm for tracking. The latter ensures that the fusion of information from cooperating vehicles is not limited to a fully overlapping Field Of View (FOV), as usually assumed in popular distributed fusion literature, but also allows for a perceptual extension corresponding to the union of the vehicles' FOV. Our method hence allows for an overall extended perception range for all cooperative vehicles involved, while preserving same or improving the accuracy in the overlapping FOV. This method also successfully mitigates noisy sensor measurement and clutter, as well as localization inaccuracies of tracking vehicles using Global Navigation Satellite Systems (GNSS). Finally, we extensively evaluate our method using a high-fidelity simulator for vehicles of varying speed and trajectories