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Public transit systems are crucial to mobility and access in cities throughout the world. This article addresses the importance of these transit systems in San Antonio, Texas. We show how transit systems exacerbate race and class inequality and the accessibility of city spaces with a focus on San Antonio’s buses. Using mixed methods (surveys, interviews, ethnography, and document analysis) we illustrate that poor and working-class Latinx communities experience reduced access to resource rich areas of the city when they are dependent upon the city’s public transportation. To better describe this experience we use the concepts, enclaves of exclusion and enclaves of inaccessibility. Our findings show that mobility through San Antonio for poor and working class Latinxs is limited especially for people in these communities who rely on public transit. This experience with these public transit systems often renders them as individuals who do not belong in certain neighborhoods, and ultimately reinforces the longstanding histories of race and class segregation in San Antonio. 

The burgeoning sophistication of Artificial Intelligence (AI) has catalyzed the rapid proliferation of Large Language Models (LLMs) within software development. These models are increasingly employed to automate the generation of functionally correct code, address complex computational problems, and facilitate the debugging of existing software systems. However, LLM-generated code often faces challenges due to inherent inefficiencies, including redundant logical structures, factually inconsistent content (hallucinations), and programming errors. To address this issue, our research rigorously evaluated the computational efficiency of Python code generated by three prominent LLMs: GPT-4o-Mini, GPT-3.5-Turbo, and GPT-4-Turbo. The evaluation metrics encompass execution time, memory utilization, and peak memory consumption, while maintaining the functional correctness of the generated code. Leveraging the EffiBench benchmark datasets within the Google Vertex AI Workbench environment, across a spectrum of machine configurations, the study implemented a consistent seed parameter to ensure experimental reproducibility. Furthermore, we investigated the impact of two distinct optimization strategies: Chain-of-Thought (CoT) prompting and model fine-tuning. Our findings reveal a significant enhancement in efficiency metrics for GPT-4o-Mini and GPT-3.5-Turbo when employing CoT prompting; however, this trend was not observed for GPT-4-Turbo. Based on its promising performance with CoT prompting, we selected the GPT-4o-Mini model for subsequent fine-tuning, aiming to further enhance both its computational efficiency and accuracy. However, contrary to our expectations, fine-tuning the GPT-4o-Mini model led to a discernible degradation in both its accuracy and computational efficiency. In conclusion, this study provides empirical evidence suggesting that the deployment of high-CPU machine configurations, in synergy with the utilization of the GPT-4o-Mini model and CoT prompting techniques, yields demonstrably more efficient and accurate LLM-generated Python code, particularly within computationally intensive application scenarios. 

Serverless computing services are offered by major cloud service providers such as Google Cloud Platform, Amazon Web Services, and Microsoft Azure. The primary purpose of the services is to offer efficiency and scalability in modern software development and IT operations while reducing overall costs and operational complexity. However, prospective customers often question which serverless service will best meet their organizational and business needs. This study analyzed the features, usability, and performance of three serverless cloud computing platforms: Google Cloud’s Cloud Run, Amazon Web Service’s App Runner, and Microsoft Azure’s Container Apps. The analysis was conducted with a containerized mobile application designed to track real-time bus locations for San Antonio public buses on specific routes and provide estimated arrival times for selected bus stops. The study evaluated various system-related features, including service configuration, pricing, and memory and CPU capacity, along with performance metrics such as container latency, distance matrix API response time, and CPU utilization for each service. The results of the analysis revealed that Google’s Cloud Run demonstrated better performance and usability than AWS’s App Runner and Microsoft Azure’s Container Apps. Cloud Run exhibited lower latency and faster response time for distance matrix queries. These findings provide valuable insights for selecting an appropriate serverless cloud service for similar containerized web applications. 

In current software applications, numerous vulnerabilities may be present. Attackers attempt to exploit these vulnerabilities, leading to security breaches, unauthorized entry, data theft, or the incapacitation of computer systems. Instead of addressing software or hardware vulnerabilities at a later stage, it is better to address them immediately or during the development phase. Tools such as AIBugHunter provide solutions designed to tackle software issues by predicting, categorizing, and fixing coding vulnerabilities. Essentially, developers can see where their code is susceptible to attacks and obtain details about the nature and severity of these vulnerabilities. AIBugHunter incorporates VulRepair to detect and repair vulnerabilities. VulRepair currently predicts patches for vulnerable functions at 44%. To be truly effective, this number needs to be increased. This study examines VulRepair to see whether the 44% perfect prediction can be increased. VulRepair is based on T5 and uses both natural language and programming languages during its pretraining phase, along with byte pair encoding. T5 is a text-to-text transfer transformer model with an encoder and decoder as part of its neural network. It outperforms other models such as VRepair and CodeBERT. However, the hyperparameters may not be optimized due to the development of new optimizers. We reviewed a deep neural network (DNN) optimizer developed by Google in 2023. This optimizer, the Evolved Sign Momentum (LION), is available in PyTorch. We applied LION to VulRepair and tested its influence on the hyperparameters. After adjusting the hyperparameters, we obtained a 56% perfect prediction, which exceeds the value of the VulRepair report of 44%. This means that VulRepair can repair more vulnerabilities and avoid more attacks. As far as we know, our approach utilizing an alternative to AdamW, the standard optimizer, has not been previously applied to enhance VulRepair and similar models. 

A neuromuscular junction (NMJ) is a particularized synapse that activates muscle fibers for macro-motions, requiring more energy than computation. Emulating the NMJ is thus challenging owing to the need for both synaptic plasticity and high driving power to trigger motions. Here, we present an artificial NMJ using CuInP₂S₆(CIPS) as a gate dielectric integrated with an AlGaN/GaN-based high-electron mobility transistor (HEMT). The ferroelectricity of the CIPS is coupled with the two-dimensional electron gas channel in the HEMT, providing a wide programmable current range of 6 picoampere per millimeter to 5 milliampere per millimeter. The large output current window of the CIPS/GaN ferroelectric HEMT (FeHEMT) allows for amplifier-less actuation, emulating the biological NMJ functions of actuation and synaptic plasticity. We also demonstrate the emulation of biological oculomotor dynamics, including in situ object tracking and enhanced stimulus responses, using the fabricated artificial NMJ. We believe that the CIPS/GaN FeHEMT offers a promising pathway for bioinspired robotics and neuromorphic vision.