denverwilliams

64A Single Phase Load Management System — Full Technical Specification

Property: 4 Houses on Single 64A Phase with 13kW Solar

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Table of Contents

1. System Overview
2. House Load Summary

denverwilliams

64A Single Phase Load Management System — Full Technical Specification

Property: 4 Houses on Single 64A Phase with 13kW Solar (New Zealand)

---

Table of Contents

1. System Overview
2. House Load Summary

denverwilliams

Workplace Stress, Pressure, Fatigue & Burnout Detection System

Overview

This system detects patterns of workplace stress, pressure, fatigue, and burnout in email communications using a multimodal computational approach: combining state-of-the-art transformer-based NLP models with validated email metadata behavioral analysis and explainable linguistic features.

Purpose: Early identification of exhaustion (emotional depletion), work pressure (job demands), and burnout risk to enable timely intervention and promote employee well-being in organizational settings.

Approach: Three-tier architecture integrating (1) stress-specific transformer ensembles enhanced with LIWC features (86-93% F1 accuracy) for text-based stress detection, (2) email metadata behavioral analysis (84% F1, 96% AUC) grounded in Job Demands-Resources theory, and (3) explainable AI layer providing interpretable linguistic indicators.

denverwilliams

Aggressive & Coercive Behavior Detection System

Overview

This system detects patterns of aggressive and coercive behavior in workplace email communications using a socio-computational approach: combining state-of-the-art transformer-based NLP models with validated psychological frameworks.

Purpose: Early identification of both overt (explicit threats, insults) and covert (gaslighting, isolation, economic control) aggressive communication patterns to promote healthy workplace culture and compliance with harassment prevention standards.

Approach: Ensemble transformer models (93% F1 accuracy) for computational detection, integrated with Checklist of Controlling Behaviors (CCB) framework for interpretability and domain-specific categorization, enhanced by relationship-level contextual analysis.

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#Source: https://github.com/Azure/azure-quickstart-templates/blob/master/quickstarts/microsoft.compute/vms-with-selfhost-integration-runtime/gatewayInstall.ps1
param(
    [string]$gatewayKey,
    [string]$clientID, 
    [string]$azurePassword,
    [string]$crowdStrike,
    [string]$autoMox 
)

# init log setting

denverwilliams

#!/bin/bash

search_dir=$1
policy_script=$2
client_id=$3
client_secret=$4
tenant_id=$5
domain=$6

denverwilliams

Building Bazel on armv7l QEMU

Here you'll learn how to build Bazel for Pynq-Z1 (image v2.4). A similar flow can be run directly on the board (native build). However, this guide is based on QEMU flow, which is running faster and cleaner than a native build on the board.

The same flow can also target aarch64 (ZCU104). You just have to work with the correct image (instead of Pynq-Z1-2.4.img, work with ZCU104-2.4.img).

denverwilliams

Building Bazel for aarch64

I am building bazel for aarch64 on Amazon A1 platform. This flow is pretty nice and much easier than the QEMU flow which is prone to QEMU bugs.

Requirement

• An Amazon A1 platform running Ubuntu 18.04.

denverwilliams

#!/bin/bash
#
# Copyright 2014 Hewlett-Packard Development Company, L.P.
# All Rights Reserved.
# Authored by Yazz D. Atlas <yazz.atlas@hp.com>
#
#    Licensed under the Apache License, Version 2.0 (the "License"); you may
#    not use this file except in compliance with the License. You may obtain
#    a copy of the License at
#

denverwilliams

#!/bin/bash
#
# Copyright 2014 Hewlett-Packard Development Company, L.P.
# All Rights Reserved.
# Authored by Yazz D. Atlas <yazz.atlas@hp.com>
#
#    Licensed under the Apache License, Version 2.0 (the "License"); you may
#    not use this file except in compliance with the License. You may obtain
#    a copy of the License at
#