kubectl debug cluster conatiner

kubectl run net-debug -n dev-refresh-services --rm -it --image=alpine/curl:latest -- /bin/sh
curl -v http://eurekahostr:8080/eureka/apps

customHook

import { useEffect, useState } from "react";
import {
  getUsers,
  createUser,
  updateUser,
  deleteUser,
} from "../services/userService";

function UsersWithService() {
  const [users, setUsers] = useState([]);
  const [loading, setLoading] = useState(false);

  async function fetchUsers() {
    setLoading(true);
    try {
      const res = await getUsers();
      setUsers(res.data);
    } catch (err) {
      console.error(err);
    } finally {
      setLoading(false);
    }
  }

  async fun

Mob Names

Syd
Makaveli

b2b Checkout TEST frontend v2

import wixUsers from 'wix-users';
import { currentMember } from 'wix-members-frontend';
import {
  getOrderProfileByContactId,
  createB2BOrderFromProfile,
  smokeOrdersKey
} from 'backend/b2bCheckoutTest.web';

// Fallback nur wenn nicht eingeloggt / Profil leer
const TESTDATA = {
  companyName: 'Musterfirma',
  firstName: 'Max',
  lastName: 'Mustermann',
  email: 'bernhard.holl@gmx.at',
  phone: '+43 664 9920440',
  billingAddress: { addressLine: 'Musterstrasse 9', postalCode: '

b2b Checkout TEST v2

// backend/b2bCheckoutTest.web.js
import { Permissions, webMethod } from 'wix-web-module';
import wixStores from 'wix-stores-backend';
import { contacts } from 'wix-crm-backend';
import wixFetch from 'wix-fetch';
import { getSecret } from 'wix-secrets-backend';

/* ---------------------------------- Helpers ---------------------------------- */

function pseudoUuid() {
  return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, c => {
    const r = (Math.random() * 16) | 0; // es

OpenWISP2 [ problems & solutions ]

# Problem

OpenWISP custom commands remained **in-progress** because **Celery workers** were stale/hung (lost broker connection or stuck tasks), so jobs were never processed.

# Solution

Restart Celery services (and Daphne optionally) to restore task processing:

```bash
supervisorctl restart celery celery_network celery_monitoring celery_firmware_upgrader celerybeat
supervisorctl restart daphne:asgi0
```

Report Gympass Subscriptions

[HttpGet("report")]
public async Task<string> GenerateReport()
{
    string report = "DATA;NOME;VALOR;RENOVACAO;FUNCIONARIO\n";

    IList<WellhubSubscription> subscriptions = await _wellhubRepository
        .FilterSubscriptions(x => x.DateCreated > new DateTime(2025, 9, 1) && x.Status == SubscriptionStatus.Active);

    List<Guid> saleIds = subscriptions.Select(x => x.SaleId).ToList();
    IList<Sale> sales = await _saleRepository.FilterSales(x => saleIds.Contains(x.Id));

    forea

backend v4

// backend/b2bCheckout.jsw

import { currentMember } from 'wix-members-backend';
import { contacts }      from 'wix-crm-backend';
import wixData           from 'wix-data';
import wixStores         from 'wix-stores-backend';

// --------------------------- Helper: UUID + Country ---------------------------
function pseudoUuid() {
  return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, c => {
    const r = (Math.random() * 16) | 0;
    const v = c === 'x' ? r : (r & 0x3) | 0x8;

2011. Final Value of Variable After Performing Operations

There is a programming language with only four operations and one variable X: ++X and X++ increments the value of the variable X by 1. --X and X-- decrements the value of the variable X by 1. Initially, the value of X is 0. Given an array of strings operations containing a list of operations, return the final value of X after performing all the operations.
/**
 * @param {string[]} operations
 * @return {number}
 */
var finalValueAfterOperations = function(operations) {
    // Step 1: Initialize the variable X to 0
    let X = 0;

    // Step 2: Loop through each operation in the array
    for (let i = 0; i < operations.length; i++) {
        let op = operations[i];

        // Step 3: Check if the operation is an increment
        if (op === "++X" || op === "X++") {
            X += 1; // Increase X by 1
        }
        // Step 4: Check if the o

checkout v3

// /b2b‑checkout.js

import wixUsers from 'wix-users';
import wixLocation from 'wix-location';
import { session } from 'wix-storage-frontend';
import { getMemberCheckoutProfile, createOrderFromProfile } from 'backend/b2bCheckout.jsw';

$w.onReady(function () {
  const logs = [];
  resetFields();
  if ($w('#paymentMethodRadio')) {
    $w('#paymentMethodRadio').value = 'invoice';
  }
  if ($w('#submitOrderButton')) {
    $w('#submitOrderButton').disable();
  }
  waitUntilLoggedIn(l

backend v3

// backend/b2bCheckout.jsw

import { currentMember } from 'wix-members-backend';
import { contacts } from 'wix-crm-backend';
import wixData from 'wix-data';
import wixStores from 'wix-stores-backend';

/* --------------------------- Helper: UUID + Country --------------------------- */
function pseudoUuid() {
  return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, c => {
    const r = (Math.random() * 16) | 0;
    const v = c === 'x' ? r : (r & 0x3) | 0x8;
    return v.toStr

How to check if the new architecture is enabled on react native (Hermes/Fabric)

https://stackoverflow.com/questions/57350008/react-native-hermes-is-not-enabled/72530085#72530085 https://stackoverflow.com/questions/72463820/how-to-check-if-the-new-architecture-is-enabled-on-react-native-0-68/76888613#76888613
const isHermes = global?.HermesInternal ? 'Hermes' : 'JSC';
const isFabric = global?.nativeFabricUIManager ? 'Fabric' : 'Paper';

console.log(`Engine: ${isHermes}`);
console.log(`Renderer: ${isFabric}`);

Exploring the Impact of Emotional States on Teamwork Effectiveness in Interprofessional Education Using EEG Data

# Exploring the Impact of Emotional States on Teamwork Effectiveness in Interprofessional Education Using EEG Data ## Description This repository contains the official implementation and supplementary materials of the paper: **“Exploring the Impact of Emotional States on Teamwork Effectiveness in Interprofessional Education Using EEG Data”** by *Chongye Wang (Ningbo University of Finance and Economics)* and *Wei Liu (Sichuan Provincial Maternity and Child Health Care Hospital)*, 2024. This work proposes a novel **EEG-driven collaborative learning framework** that integrates emotional state modeling with interprofessional teamwork analysis. The approach introduces two major components: a **Collaborative Interprofessional Learning Network (CILN)** and a **Dynamic Interaction Strategy (DIS)**. Together, these enhance both the interpretability and robustness of emotion-aware team collaboration models in interprofessional education (IPE). --- ## Framework Overview ### Collaborative Interprofessional Learning Network (CILN) The **CILN** module aims to model knowledge sharing and complementarity among members from different disciplines. It includes three major layers: - **Learner Embedding Module:** Encodes each participant’s cognitive and emotional features from EEG signals. - **Knowledge Fusion Network:** Performs cross-disciplinary information alignment using a convolution–Transformer hybrid backbone. - **Collaborative Optimization Layer:** Adjusts interaction weights based on team diversity and learning compatibility. *Figure 1 (Page 8)* in the paper illustrates the architecture combining CNN, Transformer, and a pre-trained BART encoder to achieve hierarchical EEG feature extraction and semantic fusion. --- ### Dynamic Interaction Strategy (DIS) The **DIS** module models evolving interactions within teams over time. It contains: - **Adaptive Interaction Modeling:** A Graph Convolutional Network (GCN) integrated with temporal attention to capture relationship dynamics. - **Feedback-Driven Collaboration Refinement:** Adjusts team interactions according to real-time feedback signals. *Figure 2 (Page 10)* visualizes the temporal structure of DIS, showing how EEG-based emotional variation influences collaboration flow. --- ## Theoretical Foundations This study is grounded in: - **Social Constructivism Theory** - **Affective Events Theory (AET)** - **Multimodal Learning Theory** The framework combines emotional and cognitive features through a joint optimization function that balances: - **Diversity Regularization (Ldiv)** – promoting heterogeneous team perspectives. - **Compatibility Loss (Lcompat)** – ensuring effective task-oriented cooperation. A dynamic update rule adjusts the interaction matrix over time, enabling continuous adaptation to team feedback. --- ## Experimental Setup ### Datasets The model is trained and evaluated on four benchmark EEG-based emotion datasets: - **DEAP** - **AMIGOS** - **DREAMER** - **ASCERTAIN** These datasets collectively represent a wide range of emotional and cognitive patterns relevant to teamwork and collaborative learning. ### Baseline Models For comparison, several mainstream models were tested: - CNN - BiLSTM - GRU - Transformer - BERT - RoBERTa --- ## Experimental Results | Dataset | Accuracy | F1 Score | AUC | Improvement over RoBERTa | |--------------|-----------|-----------|-------|---------------------------| | DEAP | 93.45% | 92.12% | 93.80% | +2.6% | | AMIGOS | 92.33% | 91.45% | 93.05% | +1.7% | | DREAMER | 92.67% | 91.34% | 93.01% | +2.2% | | ASCERTAIN | 90.89% | 89.78% | 92.34% | +3.4% | *Tables 1 and 2 (Page 19)* of the paper report detailed performance metrics. The proposed **CILN + DIS** combination consistently surpasses baseline methods in all datasets. --- ## Ablation Study An ablation analysis (Page 20) demonstrates that removing any core component (embedding, fusion, or interaction) causes a 4–5% accuracy drop. This confirms the synergistic effect of all three modules in improving team emotion recognition and collaboration modeling. --- ## Key Findings - **Innovation:** First framework integrating EEG-based emotional modeling with interprofessional team learning. - **Performance:** Achieves significant improvements in classification accuracy and robustness across multiple EEG benchmarks. - **Interpretability:** Visual attention maps explain how emotional cues influence teamwork effectiveness. - **Future Directions:** Lightweight deployment using wearable EEG devices and real-time feedback integration. --- ## Mathematical Formulation The learning process is formalized as a multi-objective optimization problem: - Utility Function **U(K, C)** captures team learning gain based on knowledge matrix *K* and collaboration matrix *C*. - Interaction matrix *C* evolves over time using adaptive feedback coefficient *ρt*. - Final objective: maximize *U(K, C)* while minimizing regularization terms *(Ldiv + Lcompat)*. --- ## Requirements - Python ≥ 3.9 - PyTorch ≥ 2.1 - NumPy, Pandas, Matplotlib, tqdm, TensorBoard - GPU: NVIDIA A100 or equivalent for full-scale EEG model training --- ## Project Structure The core implementation consists of five main Python scripts: - **`train.py`** – Entry point for model training. Handles training loops, optimization, checkpoint saving, and logging (≈100 lines). - **`model.py`** – Defines the neural network architecture. Includes feature extraction layers, attention mechanisms, and forward computation (≈100 lines). - **`dataset.py`** – Manages dataset loading and preprocessing. Implements custom `Dataset` and `DataLoader` classes for EEG and image inputs (≈100 lines). - **`utils.py`** – Contains helper utilities and evaluation metrics. Provides functions for accuracy, F1 computation, checkpoint management, and visualization (≈100 lines). - **`inference.py`** – Performs inference using trained models. Loads saved weights, processes test data, and outputs prediction results (≈100 lines). --- ## Results Visualization *Figure 4 (Page 15)* demonstrates the evolving team collaboration graph under the DIS mechanism. It shows stronger node connectivity when team emotion alignment increases, indicating that emotional coherence directly enhances collaborative performance. --- ## Citation If you use this work or build upon it, please cite the following: **Wang, C., & Liu, W. (2024).** *Exploring the Impact of Emotional States on Teamwork Effectiveness in Interprofessional Education Using EEG Data.* *PLOS ONE (under revision).* --- ## License This project is released under the **MIT License**. Please see the LICENSE file for details. --- ## Authors - **Chongye Wang** – Methodology, Implementation, and Experiments - **Wei Liu** – Validation, Writing, and Supervision --- ## Acknowledgments This study was supported by *Ningbo University of Finance and Economics* and *Sichuan Provincial Maternity and Child Health Care Hospital*. The authors declare no conflict of interest.
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from model import HRAN_CGUR_Network
from dataset import EEGDataset
from utils import AverageMeter, accuracy, save_checkpoint, set_seed
import yaml
import os
from tqdm import tqdm


def train_one_epoch(model, loader, criterion, optimizer, device):
    model.train()
    loss_meter, acc_meter = AverageMeter(), AverageMeter()

    for batch in tqdm(loader, desc="Training", ncols=100

backend v2

// backend/b2bCheckout.jsw

import { currentMember } from 'wix-members-backend';
import { contacts } from 'wix-crm-backend';
import wixData from 'wix-data';

function contactToHeader(contact) {
  const firstName = contact?.firstName || contact?.info?.name?.first || '';
  const lastName  = contact?.lastName  || contact?.info?.name?.last  || '';
  const email     = contact?.info?.emails?.[0]?.email || '';
  const company   = contact?.company || contact?.info?.company || '';
  const addr

color-mixでopacity適用後の背景色カラーコードを描画する

<div class="box opacity"></div>
<div class="box opacity-mix"></div>
<div class="box color-mix"></div>

Mob Control

[[mob]]
name = ["exoticbirds:*", "minecraft:pig", "minecraft:cow", "minecraft:chicken"]

[[mob.rule]]
type = "natural"
run = 125

[[mob]]
name = ["epichunt:*"]

[[mob.rule]]
type = "natural"
run = 250

[[mob]]
name = ["treasure2:bound_soul"]

[[mob.rule]]
type = "all"
set.sunburn= 0

[[mob]]
name = ["mocreatures:rat"]

[[mob.rule]]
type = "all"
set.attack=["mincreaft:skeleton"]

[[mob]]
name = ["cave_stuff:purplespringtail", "cave_stuff:grottosculpin"]

[[mob.rule]