【学术会议前沿信息|科研必备】JPCS/IEEE出版·EI检索 | 2026材料工程、应用力学、电子AI、应用经济学、管理科学、社会发展、可再生能源与节能国际会议征稿

【学术会议前沿信息|科研必备】JPCS/IEEE出版·EI检索 | 2026材料工程、应用力学、电子AI、应用经济学、管理科学、社会发展、可再生能源与节能国际会议征稿

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前言

• 智汇古都，创领未来！ 在千年古都与北国冰城，将你的前沿研究转化为国际学术舞台上的璀璨星光！✨

🔬 第五届材料工程与应用力学国际学术会议（ICMEAAE 2026）

2026 5th International Conference on Materials Engineering and Applied Mechanics

• 📅 时间：2026年3月6-8日
• 📍 地点：中国·西安
• ✨ 亮点：在古都西安探讨材料科学与应用力学前沿，JPCS稳定出版，助力新材料设计与力学性能突破！
• 🔍 检索：EI Compendex, Scopus
• 👥 适合投稿人群：材料科学、力学、工程物理领域研究者，欢迎分享新型材料与结构创新成果！
• 代码示例：基于图神经网络的晶体结构缺陷预测算法

import torch
import torch.nn as nn
import torch.nn.functional as F

class CrystalDefectGNN(nn.Module):
    """用于预测晶体材料位错与空位缺陷的图神经网络"""
    
    def __init__(self, atom_feat_dim=64, hidden_dim=128):
        super().__init__()
        
        # 原子特征编码器
        self.atom_encoder = nn.Linear(atom_feat_dim, hidden_dim)
        
        # 边特征消息传递层
        self.edge_message = nn.Sequential(
            nn.Linear(hidden_dim*2 + 3, hidden_dim),  # 3D相对坐标
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim)
        )
        
        # 缺陷预测头
        self.defect_head = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim//2),
            nn.ReLU(),
            nn.Linear(hidden_dim//2, 2)  # 位错能量、空位形成能
        )
    
    def forward(self, atom_features, edge_index, edge_vectors):
        # 原子特征编码
        h = self.atom_encoder(atom_features)
        
        # 消息传递
        src, dst = edge_index
        edge_features = torch.cat([h[src], h[dst], edge_vectors], dim=1)
        messages = self.edge_message(edge_features)
        
        # 聚合消息
        aggregated = torch.zeros_like(h)
        for i in range(len(src)):
            aggregated[dst[i]] += messages[i]
        
        # 缺陷预测
        defect_pred = self.defect_head(aggregated)
        return defect_pred

# 使用示例
model = CrystalDefectGNN(atom_feat_dim=64, hidden_dim=128)
atom_features = torch.randn(100, 64)  # 100个原子
edge_index = torch.randint(0, 100, (2, 500))  # 500条边
edge_vectors = torch.randn(500, 3)  # 相对坐标

pred = model(atom_features, edge_index, edge_vectors)
print(f"缺陷预测结果形状: {pred.shape}")
print(f"位错能范围: [{pred[:,0].min():.3f}, {pred[:,0].max():.3f}] eV")

💡 第五届电子技术与人工智能国际学术会议（ETAI 2026）

The 5th International Conference on Electronics Technology and Artificial Intelligence

• 📅 时间：2026年3月6-8日
• 📍 地点：中国·哈尔滨
• ✨ 亮点：在冰雪之城哈尔滨聚焦电子技术与AI融合，IEEE出版赋能，探讨智能系统与芯片设计新前沿！
• 🔍 检索：IEEE Xplore, EI Compendex, Scopus
• 👥 适合投稿人群：电子工程、人工智能、集成电路领域师生学者，诚邀展示硬件与算法协同创新！
• 代码示例：模拟忆阻器交叉阵列的脉冲神经网络推理

import numpy as np

class MemristiveSNNInference:
    """基于忆阻器交叉阵列的脉冲神经网络推理模拟器"""
    
    def __init__(self, input_size=128, hidden_size=64, output_size=10):
        # 模拟忆阻器电导值（权重）
        self.W1 = np.random.uniform(0.1, 1.0, (input_size, hidden_size))
        self.W2 = np.random.uniform(0.1, 1.0, (hidden_size, output_size))
        
        # 神经元膜电位
        self.V_hidden = np.zeros(hidden_size)
        self.V_output = np.zeros(output_size)
        
        # 脉冲阈值
        self.threshold = 1.0
        self.leak_factor = 0.9
    
    def simulate_timestep(self, input_spikes, dt=1e-3):
        """模拟一个时间步的脉冲传播"""
        # 隐藏层膜电位更新
        self.V_hidden = self.V_hidden * self.leak_factor + np.dot(input_spikes, self.W1) * dt
        
        # 隐藏层脉冲发放
        hidden_spikes = (self.V_hidden > self.threshold).astype(float)
        self.V_hidden[hidden_spikes > 0] = 0  # 发放后重置
        
        # 输出层膜电位更新
        self.V_output = self.V_output * self.leak_factor + np.dot(hidden_spikes, self.W2) * dt
        
        # 输出层脉冲发放
        output_spikes = (self.V_output > self.threshold).astype(float)
        self.V_output[output_spikes > 0] = 0
        
        return hidden_spikes, output_spikes
    
    def online_weight_update(self, pre_spikes, post_spikes, lr=0.01):
        """模拟忆阻器的在线权重更新（STDP规则简化版）"""
        # STDP-like 更新规则
        for i in range(len(pre_spikes)):
            for j in range(len(post_spikes)):
                if pre_spikes[i] > 0 and post_spikes[j] > 0:
                    # 同时激活时增强连接
                    self.W1[i, j] += lr
                elif pre_spikes[i] > 0 and post_spikes[j] == 0:
                    # 仅前激活时减弱连接
                    self.W1[i, j] -= lr * 0.5
        
        # 限制电导值范围
        self.W1 = np.clip(self.W1, 0.1, 1.0)

# 使用示例
snn = MemristiveSNNInference(input_size=32, hidden_size=16, output_size=5)
input_spikes = np.random.randint(0, 2, 32)  # 二进制脉冲输入

# 模拟100个时间步
for t in range(100):
    hidden_spikes, output_spikes = snn.simulate_timestep(input_spikes)
    
    # 在线学习
    if t % 10 == 0:
        snn.online_weight_update(input_spikes, hidden_spikes)

print(f"隐藏层脉冲发放次数: {np.sum(hidden_spikes)}")
print(f"输出层脉冲发放次数: {np.sum(output_spikes)}")

📈 第三届应用经济学、管理科学与社会发展国际学术会议（AEMSS 2026）

2026 3rd International Conference on Applied Economics, Management Science and Social Development

• 📅 时间：2026年3月13-15日
• 📍 地点：中国·昆明
• ✨ 亮点：在春城昆明探讨经济管理与社会发展，Atlantis出版推动学术成果在产业与政策中的应用转化！
• 🔍 检索：CNKI
• 👥 适合投稿人群：经济学、管理学、社会学领域研究者，期待分享实证研究与理论模型创新！
• 代码示例：多智能体经济模拟与政策影响评估系统

import numpy as np

class MultiAgentEconomicSimulator:
    """多智能体经济系统模拟器"""
    
    def __init__(self, n_agents=100, n_goods=3):
        self.n_agents = n_agents
        self.n_goods = n_goods
        
        # 智能体属性
        self.productivity = np.random.uniform(0.5, 2.0, n_agents)
        self.preferences = np.random.dirichlet(np.ones(n_goods), n_agents)
        self.money = np.random.uniform(10, 100, n_agents)
        self.inventory = np.random.uniform(0, 20, (n_agents, n_goods))
        
        # 市场价格
        self.prices = np.ones(n_goods)
        
    def production_phase(self, tax_rate=0.1):
        """生产阶段：智能体根据生产力生产商品"""
        production = np.zeros((self.n_agents, self.n_goods))
        for i in range(self.n_agents):
            # 每个智能体专业化生产一种商品
            specialty = i % self.n_goods
            output = self.productivity[i] * (1 - tax_rate)
            production[i, specialty] = output
        
        self.inventory += production
        return production
    
    def trading_phase(self):
        """交易阶段：基于供需的价格发现和交易"""
        # 计算总供给和总需求
        total_supply = self.inventory.sum(axis=0)
        total_demand = np.zeros(self.n_goods)
        
        for i in range(self.n_agents):
            # 需求取决于偏好和收入
            budget = self.money[i] * 0.5  # 花费一半金钱
            demand_i = self.preferences[i] * budget / self.prices
            total_demand += demand_i
        
        # 价格调整（简化版瓦尔拉斯调节）
        excess_demand = total_demand - total_supply
        price_adjustment = excess_demand / (total_supply + 1e-8)
        self.prices *= (1 + 0.1 * price_adjustment)
        
        # 执行交易
        for i in range(self.n_agents):
            for g in range(self.n_goods):
                # 购买决策
                desired = self.preferences[i, g] * self.money[i] * 0.5 / self.prices[g]
                actual = min(desired, self.money[i] / self.prices[g])
                
                # 更新库存和金钱
                if actual > 0:
                    self.inventory[i, g] += actual
                    self.money[i] -= actual * self.prices[g]
        
        return self.prices
    
    def evaluate_policy(self, policy_fn, steps=100):
        """评估政策函数对经济指标的影响"""
        metrics = []
        
        for step in range(steps):
            # 应用政策（如税率调整）
            tax_rate = policy_fn(step, self)
            
            # 运行经济周期
            self.production_phase(tax_rate)
            prices = self.trading_phase()
            
            # 收集指标
            gdp = np.sum(self.inventory * prices)
            inequality = np.std(self.money) / np.mean(self.money)
            metrics.append({
                'step': step,
                'gdp': gdp,
                'inequality': inequality,
                'avg_price': np.mean(prices),
                'tax_rate': tax_rate
            })
        
        return metrics

# 使用示例
economy = MultiAgentEconomicSimulator(n_agents=50, n_goods=2)

# 定义反周期税收政策
def countercyclical_policy(step, econ):
    base_rate = 0.1
    gdp_growth = econ.inventory.sum() / (economy.n_agents * 2)
    if gdp_growth > 1.1:
        return base_rate + 0.05  # 过热时增税
    elif gdp_growth < 0.9:
        return base_rate - 0.05  # 衰退时减税
    return base_rate

# 运行模拟并评估政策
results = economy.evaluate_policy(countercyclical_policy, steps=50)
print(f"最终GDP: {results[-1]['gdp']:.2f}")
print(f"基尼系数: {results[-1]['inequality']:.3f}")
print(f"平均价格: {results[-1]['avg_price']:.3f}")

🌞 第二届可再生能源与节能国际学术会议（REEC 2026）

2026 2nd International Conference on Renewable Energy and Energy Conservation

• 📅 时间：2026年3月13-15日
• 📍 地点：中国·江西新余
• ✨ 亮点：在新能源城市新余聚焦绿色能源革命，JPCS出版助力太阳能、储能与节能技术研发突破！
• 🔍 检索：EI Compendex, Inspec, Scopus
• 👥 适合投稿人群：能源工程、环境科学、电力系统领域师生学者，欢迎分享清洁能源技术创新与应用！
• 代码示例：基于注意力机制的风光互补发电功率预测算法

import torch
import torch.nn as nn
import torch.nn.functional as F

class RenewableEnergyPredictor(nn.Module):
    """风光互补发电功率预测模型"""
    
    def __init__(self, input_features=8, seq_len=24, hidden_dim=64):
        super().__init__()
        
        # 气象特征编码器
        self.weather_encoder = nn.LSTM(input_features, hidden_dim, batch_first=True)
        
        # 时间特征编码器（小时、星期、季节）
        self.time_encoder = nn.Embedding(24*7*4, hidden_dim)  # 小时×星期×季节
        
        # 跨模态注意力
        self.cross_attention = nn.MultiheadAttention(hidden_dim*2, num_heads=4, batch_first=True)
        
        # 时空融合模块
        self.fusion_layer = nn.Sequential(
            nn.Linear(hidden_dim*4, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 2)  # 预测风、光两种功率
        )
        
        # 不确定性估计
        self.uncertainty_head = nn.Linear(hidden_dim, 2)  # 均值和方差
    
    def forward(self, weather_data, time_indices, historical_power):
        batch_size = weather_data.shape[0]
        
        # 编码气象数据
        weather_features, _ = self.weather_encoder(weather_data)
        weather_features = weather_features[:, -1, :]  # 取最后时间步
        
        # 编码时间特征
        time_features = self.time_encoder(time_indices)
        
        # 拼接历史功率特征
        historical_features = historical_power.view(batch_size, -1)
        
        # 跨模态注意力融合
        combined = torch.cat([weather_features.unsqueeze(1), 
                             time_features.unsqueeze(1)], dim=2)
        attended, _ = self.cross_attention(combined, combined, combined)
        attended = attended.squeeze(1)
        
        # 最终特征融合
        all_features = torch.cat([attended, historical_features], dim=1)
        
        # 功率预测
        power_pred = self.fusion_layer(all_features)
        
        # 不确定性估计
        uncertainty = F.softplus(self.uncertainty_head(attended))
        
        return power_pred, uncertainty
    
    def probabilistic_forecast(self, weather_data, time_indices, historical_power, n_samples=100):
        """概率性预测：生成多个可能场景"""
        with torch.no_grad():
            mean_pred, uncertainty = self(weather_data, time_indices, historical_power)
            
            # 从预测分布中采样
            samples = []
            for _ in range(n_samples):
                noise = torch.randn_like(mean_pred) * uncertainty[:, 0:1]
                sample = mean_pred + noise
                samples.append(sample)
            
            samples = torch.stack(samples, dim=1)  # [batch, n_samples, 2]
            
            # 计算置信区间
            lower_bound = torch.quantile(samples, 0.05, dim=1)
            upper_bound = torch.quantile(samples, 0.95, dim=1)
            
            return {
                'mean': mean_pred,
                'samples': samples,
                'confidence_interval': (lower_bound, upper_bound)
            }

# 使用示例
model = RenewableEnergyPredictor(input_features=6, seq_len=24, hidden_dim=32)

# 模拟输入数据
batch_size = 4
weather_data = torch.randn(batch_size, 24, 6)  # 24小时，6个气象特征
time_indices = torch.randint(0, 24*7*4, (batch_size,))  # 时间索引
historical_power = torch.randn(batch_size, 48)  # 过去48小时功率数据

# 预测
power_pred, uncertainty = model(weather_data, time_indices, historical_power)
prob_result = model.probabilistic_forecast(weather_data, time_indices, historical_power, n_samples=50)

print(f"预测风力功率: {power_pred[:,0].mean():.2f} ± {uncertainty[:,0].mean():.2f} kW")
print(f"预测光伏功率: {power_pred[:,1].mean():.2f} ± {uncertainty[:,1].mean():.2f} kW")
print(f"95%置信区间宽度: {(prob_result['confidence_interval'][1] - prob_result['confidence_interval'][0]).mean():.2f}")

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