struggle for life

本文表达了作者对一位名为‘宁江侠’的女性深切的关怀与祝福,尽管两人间存在某种无法逾越的距离,作者仍期望她能摆脱困境,开启新的人生。

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struggle for life

my angel was gone,that
i still kept a heart of looking forward to,this
and i didn't know,
that whether she lied me or not
my heart was broken out

my angel was gone,that
i still waited in hope all the time,this
and still pray for my love,angel
happy everyday and lives better
shoule you fall down on it?
 
my angel was gone,that
i still being await with great anxiety,this
or truely i was wrong?
you were right,maybe
Bless you! still

my angel was gone,that
i still wanna her can return to the fold,this
it hurt your body,konw?
give up it such a thing
i shouted to the gold crazily

my dear angel
trust yourself,just
think about in several years and let a new life to you
struggle on it
my dear angel


-----------------------------------献给‘宁江侠’
我不会写一些所谓的诗,但今晚不知道为什么,有了这种冲动。看不到你可能反而会更好,我也不知道为什么,自己竟然做了一个以前自己非常鄙视的看客,或许我现在知道,什么叫做终点。
我不知道她怎么会做上这行,或许自己根本一点也不了解她,但我还是忠心的希望她能够摆脱这种困境,重新走上一条有益于她自己的路,尽管这非常难。---------------------heshanxingzhe-on 04:22---Nov.16 2008


资源下载链接为: https://pan.quark.cn/s/d37d4dbee12c A:计算机视觉,作为人工智能领域的关键分支,致力于赋予计算机系统 “看懂” 世界的能力,从图像、视频等视觉数据中提取有用信息并据此决策。 其发展历程颇为漫长。早期图像处理技术为其奠基,后续逐步探索三维信息提取,与人工智能结合,又经历数学理论深化、机器学习兴起,直至当下深度学习引领浪潮。如今,图像生成和合成技术不断发展,让计算机视觉更深入人们的日常生活。 计算机视觉综合了图像处理、机器学习、模式识别和深度学习等技术。深度学习兴起后,卷积神经网络成为核心工具,能自动提炼复杂图像特征。它的工作流程,首先是图像获取,用相机等设备捕获视觉信息并数字化;接着进行预处理,通过滤波、去噪等操作提升图像质量;然后进入关键的特征提取和描述环节,提炼图像关键信息;之后利用这些信息训练模型,学习视觉模式和规律;最终用于模式识别、分类、对象检测等实际应用。 在实际应用中,计算机视觉用途极为广泛。在安防领域,能进行人脸识别、目标跟踪,保障公共安全;在自动驾驶领域,帮助车辆识别道路、行人、交通标志,实现安全行驶;在医疗领域,辅助医生分析医学影像,进行疾病诊断;在工业领域,用于产品质量检测、机器人操作引导等。 不过,计算机视觉发展也面临挑战。比如图像生成技术带来深度伪造风险,虚假图像和视频可能误导大众、扰乱秩序。为此,各界积极研究检测技术,以应对这一问题。随着技术持续进步,计算机视觉有望在更多领域发挥更大作用,进一步改变人们的生活和工作方式 。
能帮我做一份完整的问卷吗? 2.1Stimulus-Organism-Response Model (SOR) Existing quantitative studies predominantly rely on behavioral indicators such as click-through rate and conversion rate (Zhong et al. 2024), or biometric sensors like eye-tracking experiments to measure consumer responses (Kim and Lee 2021). However, these approaches fail to fully capture the dynamic cognitive and emotional processes of elderly consumers. Behavioral indicators only reflect the final outcomes of live streaming, without identifying specific elements that drive these results. Meanwhile, biometric sensors primarily measure short-term physiological responses, such as arousal, and fail to capture the long-term trust-building that occurs during live broadcasts. This oversight is significant, as trust established over time is crucial for understanding the deeper cognitive and emotional engagement of elderly consumers. The Stimulus-Organism-Response (SOR) model, originally developed within the field of environmental psychology (Mehrabian and Russell 1974), has since found extensive application in marketing and e-commerce research (Eroglu, Machleit, and Davis 2001). This model offers a comprehensive framework for analyzing how external stimuli trigger internal states, which subsequently drive behavioral responses. In live-streaming e-commerce, stimuli encompass content characteristics such as product displays and characteristics of host. These elements interact with the cognitive and affective systems of elderly consumers, ultimately influencing their purchase intention. Critically, the SOR model alone is sufficient to fully explain the dual-path decision-making processes observed among elderly populations. By combining these theoretical perspectives, this framework not only bridges the gap between environmental stimuli and elderly consumers’ cognitive and emotional trade-offs, but also offers a more nuanced understanding of their decision-making dynamics. 2.2Stimulus (S) Content marketing demonstrates its distinct advantages when targeting the elderly market segment. Research by Lesakova (2016) indicates that this demographic places an emphasis on the intrinsic utility of the products and the degree of satisfaction of their own needs. Content marketing is particularly effective in enhancing awareness and trust in product value by providing detailed product information and practical use cases. To better understand the underlying mechanisms, the design of live-streaming content for elderly consumers requires careful consideration of three critical dimensions: content relevance, information quality, and host characteristics. In terms of content relevance, elderly consumers demonstrate a clear preference for content that aligned with their physiological needs (Liu et al. 2025) and psychological preferences (Thörnqvist et al. 2025). Empirical evidence from CBNData reveals a significant expansion in the consumption of online health and nutritional products among senior citizens, with the market scale nearly doubling over the past three years (Hu, Fu, and Gan 2022). This trend underscores the elderly cohort's pronounced emphasis on functional utility. Particularly noteworthy is the emerging "new elderly" demographic, comprising individuals born in the 1960s and 1970s (Li 2022). This group exhibits elevated digital proficiency, enhanced disposable income, and diverse consumption aspirations spanning health maintenance, cultural tourism, and lifelong learning pursuits (Cheng 2025). Consequently, content strategies targeting this demographic must evolve to accommodate their active lifestyle orientations and multifaceted consumer requirements. Regarding information quality, particular attention should be given to the clarity of information presentation and product displays. Clarity, in this context, involves the volume, pace, and complexity of the information. Product displays, pertain to the demonstration of the use cases and the strengths and weaknesses of products. On one hand, well-designed product displays can effectively alleviate the uncertainty that elderly consumers may experience regarding products. By enabling interaction, these displays can also cultivate trust. This is especially significant given that elderly individuals tend to be more cautious during the decision-making process, as noted by Lu and Chen (2021). On the other hand, elderly consumers often struggle with information overload and technical complexity in digital environments (Qian 2024). Product descriptions that are overly detailed or laden with jargon can be particularly overwhelming for this demographic. Research in the domain of live-streaming e-commerce has indicated that employing simplified yet comprehensive explanations can significantly enhance cognitive accessibility for users (Qian 2024). Therefore, it is of great importance to comprehend the threshold between simplicity and informativeness in product presentations. Host characteristics represent a key dimension in live-stream shopping. The credibility of anchors and the use of dialects are two significant factors within this dimension. Research has demonstrated that the credibility of presenters or influencers exerts a substantial influence on purchase decisions, particularly in live-stream shopping contexts (Chen et al. 2024). Elderly consumers, who may harbor greater skepticism toward generic marketing claims, are more likely to be swayed by the expert endorsements of anchors. Such endorsements confer a sense of authority and trustworthiness, which are paramount for this demographic. In addition, the use of dialects also plays a pivotal role in reducing perceived risks and fostering reliability. This reliability, in turn, reinforces the trust-building process. For elderly consumers who prioritize authenticity and genuine connections (Leigh, Peters, and Shelton 2006), the use of dialects can be especially potent. However, the specific role of dialects in constructing trust among elderly consumers remains unexplored in the current literature. 2.3Organism (O) The organism component of the SOR framework captures the cognitive and affective mechanisms through which elderly consumers process live-streaming stimuli. Breaking it into perceived value, cognitive load, and trust allows for a nuanced analysis of their decision-making processes in live-streaming environments. Cognitive engagement in elderly consumers manifests through perceived value and the cognitive load. Perceived value is a key concept in consumer behavior research. It represents the consumers' subjective evaluation of the overall benefits they expect to receive from a product or service relative to its costs (Zeithaml 1988). In the context of live-streaming, when the content is highly relevant and informative, consumers may perceive that they are getting more value for their time and potential purchase. Higher perceived value can lead to more positive attitudes toward the products and stronger purchase intentions. Previous research points out that informative product display could significantly enhance perceived value while reducing uncertainty by the means of detailed use cases and risk-benefit comparisons (Lu and Chen 2021). Additionally, information aligned with their health interests significantly boosts perceived value (Gong et al. 2024). According to Sweller (1988), cognitive load refers to the psychological pressure generated by the working memory system when an individual is processing information, learning or solving problems due to the total amount and complexity of information. When task demands exceed its capacity, it can lead to cognitive overload, reducing learning efficiency or decision quality. Therefore, cognitive limitations in aging populations, such as reduced working memory and processing speed, heighten susceptibility to cognitive overload (Pavlidis 2025).While younger users tolerate high information density (Lee et al. 2023), elderly consumers often experience overload from complex interfaces or rapid presentations in live-streaming (Qian 2024). Trust is generally defined as the psychological state in which an individual or group, in the presence of risk or uncertainty, is willing to take vulnerability behavior based on positive expectations of the reliability, goodwill, or ability of others (Mayer, Davis, and Schoorman 1995). Informative product displays, which provide risk-benefit comparisons, align with the positive expectation of reliability. Similarly, the use of dialects can enhance trust and foster emotional bonds with elderly consumers. Expert endorsements also play a key role in building trust by providing a sense of trustworthiness. For instance, the "White Coat Effect" shows that the presence of medical professionals in live streams can increase trust (Brase and Richmond 2004). Given their cautious approach to decision-making and higher susceptibility to uncertainty, elderly consumers tend to place greater reliance on emotional trust (Leung, Zhong, and Sun 2025). This emotional bond can significantly boost their engagement and purchase intentions in the context of live-streaming e-commerce. Based on the above discussion, the following hypotheses are formulated: H1: Content relevance has a positive impact on the perceived value of elderly consumers. H2: Product displays have a positive impact on the perceived value of elderly consumers. H3: Product displays has a positive effect on the trust of elderly consumers. H4: The clarity of information has a negative impact on the cognitive load of elderly consumers. H5: Expert endorsement has a positive impact on the trust of elderly consumers. H6: The use of dialects has a positive impact on the trust of elderly consumers. 2.4Response (R) Purchase intentions reflect consumers' likelihood of making a purchase decision (Wang 2024). In the live-streaming environment, elderly consumers' purchase intentions are shaped by various factors. As mentioned earlier, stimulus factors like content design can influence organism factors such as perceived value, cognitive load and trust, which ultimately affect purchase intentions. Existing literature has indicated that positive perceived value (He 2024) and high trust levels (Lu, Fan, and Zhou 2016) can enhance purchase intentions, while high cognitive load tends to have a negative impact (Allen et al. 2013). By understanding these relationships, businesses can adjust their live-streaming content strategies to foster more favorable purchase intentions among elderly consumers. Therefore, the following hypothesis is proposed: H7: Perceived value has a positive impact on the purchase intention of elderly consumers. H8: Cognitive load has a negative impact on the purchase intention of elderly consumers. H9: Trust has a positive impact on the purchase intention of elderly consumers.
05-11
<think>嗯,用户需要设计一份基于SOR模型的学术问卷,针对老年人在直播电商中的购买决策。首先得确认SOR的三个部分:刺激、有机体、响应。刺激因素可能包括直播中的内容相关性、信息质量、主持人特征,这些需要具体的问题来测量。有机体因素如感知价值、认知负荷、信任,这部分要考虑老年人的认知特点,问题设计要简明易懂。响应因素主要是购买意向,可能需要行为意向的问题。 接下来,每个变量需要对应的量表,可能需要参考已有的文献,比如信息质量用Parasuraman的量表,信任用McKnight的。还要注意老年人的特殊性,比如语言是否简单,是否涉及技术障碍。另外,人口统计部分要包括年龄、教育背景、直播使用频率等。 问题结构方面,可能需要分部分:刺激因素、有机体、响应因素,再加人口统计。每个变量下设置3-5个问题,使用李克特量表。要确保问题清晰,避免引导性。例如,内容相关性的问题可以问直播内容是否符合他们的需求,信息质量涉及准确性、及时性。主持人特征包括专业性、互动能力。 还要考虑认知负荷,可能用Paas的量表,询问老年人是否觉得信息过载。信任方面的问题要涵盖对主播和平台的信任。购买意向的问题要直接,比如是否愿意购买或推荐。 最后,问卷前要有清晰的说明,匿名处理,数据保密,这部分符合学术伦理。测试阶段可能需要先做预调研,确保问题有效性。相关问题可能需要扩展其他理论模型,或比较不同年龄段,或者探讨其他影响因素如促销策略、界面设计的影响。</think>根据Stimulus-Organism-Response(SOR)模型框架,结合老年人直播电商行为特征,设计问卷包含以下结构: **一、问卷说明** - 明确研究目的及数据匿名性 - 标注适用人群(60岁及以上直播电商用户) - 填写预计时间(8-10分钟) **二、刺激因素(Stimulus)** *测量维度:内容相关性、信息质量、主持人特征* 1. 直播推荐的商品/服务与个人需求的匹配程度如何?(1-5李克特量表) - 参考Chen(2022)的"内容适配性"量表 2. 主播提供的商品信息是否具备以下特征?(多选题) $\bullet$ 描述准确性 $\bullet$ 参数完整性 $\bullet$ 更新及时性 3. 主播表现的以下特征对观看决策的影响程度?(语义差异量表) ```spss COMPUTE Host_Professionalism = MEAN(Q3_1,Q3_2,Q3_3). ``` **三、有机体因素(Organism)** *核心变量:感知价值、认知负荷、信任* 4. 观看直播时信息理解难度评估: $$CL = \frac{\sum_{i=1}^{n} MentalEffort_i}{TimeSpent}$$ 参考Paas(1994)认知负荷量表调整 5. 对以下陈述的同意程度:(7点量表) - "认为主播推荐值得信赖" - "相信平台会保障消费者权益" - "觉得商品价格与实际价值相符" **四、响应因素(Response)** *聚焦购买意向和行为* 6. 观看直播后的行为倾向:(多项排序) $\square$ 立即购买 $\square$ 加入购物车 $\square$ 咨询子女 $\square$ 退出页面 **五、人口统计变量** 7. 直播使用模式: - 单次观看时长(<30分钟;30-60分钟;>60分钟) - 常用平台(抖音/淘宝/快手/其他) 8. 技术适应能力: - 支付方式掌握数量 - 退货流程熟悉度 **验证设计要点** 1. 量表信效度检验:Cronbach's α >0.7,AVE >0.5 2. 反向问题设置:每个维度包含1-2道反向计分题 3. 视觉辅助:关键问题添加直播界面截图示例 问卷预测试建议通过焦点小组访谈优化表述方式,确保老年群体能准确理解专业术语。数据分析可采用结构方程模型验证SOR路径: $$PurchaseIntention = \beta_1Stimulus + \beta_2Organism + \epsilon$$
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