东海一览维权
关于朱东海的案件(即综执罚决字[2021]第01085号)
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关于朱东海的案件(即综执罚决字[2021]第01085号)【主题分类】土地城建【发文案号】即综执罚决字[2021]第01085号【处罚日期】2022.08.31【处罚机关】即墨区综合行政执法局【处罚机关类型】综合行政执法局【处罚机关】即墨区综合行政执法局【处罚种类】罚款、没收违法所得、没收非法财物【执法级别】区/县级【执法地域】即墨市【处罚对象】朱东海【处罚对象分类】个人【更新时间】2022.09.20 12:43:58处罚决定即综执罚决字[2021]第01085号书文号案件关于朱东海的案件法定代表人名称当事朱东海工商注册号人统一社会行政处罚的种类罚款信用代码作出即墨区综合行政执法局处罚的机关名称处罚决定日期2022-08-31 00:00:00处罚幅度主要违法事实2022年1月4日，朱东海在即墨区蓝鳌路1555号院内实施搭建活动，院内西侧已建成一层钢结构南北长68米，东西宽24米，建筑面积1632平方米；院内东侧正在搭建一层钢结构南北长43米，东西宽11米，建筑面积473平方米，主体已完工；院内东北侧已建成房屋1处，东西长18米、南北宽6米，一层砖混结构，建筑面积108平方米，以上三处建筑面积共计2213平方米行政处罚依据《中华人民共和国城乡规划法》第六十四条：未取得建设工程规划许可证或者未按照建设工程规划许可证的规定进行建设的，由县级以上地方人民政府城乡规划主管部门责令停止建设；尚可采取改正措施消除对规划实施的影响的，限期改正，处建设工程造价百分之五以上百分之十以下的罚款；无法采取改正措施消除影响的，限期拆除，不能拆除的，没收实物或违法收入，可以并处建设工程造价百分之十以下罚款处罚的履行方式和期限罚款181360.00元北大法宝1985年创始于北京大学法学院，为法律人提供法律法规、司法案例、学术期刊等全类型法律知识服务。
中国船主于学君披露渔船被朝鲜抓扣详情
中国船主于学君披露渔船被朝鲜抓扣详情作者：张珍来源：《读天下》2013年第11期“我是大连的，船被朝鲜劫持了。
现在还没放！”2013年5月18日下午，“辽普渔25222船”船主于学君向记者披露，渔船被朝鲜抓扣详情。
于学君称，5月5日23时“辽普渔25222船”在中国海域泊流时，被朝方船只持枪拖走。
此消息不久被中国驻朝鲜大使馆证实。
被扣16天未遭虐待“2013年5月5日，半夜10点多钟，辽普渔25221的船长姚国锋和辽普渔25222的船长姚国治，用对讲机讲过话，（之后）大概半夜左右，就联系不上了。
” 船主于学君说。
当时辽普渔25222船（包括船长在内有16名渔民）所在位置是在东经123度53分、北纬38度18分，而东经124度才是渔民心目中的两国海上分界线。
这是继去年“5·8朝鲜扣押中国渔民事件”后，再次有中国渔船在此东经线附近作业又被扣押。
于是，姚国锋开始在海面上寻找。
5月6日凌晨4点多钟，在丹东海域上，刚好遇到一艘正在海上放网的木质船。
该船船长告诉姚，看到辽普渔25222号被一艘巡逻艇带走，向东航行，而向东就是朝鲜海域。
之后，于学君接到朝方电话。
被扣渔船船长姚国治在电话中描述：事发当晚，是朝鲜189艇队的一艘机械船，突然接近辽普渔25222号，其上船员持枪，十分蛮横地扣留了渔船。
当记者问及朝方是否给予明确的扣押原因时，船主于学君回答：“没有，就是跟我要钱。
什么也没有。
说把钱打了，就放船。
”朝方先要120万元人民币，后来要60万。
于学君表示，60万太多，少点还能凑合一下，结果这样反而让朝鲜方面更加积极地越界扣押中国渔船。
朝方要求于学君必须在5月19日12点之前将赎金打到辽宁丹东某家公司的账号上。
若船主不交赎金就将没收船只，船员全部遣送回国。
5月19日21点左右，船主于学君又接到朝鲜方面的电话。
朝鲜继续要钱，仍无放人打算，还称最迟期限为20日下午5点。
5月21日终于传来好消息，外交部发言人洪磊在当天例行记者会说，“辽普渔25222”号渔船已与其他中方渔船在海上会合，将继续在海上进行捕捞作业。
生态环境部:打好长江口——杭州湾等重点海域综合治理攻坚战
[8]蔡鼎阳，林亮，唐涌涛。基于三维激光扫描的方家山核岛厂房SAR
气源管线改造的应用[.科技视界，2019（13）:83-86.
[9]吴保，马朋召，白文倩，等.不同扫描策略下316L/AISI304激光 熔覆过程中温度场一应力场的数值模拟[。中国激光，2021，
更广，对海洋生态环境质量改善的要求也更高，治理的重点难
点主要体现在以下三个方面： 一是陆海统筹的污染防治还需深化，重点是要把好人海排
污口和人海河流这两道关键人海“闸口”；二是海洋生态保护
修复仍需久久为功，重点是要协同推进重要海洋生物栖息地和 典型海洋生态系统这两类重大生态修复任务；三是治理监管
的长效机制还需建立健全，特别是要加强陆海统筹的生态环境 治理制度建设。
间隙异物检测研究[].机车电传动，2020（3）：3
[2]陈涛，翟超，范鹏程，等.回弹法及三维激光扫描技术在地铁线
路结构健康检测中的应用[，无损检测，2018，40（7):6. [3]杜荣武，翁顺，曾铁梅，等．基于移动三维激光扫描的地铁隧道
结构监测方法[门土木工程与管理学报，2020.37（1）:7.
[4]倪飞，王浩丞，杨艺卓，等.三维激光扫描技术在高速公路运行 非接触式变形监测中的应用[.测绘通报，2021（3）：3.
[5]李瑶，祥一，吴勇生，等.基于三维激光扫描技术的超欠挖算法
在隧道开挖中的应用[。铁道标准设计，2021.65（10）：5.
[6]李东海，三维激光扫描技术在1：1万地质灾害详细调查中的 应用—以河北邢台峡谷群国家地质公园为例[测绘通报， 2019（7）:3.
海南省公布第五批民生领域案件查办“铁拳”行动典型案例
食安观察F dsafety食品安全导刊 2023年11月（下）海南省公布第五批民生领域案件查办“铁拳”行动典型案例□ 文 韦昌惠今年以来，海南省各级市场监管部门深入贯彻落实国家市场监管总局查办“铁拳”行动的部署要求，聚焦人民群众饮食健康安全，查处了一批食品安全违法犯罪案件。
海南省市场监督管理局对2023年民生领域案件查办“铁拳”行动典型案例（第五批）进行公布。
一、昌江黎族自治县综合行政执法局查处昌江石碌小武成人用品店销售有毒有害食品案2023年9月25日，昌江黎族自治县综合行政执法局依法查处昌江石碌小武成人用品店销售有毒有害食品的违法行为，当事人的行为涉嫌构成犯罪，案件已移送公安机关。
2023年7月14日，昌江黎族自治县综合行政执法局联合昌江黎族自治县市场监管局检查昌江石碌小武成人用品店，在该店销售柜台发现8盒“德国黑金刚”、8盒“伟哥性动力”、12盒“牛鞭补肾王”、10盒“蓝钻伟哥”、12盒“红钻伟哥”、3盒“至尊宝植物壮根”、3盒“美国蓝金”、11瓶“老中医”、5瓶“野生黑蚂蚁”、6瓶“鹿茸回春”、4瓶“冬虫夏草”、25盒“每粒坚”、5盒“藏秘肾宝片”、3盒“红钻伟哥”（标识：美国辉腾生物科技有限公司监制）。
执法人员对以上产品进行抽样送检，经检测，以上产品均检出西地那非或他达拉非。
以上产品货值金额1 275元，违法所得106元。
当事人的行为违反了《中华人民共和国食品安全法》第三十四条第一项的规定，已涉嫌构成犯罪，昌江黎族自治县综合行政执法局依法将该案移送公安机关处理，公安机关已立案侦查。
二、东方市综合行政执法局查处东方东海罗育成人用品店销售有毒有害食品案2023年5月26日，东方市综合行政执法局依法查处东方东海罗育成人用品店经营有毒有害食品的违法行为，当事人的行为涉嫌构成犯罪，案件已移送公安机关。
2023年3月16日，东方市市场监督管理局检查东方东海罗育成人用品店，在当事人柜台发现4盒“天下第一棒”，并予以扣押。
乱刀弑母杀妹
鏞記母公司清盤案昨進入第3日審訊，大哥甘健成供稱，曾建議父親甘穗煇在分配公司股權時保留大股東身份，不要給兒子太多股權，以免被他們站在同一陣線「對付」。
甘健成指鏞記是家族式經營的生意，不會有人玩弄權力，不過其父甘穗煇1974年成立鏞記酒家有限公司時，曾打算把60%股權平均分給他與二弟及另一房妻子所生的兒子琨華，但他建議父親應該只把30%股權平均分配給他們，父親則持有70%並保留大股東身份。
終在股東大會上，以大比數否決父親的建議。
甘健成解釋，當年二弟琨禮剛在台灣完成學業返港，合作時間不長，琨華又是另一房人，兄弟間未夠了解，因此他才有此擔心，認為父親應該未雨綢繆。
二弟質疑證供背道而馳甘健成承認曾在書面供詞中表示，父親當年處於半退休狀態時，在酒家業務上仍操生殺大權。
代表二弟甘琨禮的資深大律師John Bleach，質疑其證供與他宣稱酒家是合夥生意，大家平起平坐的說法背道而馳。
甘健成解釋父親確有生殺大權，但從未行使此權力，他早已將酒家生意放手交給3名兒子打理，書面供詞所寫的只是尊重父親的描述。
甘健成謂，自從他2010年提出清盤呈請後，酒家的生意並無受影響，員工流失率亦無異常情況，他與員工開會只為穩定軍心。
堅稱集團整體不能斬件二弟一方認為母公司Yung Kee Holdings Limited是海外註冊公司，唯一資產是子公司Long Yau Limited的100%股份，在香港無實際業務，故此法庭無司法管轄權下令大哥或二弟收購對方的股份。
但甘健成認為母公司的業務是投資控股公司，辦公室在中環鏞記大廈5樓，他又將母公司比喻為整個鏞記集團的「大腦」，Long Yau Limited是「身體」，鏞記酒家是「腳」，用來賺錢，整個集團是一個整體，不能斬件。
另外，庭上日前披露，甘家三妹甘美玲在書面供詞批評大哥兩子崇軒及崇轅工作懶散又欠幹勁，2人昨在庭外表示並非事實，崇軒指姑姐移居美國夏威夷，甚少回港，最近見她已是2、3年前，因此她不會有機會到酒家觀察他們的工作表現。
%81东海温盐跃层的分布特征及其季节变化
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关于我国近海跃层的研究， 推毛汉礼等人‘ 应首 ｒ 基于 １ ８ ９９ ９ 一］５ 年全国海洋普查资 ５ 料． 对黄、 东海温、 密跃层的分布和消长变化规律详细地阐述。但这些资料多数是用颠 盐、 倒温度表观测， 少数用 Ｂ Ｔ观测， 并且仅限于 １９ ２０ Ｅ以西。之后，黄海温跃层课题组” ‘ ・ 收集 了 １ ７ ９５ ９ －１７ 年间颠倒温度计观测记录， ５ 分析了 黄海跃层特征值的分布规律。蓝淑芳 等Ｄ又 １ ５ ９。 ： 用 ９ －１８ 年间Ｂ 资料统计分析了 黄、 ７ Ｔ 渤、 东海的水温垂直结构及其时空变化 特征。赵保仁［利用 ］８ 年前所积累的部分Ｃ Ｄ资料， ｘ ７ ９６ Ｔ 探讨了夏季渤、 黄海及东海北部 强温跃层的纂本特征及形成机制， ＣＤ的调查资料仅限于东海以 但 Ｔ 北。 本文利用 １ ６ ９ － ９ １９ 年所获得黄、 ９９ 东海域 的 Ｃ Ｄ资料， Ｔ 在空间覆盖、 时间跨度和资料质量方面均是近期 ＣＤ调查中少有的 Ｔ
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陈伟星、李笑来名誉权纠纷二审民事判决书
陈伟星、李笑来名誉权纠纷二审民事判决书【案由】民事人格权纠纷人格权纠纷名誉权纠纷【审理法院】浙江省杭州市中级人民法院【审理法院】浙江省杭州市中级人民法院【审结日期】2020.09.14【案件字号】(2020)浙01民终2339号【审理程序】二审【审理法官】石清荣韦薇孔文超【审理法官】石清荣韦薇孔文超【文书类型】判决书【当事人】陈伟星;李笑来【当事人】陈伟星李笑来【当事人-个人】陈伟星李笑来【代理律师/律所】廖文燕、吴壮浙江六和律师事务所;方皛、卢文杰上海君悦律师事务所【代理律师/律所】廖文燕、吴壮浙江六和律师事务所方皛、卢文杰上海君悦律师事务所【代理律师】廖文燕、吴壮方皛、卢文杰【代理律所】浙江六和律师事务所上海君悦律师事务所【法院级别】中级人民法院【字号名称】民终字【原告】陈伟星【被告】李笑来【本院观点】李笑来对该段视频确为其直播内容并无异议，本院对该证据的真实性予以认可，但该视频仅为直播片段的截取，无法反映直播的完整内容，难以体现直播者真实的意思表示，故对陈伟星提交该证据的证明目的本院不予认可。
民事主体享有名誉权，任何人不得对他人的名誉权加以侵害。
陈伟星的案涉言论主要围绕李笑来在虚拟币领域的投融资行为展开，对于虚拟币投融资这一具有一定知识门槛的社会新生事物，客观上确能起到一定的舆论监督的作用，但舆论监督亦应遵循一定的边界，即行为人应对言论的内容尽到合理的核实义务，且不得使用侮辱性言辞贬损他人的名誉。
陈伟星作为具有一定社会知名度的主体，应当预见到其言论内容在网络中可能引发的效应。
【权责关键词】社会公共利益委托代理过错停止侵害排除妨碍消除危险返还财产恢复原状消除影响恢复名誉赔礼道歉自认合法性质证证明责任（举证责任）诉讼请求维持原判【指导案例标记】0【指导案例排序】0【本院查明】经审理，本院查明的事实与原审法院查明的事实一致。
【本院认为】本院认为，民事主体享有名誉权，任何人不得对他人的名誉权加以侵害。
张永福、海南鼎顶旅游文化股份有限公司民间借贷纠纷二审民事判决书
张永福、海南鼎顶旅游文化股份有限公司民间借贷纠纷二审民事判决书【案由】民事合同、无因管理、不当得利纠纷合同纠纷借款合同纠纷民间借贷纠纷【审理法院】最高人民法院【审理法院】最高人民法院【审结日期】2020.11.27【案件字号】(2020)最高法民终742号【审理程序】二审【审理法官】马成波葛洪涛马岚【审理法官】马成波葛洪涛马岚【文书类型】判决书【当事人】张永福;海南鼎顶旅游文化股份有限公司;蒋明利;海南鼎顶实业集团有限公司;海南鼎顶网络科技股份有限公司;海南鼎顶建材股份有限公司;澄迈鼎顶矿业有限公司;海南金砂实业有限公司;海南中水渔业投资有限公司;梁锡华【当事人】张永福海南鼎顶旅游文化股份有限公司蒋明利海南鼎顶实业集团有限公司海南鼎顶网络科技股份有限公司海南鼎顶建材股份有限公司澄迈鼎顶矿业有限公司海南金砂实业有限公司海南中水渔业投资有限公司梁锡华【当事人-个人】张永福蒋明利梁锡华【当事人-公司】海南鼎顶旅游文化股份有限公司海南鼎顶实业集团有限公司海南鼎顶网络科技股份有限公司海南鼎顶建材股份有限公司澄迈鼎顶矿业有限公司海南金砂实业有限公司海南中水渔业投资有限公司【代理律师/律所】辛渝重庆恒庆律师事务所;沙骏北京市金杜(广州)律师事务所;王东北京市中银(重庆)律师事务所;戴勤勇重庆百君律师事务所【代理律师/律所】辛渝重庆恒庆律师事务所沙骏北京市金杜(广州)律师事务所王东北京市中银(重庆)律师事务所戴勤勇重庆百君律师事务所【代理律师】辛渝沙骏王东戴勤勇【代理律所】重庆恒庆律师事务所北京市金杜(广州)律师事务所北京市中银(重庆)律师事务所重庆百君律师事务所【法院级别】最高人民法院【原告】张永福;海南鼎顶旅游文化股份有限公司【被告】蒋明利;海南鼎顶实业集团有限公司;海南鼎顶网络科技股份有限公司;海南鼎顶建材股份有限公司;澄迈鼎顶矿【本院观点】本案二审争议焦点为：一、2016年12月12日《借款合同》效力和履行问题；二、出借金额、还款金额及尚欠款金额认定问题；三、鼎顶旅游公司是否应作为债的加入承担案涉借款合同项下的相关债务问题；四、鼎顶网络公司、鼎顶实业公司、鼎顶建材公司、金砂公司、澄迈鼎顶公司应否作为担保人对本案全部债务承担连带责任问题；五、一审法院程序是否违法问题。
Limnol Oceanogr
Modeling the irradiance dependency of the quantum efficiency of photosynthesisGreg M. Silsbe *and Jacco C. KromkampRoyal Netherlands Institute for Sea Research (NIOZ-YE). Postbus 140, 4400 AC. Yerseke, NetherlandsAbstractMeasures of the quantum efficiency of photosynthesis (f PSII ) across an irradiance (E) gradient are an increas-ingly common physiological assay and alternative to traditional photosynthetic-irradiance (PE) assays.Routinely, the analysis and interpretation of these data are analogous to PE measurements. Relative electron transport rates (rETR = E ¥f PSII ) are computed and fit to a PE curve to retrieve physiologically meaningful PE parameters. This widespread approach is statistically flawed as the response variable (rETR) is explicitly depend-ent on the predictor variable (E). Alternatively the E-dependency of f PSII can be modeled directly while retain-ing the desired PE parameters by normalizing a given PE model to E. This manuscript presents a robust analy-sis in support of this alternative procedure. First, we demonstrate that scaling f PSII to rETR unnecessarily ampli-fies the measurement error of f PSII and using a Monte-Carlo analysis on synthetic data induces significantly higher uncertainty in computed PE parameters relative to modeling the E-dependency of f PSII directly. Next a large dataset is simultaneously fitted to four PE models implemented in their original and E-normalized forms.Four statistical criteria used to evaluate the efficacy of nonlinear models demonstrate improved model fits and more precise PE parameters when data are modeled as E-dependent changes in f PSII . The analysis presented in this manuscript clearly demonstrates that modeling the E-dependency of f PSII directly should be the norm for interpreting active fluorescence measures.LIMNOLOGYandIn this article, we demonstrate that modeling FLC data as f PSII (E) is statistically more robust than modeling rETR(E). First,we demonstrate how the measurement error of rETR increases with E and document how this induces significant uncertainty in computed PE parameters. Next, a large FLC dataset is intro-duced and simultaneously fitted to four commonly cited PE models implemented in their original and E-normalized forms (Table 1). Four statistical criteria used to evaluate the efficacy of nonlinear models (Motulsky and Ransnas 1987) are pre-sented for each model. PE parameters (presented in Table 1)are evaluated for their level of significance (P value) and rela-tive standard error (RSE , equal to the standard error normal-ized to parameter estimate). Model fits are evaluated for their root mean square value (RMS , equal to the square root of the sum of square residuals normalized to degrees of freedom in the regression), and the distribution of model residuals (observed - predicted) as they vary with E.Materials and proceduresStudy site and samplingFluorescence light curve data were performed on naturalphytoplankton communities across a seasonal and nutritional gradient in the western Wadden Sea and nearby Lake Ijsmeer,Netherlands. Water samples were collected every hour over a diurnal cycle at two discrete depths during four research cruises in February, March, April, and September 2010 at 6 different stations. Table 2 lists the mean and the range in water temper-ature, Chlorophyll a , dissolved inorganic nitrogen (DIN), solu-ble reactive phosphorus,and silicate concentrations along with the number of FLCs acquired during each cruise.Fluorescence light curve measurementsEach FLC was measured with a FAST act single turnover fluo-rometer (Chelsea Technologies Group), which exposes a water sample to a set of user-defined irradiance levels applied incre-mentally. Here phytoplankton were incrementally exposed to 10 irradiance levels (range 5 to 1071 µmol m –2s –1) at 30-s inter-vals. A temperature controlled water bath attached to the FAS-T act ensured that water sample remained at in situ tempera-tures. Fluorescence induction curves were user-defined and programmed to consist of 100 µs flashlets applied every 2 µs followed by 20 relaxation flashlets applied every 49 µs (Kolber et al. 1998). Five induction curves spaced 100 ms apart wereFig. 1.An FLC dataset simultaneously modeled as A) rETR(E) and B) F PSII(E) using Webb et al. (1974). The E axis in B) is log-transformed for clarity. PEparameters are shown and differ between modeling approaches.averaged by the Chelsea FastPro software into a single com-posite induction curve; this was repeated every 5 s allowing for approximately 6 composite curves per 30-s irradiance level.The FastPro software models fluorescence (F n ) as a function of the number of closed reaction centers (C n ), where n is the exci-tation flashlet (n = 1,2…100), using the Kolber et al. (1998)model shown in Eqs. 1 and 2. This model derives four fluores-cence parameters in actinic light (F’, F M ¢, s PSII ¢, p), where F ¢and F M ¢are the minimum and maximum fluorescence yield in actinic light, s PSII ¢(nm 2) is the effective absorption cross sec-tion in actinic light, and p is a dimensionless integer repre-senting the degree of connectivity between photosystem II reaction centers. From these parameters f PSII is derived as (F M ¢-F ¢)/F M ¢. At each irradiance, six measures of f PSII were averaged into a single composite value. Background fluorescence was determined by measuring sample filtrate (filtered through a Whatman GF/F filter) in the FAST act and subtracted from all fluorescence measurements following Suggett et al. (2006).C n = C n-1+ s PSII ¢¥(1 – C n-1) ¥(1 – p ¥C n-1)–1(1)F n = F ¢+ (F M ’ - F’) ¥C n ¥(1 – p) ¥(1 – p ¥C n )–1(2)The FastPro software does not quantify the standard errors of the various fluorescence parameters derived from fitting induction curves to the Kolber et al. (1998) model. Rather, the standard error that is given in the software refers to the stan-dard error of the linear regression of the last 24 flashlets used to establish the initial value for F M ¢(Kevin Oxborough m.). To estimate the standard error of f PSII across FLCs,induction curves (n = 4578) from a subset of FLCs (n = 77)were fit to the Kolber et al. model (1998) using the open source statistical program R (R Development Core Team 2011).Statistical methodsNonlinear least squares regression of FLC data were per-formed using the ‘modFit’ function in the R package ‘FME’specifically designed for mathematical modeling of environ-mental data (Soetaert and Petzoldt 2010). The versatility of this function allows the user to select different numerical algo-rithms and easily extract relevant statistical parameters. In this study, numerical fitting of FLC data were performed using the Nelder-Mead algorithm. This algorithm was selected as it yielded more statistically significant model parameters (Table1) relative to other algorithms tested (Levenberg-Marquardt,Gauss-Newton, and Port). A sample script written in the open-source statistical software R that invokes the statistical tests used here accompanies this manuscript as Web Appendix I.AssessmentMeasurement precision of PSII quantum efficiency and rel-ative electron transportFig.2 illustrates how measurement precision of f PSII and rETR vary with E. Panel A shows representative single turnover induction curves in the presence of low and high light (5 and 1071 µmol m –2s –1). The high light induction curve is repeated in Panel B with the axis magnified. For each induction curve the statistical fit of F ¢, F M ¢,and f PSII are stated ± their respective standard error (SE ) along with the root mean square (RMS ),taken here as a proxy of induction curve noise. Panel C pres-ents boxplots of RMS values and the SE of f PSII and rETR derived from a large set induction curves (n = 4578) across an E gradient. As RMS values show no E-dependency (n = 4578, P > 0.10), the analysis in Fig. 2 demonstrates that induction curve noise is independent of E. However the relationship between E and the SE of f PSII is biphasic. No significant E-dependent changes in the SE of f PSII occur below 150 µmol m –2s –1, but above this irradiance, the SE of f PSII increases slightly but significantly with E (P < 0.05). Examining RMS values and SE of f PSII together, we interpret the measurement precision of f PSII as being largely driven by the redox state of photosystem II. Below 150 µmol photons m –2s –1most PSII reaction centers remain oxidized (inferred from E K measures below) and vari-able fluorescence (F M ¢- F’) is large relative to inherent noise.Above 150 µmol m –2s –1the progressive photochemical reduc-tion of PSII reaction centers flattens induction curves (F M ¢and F ¢converge) and variable fluorescence decreases relative to noise, thereby increasing the SE of f PSII .Fig. 2C also documents the SE of rETR across an E gradient (n = 4578). The SE of rETR is equal to the product of the SE of f PSII and its attendant E value (where we have assumed the SE of E is constant). As E proportionality amplifies the SE of f PSII in this calculation, the SE of rETR significantly increases with E (n = 4578, P < 0.05). The analysis presented in Fig. 2 is spe-cific to single turnover fluorometers that allow error estimates to be derived from induction curves. Many FLCs are derived from multiple turnover fluorometers (e.g., PAM fluorometers)in which single measures of F M ¢and F ¢preclude any statisticalerror analysis. As fluorometers giving a multiple turnover flashmeasure FM ¢with modulated light, such measures may have ahigher signal to noise ratio at a given irradiance than single turnover fluorometers (Kromkamp and Forster 2003). But whatever active fluorometer is used, the convergence of F ¢and FM ¢caused by reduction of PSII reaction centers makesmeasures of fPSII prone to relatively large errors in high light,as experienced by most users of active fluorometers. Thus the measurement precision of FLC data runs counter to traditional PE assays (e.g., 14C) where the most precise measures are typi-cally measured under high E.Fig.3 illustrates the effects of rETR and fPSII measurementprecision on PE parameters. Shown in Fig. 3A and 3B, a set of synthetic FLCs (n = 1000) were derived at ten E levels using the original and E-normalized PE model of Webb et al. (1974),respectively. At each E level, a random rETR or fPSII value wasgenerated from a normal distribution with a mean value derived from preset PE parameters (a= 0.5, EK= 200, and PM= 100) and with a standard deviation equivalent to their respec-tive standard error taken from Fig. 2C (and shown as vertical error bars in Fig. 3A and B). Each set of randomly generated rETR and fPSIImeasures were then refit to the model of Webb et al. (1974). Fig. 3C, D, and E show boxplots of the PE param-eters derived from these fits. Mean PE parameters for rETR(E) and fPSII(E) models were not statistically different than their original value (t-test,P < 0.05). However Fig. 3 clearly shows that, relative to the synthetic fPSIIdata, E-dependent increases in the SE of rETR induce significantly larger variance in com-puted PE model parameters (F-test, P < 0.05).Model performanceTable 3 summarizes statistical and model parameters derived by fitting the models listed in Table 1 to a large set of FLC measurements (n = 412). In this section, we ignore differences in the measurement precision of rETR and fPSIIasFig. 2.A) Single turnover induction curves from a single sample with actinic irradiances of 5 and 1071 µmol m–2s–1. The high light induction curve is shown again in B) with the axis magnified. The solid and dashed lines in each panel represent the Kolber et al. (1998) model fit and its 95% confidence intervals, though these are barely visible in Panel A. For each panel, the root mean square (RMS) of the fit is shown with resultant values of F¢, F¢M and F PSII ± their respective standard error (SE). C) Boxplots of RMS values and the SE of F PSII and rETR across an E gradient derived from a large set induc-tion curves (n= 4578) from a subset of FLCs (n= 77). The upper and lower boundary of each box is the 75th and 25th percentile, whiskers represent the minimum and maximum values, and the solid line is the median.Fig. 3.Sensitivity analysis showing synthetic FLCs (n= 1000) derived from Webb et al. (1974) where a= 0.5 and EK = 200 µmol m–2s–1, and the model is implemented in A) its original form and B) its E-normalized form. Grey lines are the synthetic curves generated from normal distributions with computed mean values shown as circles, and standard deviations equivalent to the respective standard errors of rETR(E) and F PSII(E) taken from Fig. 2C and shown here as ver-tical error bars. C-E) Boxplots, as described in Fig. 2, showing the distribution of computed PE parameters from the synthetic rETR(E) and F PSII(E) curves. Table 3.PE models applied to a FLC dataset (n = 412) in their original and E-normalized form. Next to each model n states the num-ber of FLC fits with statistically significant (P < 0.05) model parameters. Mean model parameters are given with their relative standard error (RSE) in brackets. The mean model root mean square (RMS) is computed only for FLCs that yielded statistically significant model parameters (P < 0.05). An *denotes model parameters that are significantly different (P < 0.05) between rETR(E) and fPSII(E) models. PE model n PE Parameters (RSE)Model RMS(10–2)Webb et al. 1974a EK P MrETR(E)4120.47 (5.2%)294 (7.2%)132 (17.2%) 2.8 fPSII(E)4120.47 (1.3%)270 (5.0%)128 (11.4%) 1.0Jassby and Platt 1976a EK P MrETR(E)4120.37 (6.7%)*335 (8.9%)*123 (26.0%)* 5.7fPSII(E)4120.44 (2.2%)*233 (8.3%)*103 (19.4%)* 2.0Platt et al. 1980a b PS rETR(E)1670.49 (2.4%)* 2 (8.5%)99 (3.5%)* 2.4 fPSII(E)770.48 (0.7%)*0 (4.0%)108 (6.5%)* 1.0Eilers and Peeters 1988a EOPT P MrETR(E)3950.46 (10.7%)*918 (16.7%)122 (2.8%) 3.7 fPSII(E)4090.47 (1.6%)*815 (14.7%)115 (5.0%) 1.1such measures are rarely computed. Next to each model the number of FLCs that yielded statistically significant (P < 0.05) PE parameters is stated. Only models that do not sim-ulate photoinhibition (Webb et al. 1974; Jassby and Platt 1976) yielded statistically significant model parameters across all FLCs. The short duration in which phytoplankton are exposed to saturating light in the FLC protocol used here likely minimizes photoinhibitory processes relative to longer traditional photosynthetic assays. Mean coefficients of deter-mination (r2), commonly used to test the efficacy of a given model, exceed 0.97 for all models (data not shown). Despite high r2values throughout, Table 3 underscores the validity ofdirectly modeling fPSII (E). Across all PE models, fitting data asf PSII (E) yielded significantly smaller RMS values than fittingdata as rETR(E) (paired t test, P < 0.05). Table 3 also demon-strates that normalizing a PE model to E also yields system-atic differences in model parameters. In Table 3, an asterisk indicates that computed PE parameters were significantly different (Mann-Whitney test, P < 0.05) when data for agiven model is fit as rETR(E) or fPSII (E). In this dataset EKwasthe most sensitive model parameter to E normalization. Strikingly when the hyperbolic tangent model (Jassby and Platt 1976) was implemented to model rETR(E), the resultantmean EK was, on average, 1.42-fold higher then when mod-eled as fPSII (E). Fig.4 presents boxplots of model residuals(observed - predicted) as they vary with E. To compare resid-uals between the original and E-normalized models listed in Table 3, all residuals computed for rETR(E) models are nor-malized to E. Thus Fig. 4 explicitly examines each givenmodel’s ability to predict E-dependent changes in fPSII . As inTable 3, only the residuals of FLCs that yielded statistically significant model parameters are shown. In Fig. 4, an aster-isk above the 95% outlier indicates that, at a given E, the absolute value of the residual mean exceeds the nominal fPSII SE(taken here as 0.01 from Fig. 2C, t test or Wilcox test, P <0.05). Across all ten E levels, only the fPSII (E) implementedmodels of Webb et al. (1974) and Platt et al. (1980) yieldedmean fPSII residuals significantly smaller than the assumedmeasurement error. All other models indicate some E-dependency in model residuals. Fig. 4 also helps visualize persistent differences in PE parameters listed in Table 3. For example, modeling rETR(E) with Jassby and Platt (1976) and Platt et al. (1980) yielded, on average, the smallest and largest estimates of a,respectively. Accordingly, Fig. 4demonstrates that for these two respective models fPSII resid-uals are, on average, greater and less than 0 in the presence of low light. Inspection of model residuals also helps recon-cile disparate estimates of EK . Fig. 4 clearly shows that thef PSII (E) and rETR(E) implementation of Jassby and Platt(1976) underestimates and overestimates fPSII respectivelyacross the range of typical EK values. Table 3 shows the meanE K values derived from the fPSII(E) implemented version ofWebb et al. (1974) fall between the two extremes of Jassby and Platt (1976), accordingly the residuals with this model are close to 0 across the range of typical EKvalues. Regardless of PE model, Fig. 4 further illustrates that modeling FLC data as fPSII(E) yields smaller and less variable residuals than the traditional rETR(E) approach.Model selectionOverall fitting fPSII(E) with the model of Webb et al. (1974) is deemed the most appropriate model for this dataset. Only the models of Webb et al. (1974) and Jassby and Platt (1976) yielded statistically significant model parameters for all FLCs (n = 412). Of these two models, when fPSII(E) is fit to Webb et al. (1974), the relative standard error of model parameters are the lowest, and fPSIIresiduals did not exceed the measurement error. Moreover the mean model RMS was significantly smaller than any other model (paired t test, P < 0.01), with the excep-tion of fPSII(E) fitted with Eilers and Peeters (1988). This is not to say that the exponential model should be universally implemented for FLC data, rather we propose these statistical tests should be considered when selecting an appropriate model. A sample script written in the open-source statistical software R that invokes the statistical tests used here accom-panies this manuscript as Web Appendix I.DiscussionWhether as a tool to examine photosynthetic physiology or compute rates of phytoplankton production, derivation of photosynthetic parameters (a, EK, PM) from measures of active fluorescence are becoming increasingly common (Suggett et al. 2011). However, the current widespread prac-tice by which measures of rETR are modeled as a function of E, analogous to classic PE methodology, violates a key sta-tistical requirement of nonlinear regression. This statistical violation can be avoided by normalizing a given PE model to E such that fPSII(E) is modeled directly (Laws et al. 2002). The equivalency of fPSII(E) models to traditional PE models has long been recognized (Kiefer and Mitchell 1983), yet infrequently implemented (Smyth et al. 2004). Indeed the accompanying software of Walz fluorometers (WinControl 3.0, Heinz Walz GmbH, Effeltrich GE) currently models FLC data as E-dependent changes in rETR. Here we have used a series of statistical tests that clearly demonstrate that mod-eling the E-dependency of fPSIIavoids unnecessary amplifi-cation of fPSIIerrors and yields more accurate and precise PE parameters. This study definitively demonstrates that mod-eling fPSII(E) directly should be the new norm when inter-preting FLC data.The statistical approach taken here can also be applied to traditional PE measures. For example, Jassby and Platt (1976) stated “…we frequently found that the final parameter esti-mates for a were far outside the range of published values and grossly inconsistent with estimates of a made by subjective inspection of the data taken at low light levels.” Our imple-mentation of their model is consistent with this observation. The statistical tests used in this study help identify systematic biases in PE parameters (Frenette et al. 1993).ReferencesAalderink, R. H., and R. Jovin. 1997. Estimation of the photo-synthesis/irradiance (P/I) curve parameters from light and dark bottle experiments. J. Plankton Res. 19:1713-1742 [doi:10.1093/plankt/19.11.1713].Eilers, P. H. C., and J. C. H. Peeters. 1988. A model for the rela-tionship between light intensity and the rate of photosyn-thesis in phytoplankton. Ecol. Model. 42:199-215 [doi:10.1016/0304-3800(88)90057-9].Frenette, J. J., S. Demers, L. Legendre, and J. Dodson. 1993.Lake of agreement among models for estimating the pho-Fig. 4.Boxplots, as described in Fig. 2, showing the distribution of F PSII residuals at 10 irradiance levels for the models and data listed in Table 3. White and grey boxplots correspond to residuals derived from rETR(E) and F PSII(E) models, respectively. An *above a given box indicates that the absolute value of the residual mean is significantly larger than 0.01 (P< 0.05). Note different y-axis scales between panels.tosynthetic parameters. Limnol. Oceanogr. 38(3):679-687 [doi:10.4319/lo.1993.38.3.0679].Jassby, A. D., and T. Platt. 1976. Mathematical formulation of the relationship between photosynthesis and light for phy-toplankton. Limnol. Oceanogr. 21:540-547 [doi:10.4319/ lo.1976.21.4.0540].Kiefer, D. A., and B. G. Mitchell. 1983. A simple, steady state description of phytoplankton growth based on absorption cross-section and quantum efficiency. Limnol. 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Newton, and D. Starr. 1974. Carbon dioxide exchange of Alnus rubra: A mathematical model. Oecologia 17:281-291 [doi:10.1007/BF00345747].White, A. J., and C. Critchley. 1999. Rapid light curves: A new fluorescence method to assess the state of the photosyn-thetic apparatus. Photosynth. Res. 59:63-72 [doi:10.1023/ A:1006188004189].Submitted 15 December 2011Revised 22 May 2012Accepted 6 June 2012。
俞建江使用禁用的渔具、捕捞方法和小于最小网目尺寸的网具进行捕捞案(杭农（渔业）罚〔2021〕10号)
俞建江使用禁用的渔具、捕捞方法和小于最小网目尺寸的网具进行捕捞案(杭农（渔业）罚〔2021〕10号)【主题分类】农林渔牧【发文案号】杭农（渔业）罚〔2021〕10号【处罚依据】中华人民共和国渔业法(2013修正)21876300000中华人民共和国渔业法(2013修正)218763230000中华人民共和国渔业法(2013修正)218763300000渔业捕捞许可管理规定(2022修订)511433600000渔业捕捞许可管理规定(2022修订)5114336210000中华人民共和国渔业法(2013修正)218763410000【处罚日期】2022.01.14【处罚机关类型】农业农村部/厅/局【处罚机关】【浙江省杭州市】浙江省杭州市农业农村局【处罚种类】罚款、没收违法所得、没收非法财物【执法级别】市级【执法地域】杭州市【处罚对象】俞建江【处罚对象分类】个人【更新时间】2022.07.28 17:18:30处罚名俞建江使用禁用的渔具、捕捞方法和小于最小网目尺寸的网具进行捕捞案称行政处罚决定杭农（渔业）罚〔2021〕10号书文号被处罚俞建江对象处罚结果2月25日，本机关与杭州市公安局钱塘新区分局在联合执法过程中发现，当事人在江东大桥上游约1公里钱塘江江面驾驶钢质机动船1艘，使用张网6张从事鳗苗捕捞作业，捕获鳗苗经本机关执法人员清点为89尾。
其行为违反了《中华人民共和国渔业法》第二十三条和第三十条，《渔业捕捞许可管理规定》第二十一条、《浙江省渔港渔业船舶管理条例》第三十三条的规定。
依据《浙江省渔港渔业船舶管理条例》第四十三条处以没收船舶的行政处罚；按照《中华人民共和国渔业法》第四十一条处以没收渔获物和违法所得，罚款5500元，没收渔具的行政处罚。
鉴于渔获物鳗苗已放生，渔具及违法所得已由杭州市萧山区人民检察院依法予以没收，本机关在责令当事人立即改正上述违法行为的同时，作出如下行政处罚：1、罚款人民币伍仟伍佰圆整（￥：5500元）；2、没收钢质机动船舶壹艘。
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东海一览品牌维权
关于最近某些人在互联网上不具名的捏造事实、散布谣言，玷污东海一览公司和产
品，给我公司带来了很大负面冲击及影响，本公司深表震惊和痛心，为此我公司特针对相
关问题严正声明如下：
一、广东东海一览教育文化传播有限公司注册成立于2005年4月，注册资本1000
万元，是广东教育学会校本课程专业委员会常务理事单位，十年如一日专注于速读记忆训
练系统的研发和推广，相继被广东教育学会、国家教育部课题组列为科研规划课题、并荣
获中国企业教育百强等殊荣。公司从创立至今广受社会各界的关心与支持，一直严格遵守
国家法律、法规，守法经营，从未受到国家司法、行政部门的追究和处分。
二、在当今之商业中国，我们选择阳光的、积极向上的教育事业，在广东省这片土
地上兢兢业业的耕耘了十年，贡献着我们的社会责任和义务。公司董事长更是热衷公益事
业，特别重视回馈社会，多年来积极参加助残、扶贫、敬老、拥军等公益活动，在地方拥
有极高的口碑和声望。东海一览公司从来都是从善如流、闻过则喜、虚心接受。令人遗憾
的是，在企业用人和发展的过程中我们也会遇人不淑、让人感叹商业和人心之复杂。对于
网络上部分歪曲捏造事实，混淆视听、转移视线的恶意中伤和幕后有组织有计划的对公司
及产品进行污蔑、对代理商进行煽动的行为，给东海一览的品牌形象与市场运营都带来了
极大的损害。令人欣慰的是，部分被煽动的代理商及时察觉，主动向公司举报，才使得真
相浮出水面。公司今天发此声明，不仅是充分证明公司珍惜品牌声誉不希望被恶意中伤，
更是对这种丧失职业道德、自作聪明的项目合作方表示愤慨和失望。
三、东海一览速读记忆训练系统产品效果定义清晰，不存在任何夸大和虚假宣传，
这点已经过工商管理部门的认真核实。自2002年推广至今，挂牌合作的中小学校案例有、
受益感恩的学员有、跟随公司共同成长了9年之久的代理商有、经常带着孩子亲临公司
表示感谢的老学员家长有；并且得到广州市天河区科技局科技立项、广东省教育学会立项、
广东省教育厅批准设立、国家教育部课题组立项规划；不仅如此，我们还与每位学员签订
效果承诺书，签约承诺学完系统可达到每分钟2000字的阅读速度，如果学完达不到承诺
效果，无条件全额退费。试问：同类公司哪家具备这么好的优势？ 哪家敢承诺的这么细
致？
四、为更好的体恤和支持各代理商，项目自2011年10月启动招商至2012年5月
初，从未收取任何加盟费、保证金之类，近期因售后运营成本的持续增长，三年合约期才
象征性的收取1000元-3000元的品牌使用费或市场保证金，并且在合同里明确写明：合
同期内无串货等恶性行为，合同期满市场保证金将全额退还。扪心自问，公司绝无网络所
言的欺骗加盟费和保证金的意图！ 公司想要的是可持续性的良性合作，将东海一览从一
个地方品牌做成全国性的品牌，
为此，
1、拿货方面不仅给代理商提供极低的进货折扣价；
2、配送方面更是免费配送大量宣传单张、KT板、条幅、各种官方铜牌、包括后期
印刷的《资质成果汇编手册》等；
3、售后运营方面公司更是出工资、报销路费、亲自委派专业的技能讲师和运营经
理上门到代理商当地进行技能培训和运营指导，尽全力协助代理商；4、产品方面为了更
好的协助代理商，公司不仅授人以鱼，更授人以渔，除了训练系统本身，更是不计成本的
免费对所有代理商进行技能特训，额外教会代理商三分钟内背数字、背英文字母、背唐诗
宋词等，以期代理商自身都具备这样的技能，能够现身说法进行展示，增强市场销售力，
实现更好更快的市场推广和运营。
五、我们也希望各代理商能有正确的投资观念和事业观念，不要急功近利，不要追
求短期的投机行为，更不能有不劳而获的的心理。只要怀着爱和感恩的心，准备一双勤奋
的手和一个聪明的脑袋，任何事情都会有多种解决方法，况且东海一览已经经过十年的发
展和积累，必定有大家借鉴和学习的模式和方法，公司也将会组建更优秀的服务团队，更
好的更及时的服务各合作伙伴。 同时，公司希望各位代理商能够尊重自己的判断能力，
客观、理性的辨别事非，不要被别有用心的人制造的谣言和舆论所欺骗和误导；公司将我
们也希望所有媒体和网站能够自觉监控和净化自身内容；对歪曲事实和恶意玷污者，公司
已经委托专业的律师机构依照《中华人民共和国刑法》第221条、第243条的有关规定，
向有关司法机关报案，要求司法机关查清事实，追究违法者的法律责任。
六、关于长春代理商李文学提出的“结题报告”问题的说明：一直以来，我们都是
讲教育部课题组“立项”、“规划”、“批准”、“推广实践”等字眼，并未提过“结题”的说
法；因为我们的强化训练系统保留继续升级的可能，所以不需要用“结题”字眼来限定。
况且，资质和手续都是真实合法并极具含金量的权威文件，推广的名义或标题也足够有市
场销售力，比如：“教育部规划课题实验班今起报名”、“教育部规划课题实验班报名咨询
暨观摩会”、“教育部规划课题实验班追踪：学生受益、家长叫好、应急二次招生”、“教育
部规划课题二期集训班报名通知”等等。我们也希望代理商把精力用对地方，对项目和市
场问题及时探讨，共同创造美好明天。
七、虽然在此次事件中公司是最大的受害者和被中伤对象，但我们仍然愿意拿出公
司的气度，承担起合约应尽的责任和义务。 能够明辨是非充分理解公司的，公司将进行
更大力度的扶持，一切好商量；如果继续无理取闹胡搅蛮缠的，公司将全部委托律师建议
大家走正规法律途径解决。我们坚信时间终将证明一切。公司的经营也将一切正常。
本公司以上所述皆有章可循，有据可查。不管经历多少风雨、挫折，公司将一如既
往地秉持开放和合作的原则，一如既往地努力拼搏，坚持走市场推广的思路，积极做好服
务，精耕细作树立品牌，为响应国家“科教兴国”战略作出应有贡献！再次感谢您的理解
和支持！
特此声明！
广东东海一览教育文化传播有限公司
2012年8月15日