Comparison of ANN,MVR, and SWR models for computing thermal efficiency of a solar still

In this paper, the viability of modeling the instantaneous thermal efficiency (η_ith) of a solar still was determined using meteorological and operational data with an artificial neural network (ANN), multivariate regression (MVR), and stepwise regression (SWR). This study used meteorological and operational variables to hypothesize the effect of solar still performance. In the ANN model, nine variables were used as input parameters: Julian day, ambient temperature, relative humidity, wind speed, solar radiation, feed water temperature, brine water temperature, total dissolved solids of feed water, and total dissolved solids of brine water. The η_ith was represented by one node in the output layer. The same parameters were used in the MVR and SWR models. The advantages and disadvantages were discussed to provide different points of view for the models. The performance evaluation criteria indicated that the ANN model was better than the MVR and SWR models. The mean coefficient of determination for the ANN model was about 13% and14% more accurate than those of the MVR and SWR models, respectively. In addition, the mean root mean square error values of 6.534% and 6.589% for the MVR and SWR models, respectively, were almost double that of the mean values for the ANN model. Although both MVR and SWR models provided similar results, those for the MVR were comparatively better. The relative errors of predicted η_ith values for the ANN model were mostly in the vicinity of ±10%. Consequently, the use of the ANN model is preferred, due to its high precision in predicting η_ith compared to the MVR and SWR models. This study should be extremely beneficial to those coping with the design of solar stills.     

上海宁波两港集装箱航运网络研究（2004~2015年）

港口之间的竞争不仅仅是对陆向腹地货源的竞争，还是对海向腹地特别是航线的争夺。选取上海和宁波为样本，以网络分析软件Gephi和ArcGIS为基本工具，通过复杂网络指标分析上海宁波两港2004~2015年航运地位的变化，以航区为海向腹地的基本空间单元，刻画上海、宁波两港海向腹地市场的变动格局。研究发现：上海港一直保持中国航运网络中的中心地位，近年来，宁波港的网络地位明显上升，中心性与中介性在中国大陆各港口中仅落后于上海港而排在第二位；两港口市场覆盖范围不断扩张，宁波港在近洋航线上不如上海港，但在远洋航线的地中海、西北欧、南亚、北美东西两岸、南美东西两岸、非洲东西两岸等两港覆盖的港口数旗鼓相当；两港口在各航区的首位联系港具有一致性，但至2015年，上海港与美国东部纽约等港口的主要联系被宁波港所取代。在研究数据选取上，采用了航线数据，突破了现有研究以吞吐量数据为主的限制，在研究方法上采用了可视化网络分析软件，将定性数据进行了定量化转化，大大提升了研究成果的可靠性。     

A roadmap for knowledge exchange and mobilization research in conservation and natural resource management

Scholars across all disciplines have long been interested in how knowledge moves within and beyond their community of peers. Rapid environmental changes and calls for sustainable management practices mean the best knowledge possible is needed to inform decisions, policies, and practices to protect biodiversity and sustainably manage vulnerable natural resources. Although the conservation literature on knowledge exchange (KE) and knowledge mobilization (KM) has grown in recent years, much of it is based on context‐specific case studies. This presents a challenge for learning cumulative lessons from KE and KM research and thus effectively using knowledge in conservation and natural resources management. Although continued research on the gap between knowledge and action is valuable, overarching conceptual frameworks are now needed to enable summaries and comparisons across diverse KE‐KM research. We propose a knowledge‐action framework that provides a conceptual roadmap for future research and practice in KE/KM with the aim of synthesizing lessons learned from contextual case studies and guiding the development and testing of hypotheses in this domain. Our knowledge‐action framework has 3 elements that occur at multiple levels and scales: knowledge production (e.g., academia and government), knowledge mediation (e.g., knowledge networks, actors, relational dimension, and contextual dimension), and knowledge‐based action (e.g., instrumental, symbolic, and conceptual). The framework integrates concepts from the sociology of science in particular, and serves as a guide to further comprehensive understanding of knowledge exchange and mobilization in conservation and sustainable natural resource management.     

三峡库区消落带农用坡地氮素流失特征及其环境效应

三峡库区消落带坡地的自发农用较为常见，消落带的这种利用方式可能加剧养分流失并对库区水环境造成影响。通过对库区2011~2013年3个落干期消落带农用坡地的地表径流、壤中流中氮素形态与浓度进行定位监测，研究消落带农用坡地氮素流失特征及其环境效应。结果表明：常规施肥下，消落带农用坡地侵蚀模数为1 443 kg/（hm2·a），落干期内坡地平均径流量为230 mm，径流系数为0.58，其中壤中流流量占总径流量的77%。历次降雨产流事件中常规施肥处理时，地表径流、壤中流中TN平均浓度分别是4.85±0.85、20.73±2.05 mg/L，落干期地表径流（泥沙）和壤中流的TN流失量分别为6.63±1.19、35.22±3.38 kg/hm2，分别占当季施肥量的2.2%、11.7%。可见，随壤中流流失是三峡库区消落带农用坡地氮素流失的主要途径。与常规施肥处理相比，减量施肥处理使地表径流（泥沙）、壤中流TN流失通量分别显著降低了25%、48%，表明减少氮肥用量可以显著降低消落带农用带来的环境风险，建议消落带农用地氮肥进行减量施肥，使其既不影响作物产量，也显著降低氮流失。 关键词： 三峡水库；消落带；地表径流；壤中流；氮负荷；减量施肥     

Untangling the proximate causes and underlying drivers of deforestation and forest degradation in Myanmar

Political transitions often trigger substantial environmental changes. In particular, deforestation can result from the complex interplay among the components of a system—actors, institutions, and existing policies—adapting to new opportunities. A dynamic conceptual map of system components is particularly useful for systems in which multiple actors, each with different worldviews and motivations, may be simultaneously trying to alter different facets of the system, unaware of the impacts on other components. In Myanmar, a global biodiversity hotspot with the largest forest area in mainland Southeast Asia, ongoing political and economic reforms are likely to change the dynamics of deforestation drivers. A fundamental conceptual map of these dynamics is therefore a prerequisite for interventions to reduce deforestation. We used a system‐dynamics approach and causal‐network analysis to determine the proximate causes and underlying drivers of forest loss and degradation in Myanmar from 1995 to 2016 and to articulate the linkages among them. Proximate causes included infrastructure development, timber extraction, and agricultural expansion. These were stimulated primarily by formal agricultural, logging, mining, and hydropower concessions and economic investment and social issues relating to civil war and land tenure. Reform of land laws, the link between natural resource extraction and civil war, and the allocation of agricultural concessions will influence the extent of future forest loss and degradation in Myanmar. The causal‐network analysis identified priority areas for policy interventions, for example, creating a public registry of land‐concession holders to deter corruption in concession allocation. We recommend application of this analytical approach to other countries, particularly those undergoing political transition, to inform policy interventions to reduce forest loss and degradation.     

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

● A novel deep learning framework for short-term water demand forecasting. ● Model prediction accuracy outperforms other traditional deep learning models. ● Wavelet multi-resolution analysis automatically extracts key water demand features. ● An analysis is performed to explain the improved mechanism of the proposed method. Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.     

东部发达城市智慧水务建设探析

在互联网技术飞速发展,于各传统行业运用的建设大背景下,"智慧水务"的构想应运而生。结合当前水务发展遇到的实际问题和国家战略部署阐述了智慧水务的建设。最后提出应落实发展信息技术,加快技术融合,为智慧城市的建设添砖加瓦。     

我国涉水旅游安全事故的时空分布及成因研究*

为探究我国涉水旅游安全事故的时空分布特征及事故成因机制，提升旅游目的地安全管理水平。以2010—2018年我国旅行社责任险平台的1 752起涉水旅游安全事故保险案例为数据源，利用社会网络分析方法，并结合Ucinet可视化分析技术构建事故时空分布模型及事故成因模型。结果表明:在时空分布规律上，我国涉水旅游安全事故具有较强的时间导向性和空间异质性；在事故成因规律上，游览环境因素和游客自身因素是导致事故发生的主要原因，同时不同影响因素之间具有较强的关联性。研究结果有助于深化旅游者及安全管理人员对涉水旅游安全风险认知，为相关旅游目的地安全管理工作提供理论依据。     

Industry to Industry By-products Exchange Network towards zero waste in palm oil refining processes

This paper presents a systematic framework for optimal utilization of by-products generated during crude palm oil refining processes. Three by-products are considered in the supply chain network: soapstock, palm fatty acid distillate (PFAD) and spent bleaching earth (SBE). These by-products, generated from neutralization, deodorization and bleaching processes, are viable feedstocks to other commercial industries such as animal feed, biodiesel, lubricant and soap. The case study is formulated as Mixed Integer Linear Programming (MILP) and integrated into the framework with the objective to maximize the refinery's profit as well as moving towards a conscious mindset of zero waste. This is the first time that such framework is developed and applied for the palm oil industry. The framework is called as Industry to Industry By-products Exchange Network (I2IBEN). An illustrative case study demonstrates a significant potential profit of MYR182, 893 per month.     

特种设备模糊神经网络安全评价研究

根据特种设备的特点,提出了包含其各生命周期的安全评价指标体系,建立了模糊安全评价的评价因素及其权重,建立了评价模型.应用BP神经网络,建立了特种设备安全评价的模糊神经网络模型,通过神经网络的多次学习训练,评价因素权重得到优化.开发了特种设备模糊神经安全评价系统,以塔机为例对评价因素权重进行了优化.