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Bluetooth Mesh (BM) is one of the promising networking technologies for Internet-of-Things (IoT) networks released by Bluetooth SIG in 2017. BM provides a protocol for multi-hop scalable networking of IoT devices over the widely-used Bluetooth Low Energy (BLE) technology. However, the capabilities and limits of this technology are not fully studied to determine the IoT applications for which this technology can be used. One of the barriers towards this is the lack of a suitable BM network simulator for performance investigations under various conditions and settings. This paper presents a full-fledged open-source event-driven simulator (BMSim) for the performance evaluation of BM networks. The accuracy of the developed simulator is verified by real experiments. Also, BMSim is used to perform a comprehensive investigation of the performance of the BM protocol in various network conditions and configuration settings. The impact of several configuration parameters on the BM network performance is studied. Since the simulator is capable of simulating dynamic networks and run-time configurations, a BM network with node mobility is also investigated. The results reveal the importance and necessity of proper BM parameter configuration mechanisms to achieve the required quality-of-service and network efficiency.

Abstract Considering the cooperative sector capabilities in organizing rural people, primarily focusing on empowering manpower and adopting a sustainable approach to basic resources (water and soil), village-cooperative initiative has emerged as a new concept by registering and forming 4565 new cooperatives in Iran’s rural regions. The present research which was conducted in two qualitative and quantitative phases designed a new model by integrating the new village-cooperative approach along with the sustainable livelihood’s framework theory. The study sample of the qualitative phase included 32 theorists of the village-cooperative initiative. To analyze the data, the grounded theory and three-step process of open coding, axial coding and selective coding were used using MAXqda18 software. Finally, a paradigm model was designed whose most important components included causal conditions (shocks and seasonal conditions), contextual conditions (trends and governmental support), intervening conditions (control and market development), central categories (sustainable livelihoods with a village- cooperative approach), strategies (coping and adaptation) and consequences (creating jobs, establishing cooperatives, supporting smart agriculture and strengthening social capital). The statistical quantitative phase population consisted of cooperatives members in the village-cooperative initiative (N=405), being selected through Cochran's formula with proportional sampling method summing up to198 people from 5 provinces. The data collection tool was a researcher-made questionnaire whose validity and reliability were confirmed. The Bayesian structural equation modeling was used to analyze the data. The results of the research quantitative phase showed that the variables of financial capital, physical capital, social capital, coping and adaptation strategies, human capital and natural capital were the most effective variables on village-cooperative initiative members’ sustainable livelihoods, respectively. Finally, a hybrid model based on the qualitative and quantitative studies was designed and suggestions were made; for instance, the creation of suitable grounds for off-farm activities such as rural industries and ecotourism. Resumo Considerando as capacidades do setor cooperativo na organização da população rural, focando principalmente na capacitação de mão de obra e na adoção de uma abordagem sustentável para os recursos básicos (água e solo), a iniciativa de cooperativa de aldeia surgiu como um novo conceito ao registrar e formar 4.565 novas cooperativas nas regiões rurais do Irã. A presente pesquisa, que foi conduzida em duas fases qualitativas e quantitativas, projetou um novo modelo integrando a nova abordagem cooperativa de aldeia junto com a teoria da estrutura de subsistência sustentável. A amostra do estudo da fase qualitativa incluiu 32 teóricos da iniciativa vila-cooperativa. Para analisar os dados, a teoria fundamentada e o processo de três etapas de codificação aberta, codificação axial e codificação seletiva foram usados o software MAXqda18. Finalmente, um modelo de paradigma foi desenhado, cujos componentes mais importantes incluíam condições causais (choques e condições sazonais), condições contextuais (tendências e apoio governamental), condições intervenientes (controle e desenvolvimento de mercado), categorias centrais (subsistência sustentável com uma abordagem cooperativa de aldeia), estratégias (enfrentamento e adaptação) e consequências (criação de empregos, estabelecimento de cooperativas, apoio à agricultura inteligente e fortalecimento do capital social). A população da fase quantitativa estatística consistiu de cooperados na iniciativa vila-cooperativa (N = 405), sendo selecionados através da fórmula de Cochran com método de amostragem proporcional totalizando 198 pessoas de cinco províncias. A ferramenta de coleta de dados foi um questionário elaborado pelo pesquisador cuja validade e confiabilidade foram confirmadas. A modelagem de equações estruturais bayesianas foi utilizada para analisar os dados. Os resultados da fase quantitativa da pesquisa mostraram que as variáveis de capital financeiro, capital físico, capital social, estratégias de enfrentamento e adaptação, capital humano e capital natural foram as variáveis mais eficazes na subsistência sustentável dos membros da iniciativa cooperativa de aldeia, respectivamente. Finalmente, um modelo híbrido baseado nos estudos qualitativos e quantitativos foi desenhado e sugestões foram feitas; por exemplo, a criação de terrenos adequados para atividades não agrícolas, como indústrias rurais e ecoturismo.

Abstract It has been shown that land fragmentation can negatively impact the efficiency of farming. Therefore, experts recommend land consolidation process, as a logical and workable solution to solve the problems and complications caused by land fragmentation. Land levelling and consolidation is a process of land reform that changes the construction of agricultural lands which leads to rural development through reforming farm management. However, a single plan cannot be applied to different regions, even though they might be in the same country. Hence, it is vital to investigate multiple factors in a certain region to devise the perfect consolidation plan. The present study, which is a survey-exploratory research, is conducted to provide a comprehensive model to implement the plan for levelling and consolidation of agricultural lands in the Abu Fazel region of Ahvaz, Iran. This research is an applied field research which uses both library and field methods to collect the required data. The study population is in Abu Fazel in the northeast of Ahvaz in Zargan region. The results of the study show that cultural, social, economic, policy-making, educational, agricultural and managerial factors have an effect on the participation of farmers in the levelling and consolidation of agricultural lands in the study area (p≥0.01). Also, there is a strong positive relationship between these factors and the farmers' participation in levelling and consolidation of agricultural lands (p≥0.01). Among these factors, it is observed that policy is main factor. Policymakers can play an effective role in land consolidation and macro development on the one hand and agricultural and rural development. On the other, by accurately assessing the interactive effect of land consolidation and related factors, along with the effects of this process on the evolution of agronomic systems.

The Internet of Things (IoT) has recently become important in accelerating various functions, from manufacturing and business to healthcare and retail. A recommender system can handle the problem of information and data buildup in IoT-based smart commerce systems. These technologies are designed to determine users' preferences and filter out irrelevant information. Identifying items and services that customers might be interested in and then convincing them to buy is one of the essential parts of effective IoT-based smart shopping systems. Due to the relevance of product-recommender systems from both the consumer and shop perspectives, this article presents a new IoT-based smart product-recommender system based on an apriori algorithm and fuzzy logic. The suggested technique employs association rules to display the interdependencies and linkages among many data objects. The most common use of association rule discovery is shopping cart analysis. Customers' buying habits and behavior are studied based on the numerous goods they place in their shopping carts. As a result, the association rules are generated using a fuzzy system. The apriori algorithm then selects the product based on the provided fuzzy association rules. The results revealed that the suggested technique had achieved acceptable results in terms of mean absolute error, root-mean-square error, precision, recall, diversity, novelty, and catalog coverage when compared to cutting-edge methods. Finally, themethod helps increase recommender systems' diversity in IoT-based smart shopping. key program of the National Social Science Foundation of China [18AJY013] This work was supported by the key program of the National Social Science Foundation of China under Grant 18AJY013. Reviewof this manuscript was arranged by Department Editor D. Cetindamar.

Abstract Nanosensors work on the “Nano” scale. “Nano” is a unit of measurement around 10− 9 m. A nanosensor is a device capable of carrying data and information about the behavior and characteristics of particles at the nanoscale level to the macroscopic level. Nanosensors can be used to detect chemical or mechanical information such as the presence of chemical species and nanoparticles or monitor physical parameters such as temperature on the nanoscale. Nanosensors are emerging as promising tools for applications in agriculture. They offer an enormous upgrade in selectivity, speed, and sensitivity compared to traditional chemical and biological methods. Nanosensors can be used for the determination of microbe and contaminants. With the advancement of science in the world and the advent of electronic equipment and the great changes that have taken place in recent decades, the need to build more accurate, smaller and more capable sensors was felt. Today, high-sensitivity sensors are used that are sensitive to small amounts of gas, heat, or radiation. Increasing the sensitivity, efficiency and accuracy of these sensors requires the discovery of new materials and tools. Nano sensors are nanometer-sized sensors that, due to their small size and nanometer size, have such high accuracy and responsiveness that they react even to the presence of several atoms of a gas. Nano sensors are inherently smaller and more sensitive than other sensors. Resumo Os nanossensores funcionam na escala “Nano”. “Nano” é uma unidade de medida em torno de 10-9 m. Um nanosensor é um dispositivo capaz de transportar dados e informações sobre o comportamento e as características das partículas no nível da nanoescala para o nível macroscópico. Os nanossensores podem ser usados para detectar informações químicas ou mecânicas, como a presença de espécies químicas e nanopartículas, ou monitorar parâmetros físicos, como temperatura em nanoescala. Os nanossensores estão surgindo como ferramentas promissoras para aplicações na agricultura. Eles oferecem uma abrangente atualização em seletividade, velocidade e sensibilidade em comparação com os métodos químicos e biológicos tradicionais. Os nanossensores podem ser usados para a determinação de micróbios e contaminantes. Com o avanço da ciência no mundo e o advento dos equipamentos eletrônicos e as grandes mudanças ocorridas nas últimas décadas, sentiu-se a necessidade de construir sensores mais precisos, menores e mais capazes. Hoje, são usados sensores de alta sensibilidade que são sensíveis a pequenas quantidades de gás, calor ou radiação. Aumentar a sensibilidade, eficiência e precisão desses sensores requer a descoberta de novos materiais e ferramentas. Os nanossensores são sensores de tamanho nanométrico que, devido ao seu tamanho pequeno e tamanho nanométrico, possuem uma precisão e capacidade de resposta tão altas que reagem até mesmo na presença de vários átomos de um gás. Os nanossensores são inerentemente menores e mais sensíveis do que outros sensores.

Abstract The study strives to analyze the potential variations of farmers' income under climate change by using Ricardian approach. The case study was Mazandaran province of Iran and three autumn crops, i.e. wheat, barley and canola were considered as the investigated crops. The Long Ashton Research Station Weather Generator (LARS-WG) model was selected to downscale the climate data. Three climate variables were downscaled for the years 2020-2080 under three climate scenarios: optimistic (RCP2.6), medium (RCP4.5), and pessimistic (RCP8.5). The Ricardian approach was also employed to predict the economics of climate change. Accordingly, the mean monthly temperature of the province is projected to have an upward trend under all climate scenarios, however, the rainfall pattern would be varied. The results of economic impacts of climate change also approved that the net income of investigated crops would be different trends under climate change scenarios. Accordingly, the variations of air temperature and rainfall would lead that the net income increases for wheat and barley, while it decreases for canola.

Abstract Despite the adverse effects of late spring frost on the ecological and economic impact on agriculture, the management of these events are poorly investigated. Therefore, the grounded theory was used for extracting the livelihood stabilization model of orchardists on the shores of Lake Urmia against late spring frosts. The statistical population of this study in the qualitative part included 22 experts in the field of horticulture. In the quantitative study, they included all orchardists in the villages along with Lake Urmia, comprising 341 people, of which 236 were randomly selected. By carefully examining and linking the concepts and subcategories, the most important subcategories that were included six subcategories. The results of all calculated goodness-of-fit indexes (chi-square [P > 0.01], RMSEA = 0.000, NFI = 0.95, CFI = 0.91, and GFI = 0.92) showed the model provided an excellent fit for the data. Standardized regression coefficients showed that interfering factors had the greatest impact on the livelihood of orchardists against late spring frosts (β = 0.47). Also, the background factors (β = 0.43) and causal factors (β = 0.39) were found to affect the livelihood of the orchardists in encountering late spring frost. The results of multiple square correlations of dependent variables also indicated that about 41% of the livelihood variance of small-scale orchardists was explained by the predictor variables (i.e. causal conditions, background factors and interfering factors). The findings of this study can assist policymakers, planners and relevant managers while paying more attention to orchardists in the region, so as to support decisions in order to achieve sustainable development.

A problem with machine learning (ML) techniques for detecting intrusions in the Internet of Things (IoT) is that they are ineffective in the detection of low-frequency intrusions. In addition, as ML models are trained using specific attack categories, they cannot recognize unknown attacks. This article integrates strategies of cost-sensitive learning and multitask learning into a hybrid ML model to address these two challenges. The hybrid model consists of an autoencoder for feature extraction and a support vector machine (SVM) for detecting intrusions. In the cost-sensitive learning phase for the class imbalance problem, the hinge loss layer is enhanced to make a classifier strong against low-distributed intrusions. Moreover, to detect unknown attacks, we formulate the SVM as a multitask problem. Experiments on the UNSW-NB15 and BoT-IoT datasets demonstrate the superiority of our model in terms of recall, precision, and F1-score averagely 92.2%, 96.2%, and 94.3%, respectively, over other approaches.