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Material conscious and information network (MCIN) is a kind of cyber physics social system. This paper aims to study the MCIN modeling method and design the MCIN-based architecture of smart agriculture (MCIN-ASA) which is different from current vertical architecture and involves production, management and commerce. Architecture is composed of three MCIN-ASA participants which are MCIN-ASA enterprises, individuals and commodity.

Architecture uses enterprises and individuals personalized portals as the carriers which are linked precisely with each other through a peer-to-peer network called six-degrees-of-separation block-chain. The authors want to establish a self-organization, open and ecological operational system which includes active, personalized consumption, direct, centralized distribution, distributed and smart production.

The paper models three main MCIN-ASA participants, namely, design the smart supply, demand and management functions, which show the feasibility innovation and high efficiency of implementing MCIN on agriculture. At the same time, the paper presents a prototype system based on the architecture.

The authors think that MCIN-ASA improves current agriculture greatly and inspires a lot in production-marketing-combined electronic commerce.

This paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It could classify input retinal images into a normal class or an abnormal class, which would be further split into four stages of abnormalities automatically.

The proposed solution is developed based on a newly proposed CNN architecture, namely, DeepRoot. It consists of one main branch, which is connected by two side branches. The main branch is responsible for the primary feature extractor of both high-level and low-level features of retinal images. Then, the side branches further extract more complex and detailed features from the features outputted from the main branch. They are designed to capture details of small traces of DR in retinal images, using modified zoom-in/zoom-out and attention layers.

The proposed method is trained, validated and tested on the Kaggle dataset. The regularization of the trained model is evaluated using unseen data samples, which were self-collected from a real scenario from a hospital. It achieves a promising performance with a sensitivity of 98.18% under the two classes scenario.

The new CNN-based architecture (i.e. DeepRoot) is introduced with the concept of a multi-branch network. It could assist in solving a problem of an unbalanced dataset, especially when there are common characteristics across different classes (i.e. four stages of DR). Different classes could be outputted at different depths of the network.

Embedded systems, Internet of Things (IoT) and mobile computing devices are used in various domains which include public-private infrastructure, industrial installation and critical environment. Generally, information handled by these devices is private and critical. Therefore, it must be appropriately secured from different attacks and hackers. Lightweight cryptography is an aspiring field which investigates the implementation of cryptographic primitives and algorithms for resource constrained devices. In this paper, a new compact hybrid lightweight encryption technique has been proposed. Proposed technique uses the fastest bit permutation instruction PERMS with S-box of PRESENT block cipher for non-linearity. An arbitrary n-bit permutation is performed using PERMS instruction in less than log (n) number of instructions. This new hybrid system has been analyzed for software performance on Advanced RISC Machine (ARM) and Intel processor whereas Cadens tool is used to analyze the hardware performance. The result of the proposed technique is improved by the factor of eight as compared to the PRESENT-GRP hybrid block cipher. Moreover, PERMS instruction bit permutation properties result a very good avalanche effect and compact implementation in the both hardware and software environment.

This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

Groundwater is an important source of water supply in arid and semi-arid areas. The purpose of this study is to predict the impact of climate change on groundwater recharge in an arid environment in Ilam Province, west of Iran.

A three-dimensional transient groundwater flow model (modular finite difference groundwater FLOW model: MODFLOW) was used to simulate the impacts of three climate scenarios (i.e. an average of a long-term rainfall, predicted rainfall in 2015-2030 and three years moving average rainfall) on groundwater recharge and groundwater levels. Various climate scenarios in Long Ashton Research Station Weather Generator were applied to predict weather data.

HadCM3 climatic model and A2 emission scenario were selected as the best methods for weather data generation. Based on the results of these models, annual precipitation will decrease by 3 per cent during 2015-2030. For three emission scenarios, i.e. an average of a long-term rainfall, predicted rainfall in 2015-2030 and three years moving average rainfall, precipitation in 2030 is estimated to be 265, 257 and 247 mm, respectively. For the studied aquifer, predicted recharge will decrease compared to recharge calculated based on the average of long-term rainfall.

The decline of groundwater level in the study area was 11.45 m during the past 24 years or 0.48 m/year. Annual groundwater depletion should increase to 0.75 m in the coming 16 years via climate change. Climate change adaptation policies in the basin should include changing the crop type, as well as water productivity and irrigation efficiency enhancement at the farm and regional scales.

Recently, there has been a shift toward the embodied energy assessment of buildings. However, the impact of material service life on the life-cycle embodied energy has received little attention. We aimed to address this knowledge gap, particularly in the context of the UAE and investigated the embodied energy associated with the use of concrete and other materials commonly used in residential buildings in the hot desert climate of the UAE.

Using input–output based hybrid analysis, we quantified the life-cycle embodied energy of a villa in the UAE with over 50 years of building life using the average, minimum, and maximum material service life values. Mathematical calculations were performed using MS Excel, and a detailed bill of quantities with >170 building materials and components of the villa were used for investigation.

For the base case, the initial embodied energy was 57% (7390.5 GJ), whereas the recurrent embodied energy was 43% (5,690 GJ) of the life-cycle embodied energy based on average material service life values. The proportion of the recurrent embodied energy with minimum material service life values was increased to 68% of the life-cycle embodied energy, while it dropped to 15% with maximum material service life values.

The findings provide new data to guide building construction in the UAE and show that recurrent embodied energy contributes significantly to life-cycle energy demand. Further, the study of material service life variations provides deeper insights into future building material specifications and management considerations for building maintenance.

This study aims to build a framework of the influencing factors of construction workers' career promotion and identifies the critical determinants so as to propose suggestions for the government and enterprises to offer construction workers a path for career promotion.

In line with the theory of human resources, such as Herzberg's two-factor theory, this study constructs a theoretical framework that affects the career promotion of construction workers. Using evidence from Guangzhou city, valid data provided by 464 workers from 50 sites were collected by a questionnaire survey, and the significance test on the influencing factors of construction workers' career promotion was taken by binary logistic regression.

The overall career development of construction workers in Guangzhou is worrying. The binary logistic regression indicates that age, working years, type of work, career development awareness, legal awareness, professional mentality, vocational psychological training and career development path are critical factors that affect construction workers' career promotion.

This study for the first time explores the career promotion of frontline construction workers. Specifically, it identifies the critical factors that affect the career promotion of workers and thus lays a foundation for further research and the promotion and continuous and healthy development of the construction industry. Thus, this study is original and has theoretical and practical significance.

The Reflective Middleware for Acoustic Management (ReM-AM), based on the Middleware for Cloud Learning Environments (AmICL), aims to improve the interaction between users and agents in a Smart Environment (SE) using acoustic services, in order to consider the unpredictable situations due to the sounds and vibrations. The middleware allows observing, analyzing, modifying and interacting in every state of a SE from the acoustics. This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management.

This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management. In this paper are defined the different domains of knowledge required for the management of the sounds in SEs, which are modeled using ontologies.

This work proposes an acoustics and sound ontology, a service-oriented architecture (SOA) ontology, and a data analytics and autonomic computing ontology, which work together. Finally, the paper presents three case studies in the context of smart workplace (SWP), ambient-assisted living (AAL) and Smart Cities (SC).

Future works will be based on the development of algorithms for classification and analysis of sound events, to help with emotion recognition not only from speech but also from random and separate sound events. Also, other works will be about the definition of the implementation requirements, and the definition of the real context modeling requirements to develop a real prototype.

In the case studies is possible to observe the flexibility that the ReM-AM middleware based on the ODA paradigm has by being aware of different contexts and acquire information of each, using this information to adapt itself to the environment and improve it using the autonomic cycles. To achieve this, the middleware integrates the classes and relations in its ontologies naturally in the autonomic cycles.

The main contribution of this work is the description of the ontologies required for future works about acoustic management in SE, considering that what has been studied by other works is the utilization of ontologies for sound event recognition but not have been expanded like knowledge source in an SE middleware. Specifically, this paper presents the theoretical framework of this work composed of the AmICL middleware, ReM-AM middleware and the ODA paradigm.

The purpose of this paper is to describe how emerging open standards are replacing traditional industrial networks. Current industrial Ethernet networks are not interoperable; thus, limiting the potential capabilities for the Industrial Internet of Things (IIoT). There is no forthcoming new generation fieldbus standard to integrate into the IIoT and Industry 4.0 revolution. The open platform communications unified architecture (OPC UA) time-sensitive networking (TSN) is a potential vendor-independent successor technology for the factory network. The OPC UA is a data exchange standard for industrial communication, and TSN is an Institute of Electrical and Electronics Engineers standard for Ethernet that supports real-time behaviour. The merging of these open standard solutions can facilitate cross-vendor interoperability for Industry 4.0 and IIoT products.

A brief review of the history of the fieldbus standards is presented, which highlights the shortcomings for current industrial systems in meeting converged traffic solutions. An experimental system for the OPC UA TSN is described to demonstrate an approach to developing a three-layer factory network system with an emphasis on the field layer.

From the multitude of existing industrial network schemes, there is a convergence pathway in solutions based on TSN Ethernet and OPC UA. At the field level, basic timing measurements in this paper show that the OPC UA TSN can meet the basic critical timing requirements for a fieldbus network.

This paper uniquely focuses on the specific fieldbus standards elements of industrial networks evolution and traces the developments from the early history to the current developing integration in IIoT context.

The Kirkendall void had been a well-known issue for long-term reliability of semiconductor interconnects; while even the KVs exist at the interfaces of Cu and Sn, it may still be able to pass the condition of unbias long-term reliability testing, especially for 2,000 cycles of temperature cycling test and 2,000 h of high temperature storage. A large number of KVs were observed after 200 cycles of temperature cycling test at the intermetallic Cu₃Sn layer which locate between the intermetallic Cu₆Sn₅ and Cu layers. These kinds of voids will grow proportional with the aging time at the initial stage. This paper aims to compare various IMC thickness as a function of stress test, the Cu₃Sn and Cu₆Sn₅ do affected seriously by heat, but Ni₃Sn₄ is not affected by heat or moisture.

The package is the design in the flip chip-chip scale package with bumping process and assembly. The package was put in reliability stress test that followed AEC-Q100 automotive criteria and recorded the IMC growing morphology.

The Cu₆Sn₅ intermetallic compound is the most sensitive to continuous heat which grows from 3 to 10 µm at high temperature storage 2,000 h testing, and the second is Cu₃Sn IMC. Cu₆Sn₅ IMC will convert to Cu₃Sn IMC at initial stage, and then Kirkendall void will be found at the interface of Cu and Cu₃Sn IMC, which has quality concerning issue if the void’s density grows up. The first phase to form and grow into observable thickness for Ni and lead-free interface is Ni₃Sn₄ IMC, and the thickness has little relationship to the environmental stress, as no IMC thickness variation between TCT, uHAST and HTSL stress test. The more the Sn exists, the thicker Ni₃Sn₄ IMC will be derived from this experimental finding compare the Cu/Ni/SnAg cell and Ni/SnAg cell.

The research found that FCCSP can pass automotive criteria that follow AEC-Q100, which give the confidence for upgrading the package type with higher efficiency and complexities of the pin design.

This result will impact to the future automotive package, how to choose the best package methodology and what is the way to do the package. The authors can understand the tolerance for the kind of flip chip package, and the bump structure is then applied for high-end technology.

The overall three kinds of bump structures, Cu/Ni/SnAg, Cu/SnAg and Ni/SnAg, were taken into consideration, and the IMC growing morphology had been recorded. Also, the IMC had changed during the environmental stress, and KV formation was reserved.