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Summary Thermoelectric generator is among the earliest initiated electricity‐harvesting methods. It is a very potential power harvester that can convert wasteful thermal energy into electricity. However, it often suffers from low energy conversion rate due to its inconsistent heat source, inefficient thermoelectric material (or thermoelement) performance, and incompetent structural issues. Progressively for the first time, detailed methodological surveys and analyses are made for bulk, thick, and thin films in this review. This is in order to accommodate better insights and comprehensions on the emerging trends and progresses of thermoelectric generators from 1989 to 2017. The research interests in thermoelectric generators have started back in 1989, and have continuously experienced emerging progresses in the number of studies over the last years. The methodological reviews and analyses of thermoelectric generator showed that almost 46.6% of bulk and 46.1% of thick and thin film research works, respectively, are actively progressed in 2014 to 2017. Nearly 86.2% of bulk and 44.1% of thick and thin film thermoelectric generators are realizing in between 0.001 and 4 μW cm−2 K−2, while 43.1% of thick and thin films are earning among 10−6 to 0.001 μW cm−2 K−2. The highest achievement made until now is 2.5 W cm−2 at a temperature difference of 140 K and thermoelectric efficiency factor of 127.55 μW cm−2 K−2. This achievement remarked positive elevation for the field and interest in thermoelectric power generation. Consecutively, the research trends of fundamental devices' structure, thermoelement, fabrication, substrate, and heat source characteristics are analyzed too, along with the desired improvement highlights for the applications of thermoelectric generators.

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Abstract-The accuracy of DNA computing highly depends on the DNA strands used in solving complex computations. As such, many approaches are proposed to design DNA oligonucleotides that are stable and unique. In this paper, an improved binary particle swarm optimization (IBPSO) algorithm is proposed and implemented. Four objective functions which are H-measure, similarity, hairpin and continuity are employed to define the uniqueness of designed sequences. The DNA words are constrained within a predefined range of GC-content and melting temperature. The performances and the ability of the algorithm to enhance the characteristics of generated DNA code words are analyzed. The results obtained show that this algorithm executes better sequences and did perform better compared to other optimization techniques. Moreover, it converges faster than the previously suggested binary particle swarm optimization algorithm.

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Abstract-The DNA code words designing is a multi-criteria combinatorial optimization task. The designed words should be as unique as possible, thermodynamically stable, non-self hybridized, non-cross hybridized with others and have good chemical properties. In this paper, the DNA words designing approach implied concurrent minimizations of four objective functions, H-measure, similarity, hairpin and continuity. The designations is subjected to a predefine range of melting temperature and GC-content. A novel multi-population optimizer, M-VEDEPSO, is employed to design sets of DNA strands. The algorithm runs for 10 times and as a result, each population has lower average fitness values compared to the fitness values obtained using the conventional VEDEPSO algorithm. The results obtained from the algorithm are indicated by 12 randomly selected non-dominated particles/individuals. These solutions are obtained via Pareto dominance concepts. Keywords-fitness; Pareto; population; thermodynamic I.RESEARCH BACKGROUND DNA code words designing is a process of arranging the four DNA alphabets, letters or codes known as adenine (A), thymine (T), guanine (G) and cytosine (C) within a predefined length. These arrangements are then evaluated using some combinatorial constraints. DNA words designing are well known as a complex multi-criterion constrained optimization task. Thus, many research works had been proposed to solve this complex optimization problem. [33][34][35][36][37]. In this paper, a hybrid methodology of discrete particle swarm optimizer (PSO) and discrete differential evolution (DE) is practiced for solving the combinational optimization. II. IMPORTANCES OF DNA LIBRARY DESIGNEnsembles of DNA alphabets create a unique DNA library which is mainly used for molecular computing, DNA nanostructure design, DNA tagging in chemical libraries and DNA microarray design [38].DNA strands are massively parallelism and have an enormous information storage capacity. Unlike the conventional silicon based computations, molecular computing is a highly reliable DNA based computers for solving difficult computations or NP-hard problems. It involves some in vitro laboratory experiments to extract the solutions. Unique DNA dictionaries are used to represent each solution in molecular computing so as to ease the computation procedures. Perfect hybridizations of each DNA strand are required to avoid unnecessary circumstances during in vitro process which could lead to wrong computation results. Hence, it is vital to employ a highly unique DNA library in DNA computing [18].