Ranking Sets of Objects: The Complexity of Avoiding Impossibility Results

The problem of lifting a preference order on a set of objects to a preference order on a family of subsets of this set is a fundamental problem with a wide variety of applications in AI. The process is often guided by axioms postulating properties the lifted order should have. Well-known impossibility results by Kannai and Peleg and by Barbera and Pattanaik tell us that some desirable axioms – namely dominance and (strict) independence – are not jointly satisfiable for any linear order on the objects if all non-empty sets of objects are to be ordered. On the other hand, if not all non-empty sets of objects are to be ordered, the axioms are jointly satisfiable for all linear orders on the objects for some families of sets. Such families are very important for applications as they allow for the use of lifted orders, for example, in combinatorial voting. In this paper, we determine the computational complexity of recognizing such families. We show that it is \Pi_2^p-complete to decide for a given family of subsets whether dominance and independence or dominance and strict independence are jointly satisfiable for all linear orders on the objects if the lifted order needs to be total. Furthermore, we show that the problem remains coNP-complete if the lifted order can be incomplete. Additionally, we show that the complexity of these problems can increase exponentially if the family of sets is not given explicitly but via a succinct domain restriction. Finally, we show that it is NP-complete to decide for a family of subsets whether dominance and independence or dominance and strict independence are jointly satisfiable for at least one linear order on the objects.

A Study of Supplier Selection Method Based on SVM for Weighting Expert Evaluation

How to choose suppliers scientifically is an important part of strategic decision-making management of enterprises. Expert evaluation is subjective and uncontrollable; sometimes, there exists biased evaluation, which will lead to controversial or unfair results in supplier selection. To tackle this problem, this paper proposes a novel method that employs machine learning to learn the credibility of expert from historical data, which is converted to weights in evaluation process. We first use the Support Vector Machine (SVM) classifier to classify the historical evaluation data of experts and calculate the experts’ evaluation credibility, then determine the weights of the evaluation experts, finally assemble the weighted evaluation results, and get a preference order of choosing suppliers. The main contribution of this method is that it overcomes the shortcomings of multiple conversions and large loss on evaluation information, maintains the initial evaluation information to the maximum extent, and improves the credibility of evaluation results and the fairness and scientificity of supplier selection. The results show that it is feasible to classify the past evaluation data of the evaluation experts by the SVM classification model, and the expert weights determined on the basis of the evaluation credibility of experts are adjustable.

A Characterization of Lexicographic Preferences

This paper characterizes lexicographic preferences over alternatives that are identified by a finite number of attributes. Our characterization is based on two key concepts: a weaker notion of continuity called “mild continuity” (strict preference order between any two alternatives that are different with respect to every attribute is preserved around their small neighborhoods) and an “unhappy set” (any alternative outside such a set is preferred to all alternatives inside). Three key aspects of our characterization are as follows: (i) we use continuity arguments; (ii) we use the stepwise approach of looking at two attributes at a time; and (iii) in contrast with the previous literature, we do not impose noncompensation on the preference and consider an alternative weaker condition.

A Multiattribute Decision-Making Framework: VIKOR Method with Complex Spherical Fuzzy N -Soft Sets

In this paper, we set forth a framework for solving a multiattribute group decision-making (MAGDM) problem, namely, the selection of a firm for participation in a Saudi oil refinery project in Pakistan. This project will prove a key success factor for the economic growth of Pakistan due to its enormous economic impact on the energy sector, industrial development, commerce, transportation, and so on. This multiplicity justifies that several intricate components comprising both intrinsic and external attributes should be adequately evaluated for the selection of such a firm, that is, the formulation of this question as a MAGDM problem. Nonbinary evaluation with two-dimensional ambiguity and uncertainty in the parameters are general concerns in modern literature, and they fit into this problem. Within this context, one of the most superior and amenable theories (complex spherical fuzzy N -soft sets, henceforth C S F N S f S s ) shall be used to formulate a new comprehensive method, known as complex spherical fuzzy N -soft-VIKOR ( C S F N S f -VIKOR) method. According to the general spirit of the benchmark technique, the normalized Euclidean distances and the weights of the attributes are jointly handled, and as consequence, two main features (“maximum group utility” and “minimum individual regret”) are acquired. The coefficient strategy with reference to group utility measure and individual regret measure of opponents are employed for the compromise measure. Armed with this novel tool, we single out the most feasible firm according to the preference order of the alternatives examined by the decision-makers on the subject of linear normalized weights of experts and attributes. Furthermore, a comparative analysis justifies the C S F -VIKOR method, and some results prove its capabilities and validity. Moreover, a sensitivity test certifies the stability of the proposed method.

Putting a Compass on the Map of Elections

In their AAMAS 2020 paper, Szufa et al. presented a "map of elections" that visualizes a set of 800 elections generated from various statistical cultures. While similar elections are grouped together on this map, there is no obvious interpretation of the elections' positions. We provide such an interpretation by introducing four canonical “extreme” elections, acting as a compass on the map. We use them to analyze both a dataset provided by Szufa et al. and a number of real-life elections. In effect, we find a new parameterization of the Mallows model, based on measuring the expected swap distance from the central preference order, and show that it is useful for capturing real-life scenarios.

Implementasi Topsis untuk Menentukan Rekomendadi Makanan Anak Usia 1-3 Tahun pada Sistem Monitoring Tumbuh Kembang Anak

<p class="Abstrak"> </p><p class="Abstrak">AbstractPenting bagi orang tua untuk memperhatikan pertumbuhan anak secara teratur terutama pada saat periode emasnya. Usia emas pada anak berada pada saat 1000 hari pertama sejak kelahiran atau hingga anak berusia 2 tahun, tumbuh kembang anak dapat meningkat sangat signifikan pada usia ini. Pertumbuhan anak dapat maksimal apabila nutrisi yang diberikan juga tepat sejak usia lahir sampai 3 tahun. Stunting (kerdil) merupakan salahsatu penyakit yang disebabkan karena kurangnya nutrisi pada anak. Stunting adalah sebuah kondisi dimana bayi memiliki panjang dan tinggi badan yang lebih rendah daripada bayi pada umumnya. Pola asuh orang tua terhadap bayi secara mandiri menjadi sangat diperlukan. Untuk membantu orang tua dalam memantau tumbuh kembang anak serta mengurangi peningkatan jumlah bayi stunting maka dibangun sistem monitoring tumbuh kembang anak berbasis web. Pada sistem ini terdapat fitur untuk memberikan rekomendasi makanan berdasarkan kebutuhan kalori setiap anak. Dalam menentukan rekomendasi makanan diperlukan metode Sistem Pendukung Keputusan (SPK) sesuai dengan kebutuhan kalori anak. Dalam penerapan SPK, terdapat metode yang dapat digunakan untuk melakukan analisis data antara lain adalah metode <em>Technique for Order of Preference by Similarity to Ideal Solution</em> (TOPSIS). Alternatif yang digunakan meliputi nama makanan yang dapat dikonsumsi oleh anak usia 1 sampai dengan 3 tahun. Sedangkan kriteria yang digunakan adalah kalori yang didalamnya terdapat karbohidrat, lemak, protein, dan kalsium. 3. Hasil perankingan yang diberikan oleh TOPSIS pada telah berhasil memberikan perankingan dengan nilai yang berbeda-beda, kecuali pada beberapa alternatif. Hal itu dikarenakan kesamaan nilai dari kedua alternatif pada setiap kriteria.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong> Abstract</strong></em></p><p class="Abstract"><em>It is important for parents to pay attention to the growth of the child in the golden period. The golden age in children remains in the first 1000 days from birth or until the child is born 2 years, child growth and development can increase very significantly at this age. The number of children who can reach a maximum age of 3 years. Stunting is a disease that causes nutritional deficiencies in children. Stunting is a place where babies have a lower length and height than a baby's place in general. Parenting for independent babies is needed. To help parents in developing child growth and development also increase the number of stunting babies a web-based growth and development monitoring system was built. This system provides features to provide food recommendations based on the calorie needs of each child. In determining food recommendations, a Decision Support System (SPK) method is needed in accordance with the calorie needs of children. In the application of SPK, methods that can be used to analyze data include the Technical Method for Preference Order with Similarity to Ideal Solution (TOPSIS). The alternatives used are the names of foods that can be consumed by children aged 1 to 3 years. While the criteria used are calories in fat, fat, protein, and calcium. 3. The ranking results given by TOPSIS have succeeded in ranking them with different values, except for a number of alternatives. That's because it considers the value of the two alternatives on each criterion.</em></p><p class="Judul2"> </p><p class="Abstrak"><em><strong><br /></strong></em></p>

The Effective Use of RRI Teaching Methods on a CSR Course

Teaching with RRI methods makes a difference. This paper aims to evaluate the application of RRI tools in a CSR course. A course is considered effective when objectives, targeted skills, competences, and expected learning outcomes are communicated and reached. The focus of this research is understanding the impact of a specific international blocked course on the social and environmental sensitivity of students through the application of a mixed-method approach. Q-methodology was used to measure the preferences of students before and after the course regarding their individual behaviour and expectations towards companies. Interviews were conducted after the course to assess individual perceptions about the course and its teaching methods. Results suggest that the RRI approach in teaching is clearly appreciated by students, and its effectiveness is estimated as high. Changes in responsibility-related preference order reflect stronger and weaker impacts alike, helping identify effective RRI tools for teaching, as well as opportunities for further improvement.

Multi-Objective Heuristic Decision Making and Benchmarking for Mobile Applications in English Language Learning

This research proposes to evaluate and analyze the decision matrix for learner's English mobile applications (EMAs) based on multi-objective heuristic decision making with a view to listening, speaking, reading, and writing. Because of the number of criteria, the significance of parameters, and variance in results, EMAs are difficult. Decision making has built on the combination of listening, speaking, reading, and writing and EMA evaluation criteria for students. The requirements are adapted from a framework of pre-school education. Six alternatives and 17 skills as a requirement are included in decision-making results. The six EMA are then assessed, with six English learning experts distributing a review form. The application subsequently is evaluated using the best-worst method and preference-order technique (TOPSIS) using multi-objective heuristic decision making methods. The best-worst method is used to measure requirements, whereas TOPSIS is used to test and assess the applications. In two cases, namely person and group, TOPSIS is used. Internal and external aggregations are used throughout the group context. In effect, the aim of evaluating the proposed study and comparing it to six relative studies with scenarios and benchmarking checklists is to develop an objectives validation framework for e-apps.

Quantum chemical calculation studies toward microscopic understanding of retention mechanism of Cs radioisotopes and other alkali metals in lichens

We evaluate stability of cesium (Cs) and other alkali-metal cation complexes of lichen metabolites in both gas and aqueous phases to discuss why lichens can retain radioactive Cs in the thalli over several years. We focus on oxalic acid, (+)-usnic acid, atranorin, lecanoric acid, and protocetraric acid, which are common metabolite substances in various lichens including, e.g., Flavoparmelia caperata and Parmotrema tinctorum retaining Cs in Fukushima, Japan. By performing quantum chemical calculations, their gas-phase complexation energies and aqueous-solution complexation free energies with alkali-metal cations are computed for their neutral and deprotonated cases. Consequently, all the molecules are found to energetically favor cation complexations and the preference order is Li$$^+>$$ + > Na$$^+>$$ + > K$$^+>$$ + > Rb$$^+>$$ + > Cs$$^+$$ + for all conditions, indicating no specific Cs selectivity but strong binding with all alkali cations. Comparing complexation stabilities among these metabolites, lecanoric and protocetraric acids seen in medullary layer are found to keep higher affinity in their neutral case, while (+)-usnic acid and atranorin in upper cortex exhibit rather strong affinity only in deprotonated cases through forming stable six atoms’ ring containing alkali cation chelated by two oxygens. These results suggest that the medullary layer can catch all alkali cations in a wide pH range around the physiological one, while the upper cortex can effectively block penetration of metal ions when the metal stress grows. Such insights highlight a physiological role of metabolites like blocking of metal-cation migrations into intracellular tissues, and explain long-term retention of alkali cations including Cs in lichens containing enough such metabolites to bind them.

IN-SILICO ALANINE SCANNING ANALYSIS ON THE CATALYTIC RESIDUES OF A NOVEL Β-GLUCOSIDASE FROM TRICHODERMA ASPERELLUM UC1

Currently, the catalytic residue of the highly prolific fungal β-glucosidase (BGL) of Trichoderma asperellum UC1 remains unvalidated. The study used the alanine scanning method to confirm the catalytic residues of the BGL as Glu165, Asp226, and Glu423. This method cancels out all intermolecular hydrogen bonds with substrates, lignin, hemicellulose, and cellulose. Results revealed an overall decline in the stability of the energy-minimized mutant enzymes' compared to the wild-type BGL. The mutant enzyme registered lower PROCHECK (91.0%), ERRAT (93.09%), and Verify-3D (98.92%) values, in comparison to 90.2%, 92.09%, 98.06%, in the wild-type BGL, respectively. The mutant BGL UC1-substrate complexes were less stable than the wild-type enzyme, in which the mutant exhibited higher binding energies for docked lignin (−7.4% kcal mol-1), cellulose (−7.2 kcal mol-1), and hemicellulose (−7.2 kcal mol-1). Binding energies of the wild-type BGL with the corresponding substrates were lower at −7.9 kcal mol-1, −8.1 kcal mol-1, and −7.8 kcal mol-1. An interesting observation was that the alanine scanning changed the substrate preference order based on the calculated binding energies. The mutant BGL bound preferentially to lignin>cellulose=hemicellulose, while the wild-type BGL was selective to cellulose>lignin>hemicellulose. Hence, the findings convey the high likelihood of Glu165, Asp 256, and Glu423 are the catalytic residues of the BGL of T. asperellum UC1.