Evaluating Freight Forwarders in the Air Cargo Industry

Freight forwarders are of great importance in the air cargo industry. As the cargo revenue constitutes an important source for airline companies, the evaluation of freight forwarders is vital for airlines’ success. This study employs DEMATEL method in order to prioritize the determined criteria for evaluating freight forwarders from the point of view of an airline. The calculated importance weights are used in an illustrative problem where the ranking of freight forwarders are obtained by employing the simple additive weighting method.

Willingness to Travel for Cellular Therapy: The Influence of Follow-Up Care Location, Oncologist Continuity, and Race

PURPOSE Patients weigh competing priorities when deciding whether to travel to a cellular therapy center for treatment. We conducted a choice-based conjoint analysis to determine the relative value they place on clinical factors, oncologist continuity, and travel time under different post-treatment follow-up arrangements. We also evaluated for differences in preferences by sociodemographic factors. METHODS We administered a survey in which patients with diffuse large B-cell lymphoma selected treatment plans between pairs of hypothetical options that varied in travel time, follow-up arrangement, oncologist continuity, 2-year overall survival, and intensive care unit admission rate. We determined importance weights (which represent attributes' value to participants) using generalized estimating equations. RESULTS Three hundred and two patients (62%) responded. When all follow-up care was at the center providing treatment, plans requiring longer travel times were less attractive ( v 30 minutes, importance weights [95% CI] of –0.54 [–0.80 to –0.27], –0.57 [–0.84 to –0.29], and –0.17 [–0.49 to 0.14] for 60, 90, and 120 minutes). However, the negative impact of travel on treatment plan choice was mitigated by offering shared follow-up (importance weights [95% CI] of 0.63 [0.33 to 0.93], 0.32 [0.08 to 0.57], and 0.26 [0.04 to 0.47] at 60, 90, and 120 minutes). Black participants were less likely to choose plans requiring longer travel, regardless of follow-up arrangement, as indicated by lower value importance weights for longer travel times. CONCLUSION Reducing travel burden through shared follow-up may increase patients' willingness to travel to receive cellular therapies, but additional measures are required to facilitate equitable access.

Peningkatan Akurasi Pembobotan Attribute importance weights Pada Deteksi Fraud

Kerugian miliaran dollar setiap tahunnya dialami oleh bank yang disebabkan oleh Fraud. Salah satu solusi untuk mengatasi kasus fraud yang dialami dunia perbankan dapat dilakukan dengan proses deteksi fraud. Pada proses deteksi Fraud, terdapat berbagai atribut PBF (Process Based Fraud) yang setiap atributnya memiliki dampak yang berbeda dalam mendeteksi fraud. Untuk menentukan bobot setiap atribut PBF digunakan metode MDL (Modified Digital Logic). Metode MDL menghasilkan attribute importance weights yang sesuai dengan dampak atribut PBF. Namun peran pakar masih sangat signifikan dalam menilai setiap attribute importance weights. Penelitian ini bertujuan untuk mengubah prosedur penentuan bobot attribute importance weights dalam metode MDL dengan menambahkan metode Multiple Linear Regression (MLR). Dengan mengganti inputan yang sebelumnya diberikan oleh pakar menjadi perbandingan bobot atribut secara otomatis. Kemudian hasil dari kedua metode dievaluasi menggunakan confusion matrix. Berdasarkan hasil eksperimen, metode MLR menunjukkan persentase klasifikasi menggunakan semua attribute importance weights menunjukkan hasil yang lebih baik dengan akurasi sebesar 99,5%.

Overcoming Bottlenecks for Realizing a Vehicle-to-Grid Infrastructure in Europe through Standardization

This paper focuses on committee–market standards battles for the case of vehicle-to-grid technology in Europe. In this battle, standards such as CHArge de MOve (CHAdeMO) and Combined Charging System (CCS) Combo are competing. The paper identifies relevant factors with the help of a literature review and expert interviews. Furthermore, the importance weights were established for the factors. The paper ends with a discussion and conclusion in which the theoretical contributions, practical implications, limitations, and recommendations for further research are discussed.

Peningkatan Akurasi Pembobotan Attribute Importance Weights pada Deteksi Fraud

Kerugian miliaran dollar setiap tahunnya dialami oleh bank yang disebabkan oleh Fraud. Salah satu solusi untuk mengatasi kasus fraud yang dialami dunia perbankan dapat dilakukan dengan proses deteksi fraud. Pada proses deteksi Fraud, terdapat berbagai atribut PBF (Process Based Fraud) yang setiap atributnya memiliki dampak yang berbeda dalam mendeteksi fraud. Untuk menentukan bobot setiap atribut PBF digunakan metode MDL (Modified Digital Logic). Metode MDL menghasilkan attribute importance weights yang sesuai dengan dampak atribut PBF. Namun peran pakar masih sangat signifikan dalam menilai setiap attribute importance weights. Penelitian ini bertujuan untuk mengubah prosedur penentuan bobot attribute importance weights dalam metode MDL dengan menambahkan metode Multiple Linear Regression (MLR). Dengan mengganti inputan yang sebelumnya diberikan oleh pakar menjadi perbandingan bobot atribut secara otomatis. Kemudian hasil dari kedua metode dievaluasi menggunakan confusion matrix. Berdasarkan hasil eksperimen, metode MLR menunjukkan persentase klasifikasi menggunakan semua attribute importance weights menunjukkan hasil yang lebih baik dengan akurasi sebesar 99,5%.

Sparse Deep Neural Networks on Imaging Genetics for Schizophrenia Case-Control Classification

Machine learning approaches hold potential for deconstructing complex psychiatric traits and yielding biomarkers which have a large potential for clinical application. Particularly, the advancement in deep learning methods has promoted them as highly promising tools for this purpose due to their capability to handle high-dimensional data and automatically extract high-level latent features. However, current proposed approaches for psychiatric classification or prediction using biological data do not allow direct interpretation of original features, which hinders insights into the biological underpinnings and development of biomarkers. In the present study, we introduce a sparse deep neural network (DNN) approach to identify sparse and interpretable features for schizophrenia (SZ) case-control classification. An L0-norm regularization is implemented on the input layer of the network for sparse feature selection, which can later be interpreted based on importance weights. We applied the proposed approach on a large multi-study cohort (N = 1,684) with brain structural MRI (gray matter volume (GMV)) and genetic (single nucleotide polymorphism (SNP)) data for discrimination of patients with SZ vs. controls. A total of 634 individuals served as training samples, and the resulting classification model was evaluated for generalizability on three independent data sets collected at different sites with different scanning protocols (n = 635, 255 and 160, respectively). We examined the classification power of pure GMV features, as well as combined GMV and SNP features. The performance of the proposed approach was compared with that yielded by an independent component analysis + support vector machine (ICA+SVM) framework. Empirical experiments demonstrated that sparse DNN slightly outperformed ICA+SVM and more effectively fused GMV and SNP features for SZ discrimination. With combined GMV and SNP features, sparse DNN yielded an average classification error rate of 28.98% on external data. The importance weights suggested that the DNN model prioritized to select frontal and superior temporal gyrus for SZ classification when a high sparsity was enforced, and parietal regions were further included with a lower sparsity setting, which strongly echoed previous literature. This is the first attempt to apply an interpretable sparse DNN model to imaging and genetic features for SZ classification with generalizability assessed in a large and multi-study cohort. The results validate the application of the proposed approach to SZ classification, and promise extended utility on other data modalities (e.g. functional and diffusion images) and traits (e.g. continuous scores) which ultimately may result in clinically useful tools.