Responsible Sourcing and Supply Chain Traceability

This paper explores a buyer\u27s tracing and its supplier\u27s own sourcing decisions in a multi-tier supply chain. We explore what different stakeholders can do to achieve a more transparent and/or responsible supply chain. We establish that under rather general conditions, the two firms will adopt mixed strategies in equilibrium, a focal case of our analysis. The mixed-strategy results first explain at the micro level why many companies are not certain about whether their supply chains are ethical or not. At the more macro level, they also help explain why a significant proportion of the buyers did not trace or comply with transparency regulations. We then show that more responsible sourcing can be induced by lowering the buyer\u27s tracing cost but not by reducing the supplier\u27s own responsible sourcing cost. We also find that more transparency does not always imply more responsible sourcing. For the external stakeholders, more responsible sourcing may be obtained through lowering tracing costs, improving tracing or public discovery of violations, and imposing more significant reputational damage or penalties only on the buyer. For the internal stakeholders, a contract incorporating both responsible sourcing cost sharing and non-compliance penalty if found may be constructed for the first-best supply chain efficiency and likely social optimality under some simple sufficient conditions

Theoretical characterization of the collective resonance states underlying the xenon giant dipole resonance

We present a detailed theoretical characterization of the two fundamental collective resonances underlying the xenon giant dipole resonance (GDR). This is achieved consistently by two complementary methods implemented within the framework of the configuration-interaction singles (CIS) theory. The first method accesses the resonance states by diagonalizing the many-electron Hamiltonian using the smooth exterior complex scaling technique. The second method involves a new application of the Gabor analysis to wave-packet dynamics. We identify one resonance at an excitation energy of 74 eV with a lifetime of 27 as, and the second at 107 eV with a lifetime of 11 as. Our work provides a deeper understanding of the nature of the resonances associated with the GDR: a group of close-lying intrachannel resonances splits into two far-separated resonances through interchannel couplings involving the 4d electrons. The CIS approach allows a transparent interpretation of the two resonances as new collective modes. Due to the strong entanglement between the excited electron and the ionic core, the resonance wave functions are not dominated by any single particle-hole state. This gives rise to plasma-like collective oscillations of the 4d shell as a whole.Comment: 12 pages, 6 figures, 2 table

Ultrasonication-Assisted Spray Ionization Mass Spectrometry for the Analysis of Biomolecules in Solution

In this paper, we describe a novel technique—ultrasonication-assisted spray ionization (UASI)—for the generation of singly charged and multiply charged gas-phase ions of biomolecules (e.g., amino acids, peptides, and proteins) from solution; this method employs a low-frequency ultrasonicator (ca. 40 kHz) in place of the high electric field required for electrospray ionization. When a capillary inlet is immersed into a sample solution within a vial subjected to ultrasonication, the solution is continually directed to the capillary outlet as a result of ultrasonication-assisted capillary action; an ultrasonic spray of the sample solution is emitted at the outlet of the tapered capillary, leading to the ready generation of gas-phase ions. Using an ion trap mass spectrometer, we found that singly charged amino acid and multiply charged peptides/proteins ions were generated through this single-step operation, which is both straightforward and extremely simple to perform. The setup is uncomplicated: only a low-frequency ultrasonicator and a tapered capillary are required to perform UASI. The mass spectra of the multiply charged peptides and proteins obtained from sample solutions subjected to UASI resemble those observed in ESI mass spectra

Dynamics of fluctuations in a quantum system

"\textit{The noise is the signal}"[R. Landauer, Nature \textbf{392}, 658 (1998)] emphasizes the rich information content encoded in fluctuations. This paper assesses the dynamical role of fluctuations of a quantum system driven far from equilibrium, with laser-aligned molecules as a physical realization. Time evolutions of the expectation value and the uncertainty of a standard observable are computed quantum mechanically and classically. We demonstrate the intricate dynamics of the uncertainty that are strikingly independent of those of the expectation value, and their exceptional sensitivity to quantum properties of the system. In general, detecting the time evolution of the fluctuations of a given observable provides information on the dynamics of correlations in a quantum system.Comment: 6 pages, 2 figure

Revisiting the problem of audio-based hit song prediction using convolutional neural networks

Being able to predict whether a song can be a hit has impor- tant applications in the music industry. Although it is true that the popularity of a song can be greatly affected by exter- nal factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own. Motivated by the recent success of deep learning techniques, we attempt to ex- tend previous work on hit song prediction by jointly learning the audio features and prediction models using deep learning. Specifically, we experiment with a convolutional neural net- work model that takes the primitive mel-spectrogram as the input for feature learning, a more advanced JYnet model that uses an external song dataset for supervised pre-training and auto-tagging, and the combination of these two models. We also consider the inception model to characterize audio infor- mation in different scales. Our experiments suggest that deep structures are indeed more accurate than shallow structures in predicting the popularity of either Chinese or Western Pop songs in Taiwan. We also use the tags predicted by JYnet to gain insights into the result of different models.Comment: To appear in the proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP

A QoS aware services mashup model for cloud computing applications

Purpose: With the popularity of cloud computing, cloud services have become to be application programming platform where users can create new applications mashup(composing) the functionality offered byothers.By composing of distributed, cloud services dynamicallyto provide more complex tasks, services mashup provides an attractive way for building large-scale Internet applications.One of the challenging issues of cloud services mashup is how to find service paths to route the service instances provider through whilemeeting the applications’ resource requirements so that the QoS constraints are satisfied. However, QoS aware service routing problem istypically NP-hard.The purpose of this paper is to propose a QoS Aware Services Mashup(QASM) model to solve this problem more effectively. Design/methodology/approach: In this paper, we focus on the QoS aware services selection problem in cloud services mashup, for example, given the user service composition requirements and their QoS constraint descriptions, how to select the required serviceinstances and route the data flows through these instances so that the QoS requirements are satisfied. We design a heuristic algorithm to find service paths to route the data flows through whilemeeting the applications’ resource requirements and specific QoS constraints. Findings: This study propose a QoS Aware Services Mashup(QASM) model to solve this problem more effectively. Simulations show that QASM can achieve desired QoS assurances as well as load balancing in cloud services environment. Originality/value: This paper present a QASM model for providing high performance distributed applications in the cloud computingPeer Reviewe

Technology Management Competency of Healthcare IS Professionals and Its Effects on IT-healthcare Partnerships

This study presents a conceptual model to investigate technology management competency required by healthcare IS professionals and its impacts on IT-healthcare partnerships. Technology management competency, at the broad level, comprises the business strategic thinking, external knowledge resources linkage, healthcare technology integration capability as well as management and interpersonal skill/knowledge possessed by IS professionals. Such competency is hypothesized to be instrumental in increasing the intentions of IS professionals to develop and reinforce the partnerships with healthcare people. The empirical results support the proposed conceptual framework for technology management related skills/knowledge of IS professionals and indicate that the competency would significantly influences the intentions of IS professionals to develop collaborative relationships with their healthcare partners. The findings of this study not only can provide useful suggestions to help IS professionals review their technology management competency, but also serve as instrumental guidelines for the technology management competency training to strength the IT-healthcare partnership