The Juridical Status of Privileged Combatants Under the Geneva Protocol of 1977 Concerning International Conflicts

Centralized control and coordination of the connections in a wireless network is not possible in practice. To keep the delay from measure-ment instants to actuating the decisions, distributed control is required. This paper focuses on the uplink (from mobiles to base stations) and dis-cusses distributing the decision of when and when not to transmit data (distributed scheduling) to the mobiles. The scheme, uplink transmission timing, utilizes mobile transmitter power control feedback from the base station receiver to determine whether the channel is favorable or not compared to the average channel condition. Thereby, the battery consumption and disturbing power to other connections are reduced. The algorithm can be described as a feedback control system. Some transient behaviors are analyzed using systems theory, and supported by wireless network simulations of a system with a WCDMA (Wideband Code Division Multiple Access) radio interface as in most 3G systems

THE ROLE OF SMALL FIRMS IN CHINA’S TECHNOLOGY DEVELOPMENT

Science & Technology (S&T) is high on the Chinese policy agenda but there are large uncertainties on the actual S&T development. For instance, previous studies tend to focus only on large and medium-sized enterprises (LMEs). The situation in Chinese small firms is far less explored. This paper aims to examine the role of S&T-based small firms. More precisely, we examine how much S&T that has been accounted for by small firms and how their S&T intensity differs across industries and ownership groups. We also analyze how various firm characteristics differ over size categories and S&T status. This study is based on newly processed micro level data provided by the National Bureau of Statistics with information on a large number of S&T indicators for small-, medium-, and large-sized manufacturing firms in China in 2000 and 2004. Our results suggest that small firms in Chinese S&T resemble their role in many other countries. They account for a comparably small share of total S&T and most small firms are not engaged in any S&T. However, those small firms that do engage in S&T tend to be more S&T intensive and have a higher output in terms of patents than larger Chinese S&T firms.Technology; SMEs; China; S&T; R&D

The Due-on-Sale Controversy: Beneficial Effects of the Garn-St. Germain Depository Institution Act of 1982

Radio resource management (RRM) in cellular radio system is an example of automatic control. The system performance may be increased by introducing decentralization, shorter delays and increased adaptation to local demands. However, it is hard to guarantee system stability without being, too conservative while using decentralized resource management. In this paper, two algorithms that both guarantee system stability and use local resource control are proposed for the uplink (mobile to base station). While one of the algorithms uses only local decisions, the other uses a central node to coordinate resources among different local nodes. In the chosen design approach, a feasible solution to the optimization problems corresponds to a stable system. Therefore, the algorithms will never assign resources that lead to an unstable system. Simulations indicate that the proposed algorithms also provide high capacity at any given uplink load level

FDI, Market Structure and R&D Investments in China

FDI can be an important channel for developing countries’ ability to get access to new technology. The impact of FDI on domestically-owned firms’ technology development is less examined but it is frequently argued that technology externalities or demonstration effects could have a positive impact. Another and so far little examined effect of FDI on technology development in domestically-owned firms is through the impact on competition. We examine the effect of FDI on competition in the Chinese manufacturing sector and the effect of competition on firms’ R&D. Our analysis is conducted on a large dataset including all Chinese large and medium sized firms over the period 1998-2004. Our results show that FDI increases competition but there are no strong indications of competition affecting investments in R&D.China; FDI; Competition; R&D

Technology Development and Job Creation in China

This paper examines how Science and Technology (S&T) contribute to job creation in the Chinese manufacturing sector. The ambition of transforming China into an innovation-oriented nation and the emphasis on indigenous innovation capacity building have placed Science and Technology (S&T) high on the Chinese policy agenda. At the same time, the need for job creation is pressing, both to absorb the huge supply of underemployed people, and to enable the annual 20 million new labor market entrants to find employment. We examine the relationship between S&T and job growth in the Chinese industrial sector. S&T can be expected to have both positive and negative effects on employment. For instance, new technology might increase competitiveness and enable Chinese firms to expand their labor force. On the other hand, new technology might be labor-saving, thereby enabling Chinese firms to produce more output with fewer employees. Based on a large sample of manufacturing firms in China between 1998 and 2004, we analyze how S&T affect employment growth. Our results suggest that S&T activities have no effect on job creation.China; Science and Technology; Job-Creation

Incentives vs. Nonpartisanship: The Prosecutorial Dilemma in an Adversary System

The limiting factor in the uplink of all CDMA cellular systems is the relation between uplink noise rise and intended coverage. In link budgets, noise rise is usually simply handled as a constant contribution to the background noise in logarithmic scale, often referred to as interference margin. In practice, however, it is not constant. We model the uplink noise rise as a lognormal distribution, and investigate the impact to link budgets. Simulations and numerical calculations show that the uplink noise rise variance does not critically affect the uplink capacity and coverage. System feasibility and its relation to the uplink load is also discussed. It is shown that approximative load expressions provides an upper bound on the uplink load and therefore they can be used to imply system feasibility. Furthermore, the uplink load expressions provide accurate approximations of the load given that the load is within the practical limits given by the link budgets

CRITICAL FACTORS FOR CSV IMPLEMENTATION

Summary Title: Critical factors for CSV implementation: A case study of reconceiving products within the food industry in Sweden Seminar date: 2016-05-26 Course: FEKN90, Degree Project, Master level in Business Strategy, Business Administration, 30 University Credit Points (30ECTS) Authors: Emelie Jacobsen, Fredrik Lundin & Stephanie Schramm Davholt Supervisor: Matts Kärreman Key words: Creating shared value, CSV implementation, reconceiving products, strategy, sustainability Purpose: The purpose of this thesis is to identify whether there is a difference in critical success factors when implementing CSV compared to a general strategy implementation. A chronological perspective as well as a ranking of importance of critical factors will also be investigated to form a comprehensive framework of the CSV implementation process. Methodology: This study followed a qualitative multiple-case design including six Swedish case companies within the food industry. The empirical information has been gathered through semistructured interviews, sustainability reports and with follow up questions through mail correspondence. The empirical information was analyzed through coding and then compared to previous literature. Theoretical perspective: Previous research has focused on how to implement strategy in general. We could not find as much research on CSV implementation, therefore we have proceeded from the general strategy implementation literature’s results where we identified eleven critical factors. Empirical findings: When implementing CSV our empirical findings show that eleven factors are critical for the implementation process. Out of these eleven factors we have categorized two of them as continuous, corporate culture and cooperation between departments, and we have identified two new factors, trends and timing and education. The remaining seven factors are: clear strategy formulation, management’s involvement, commitment, implementation approach, communication, control systems and follow-up and feedback. Besides these eleven factors we could also identify value based communication as crucial for the CSV implementation. Since it has a direct link to corporate culture we have chosen to include this concept within this factor. Conclusions: This thesis culminates into a three step model for CSV implementation with two continuous factors, corporate culture and cooperation between departments, which are present throughout the entire implementation process. Step 1 includes clear strategy formulation, management’s involvement, trends and timing, and commitment. Step 2 consists of implementation approach, communication and education. Step 3 consists of control systems and follow-up and feedback

Overregulation of Health Care: Musings on Disruptive Innovation Theory

Disruptive innovation theory provides one lens through which to describe how regulations may stifle innovation and increase costs. Basing their discussion on this theory, Curtis and Schulman consider some of the effects that regulatory controls may have on innovation in the health sector

A proof-of-concept study on mortality prediction with machine learning algorithms using burn intensive care data

INTRODUCTION: Burn injuries are a common traumatic injury. Large burns have high mortality requiring intensive care and accurate mortality predictions. To assess if machine learning (ML) could improve predictions, ML algorithms were tested and compared with the original and revised Baux score. METHODS: Admission data and mortality outcomes were collected from patients at Uppsala University Hospital Burn Centre from 2002 to 2019. Prognostic variables were selected, ML algorithms trained and predictions assessed by analysis of the area under the receiver operating characteristic curve (AUC). Comparison was made with Baux scores using DeLong test. RESULTS: A total of 17 prognostic variables were selected from 92 patients. AUCs in leave-one-out cross-validation for a decision tree model, an extreme boosting model, a random forest model, a support-vector machine (SVM) model and a generalised linear regression model (GLM) were 0.83 (95% confidence interval [CI] = 0.72-0.94), 0.92 (95% CI = 0.84-1), 0.92 (95% CI = 0.84-1), 0.92 (95% CI = 0.84-1) and 0.84 (95% CI = 0.74-0.94), respectively. AUCs for the Baux score and revised Baux score were 0.85 (95% CI = 0.75-0.95) and 0.84 (95% CI = 0.74-0.94). No significant differences were observed when comparing ML algorithms with Baux score and revised Baux score. Secondary variable selection was made to analyse model performance. CONCLUSION: This proof-of-concept study showed initial credibility in using ML algorithms to predict mortality in burn patients. The sample size was small and future studies are needed with larger sample sizes, further variable selections and prospective testing of the algorithms. LAY SUMMARY: Burn injuries are one of the most common traumatic injuries especially in countries with limited prevention and healthcare resources. To treat a patient with large burns who has been admitted to an intensive care unit, it is often necessary to assess the risk of a fatal outcome. Physicians traditionally use simplified scores to calculate risks. One commonly used score, the Baux score, uses age of the patient and the size of the burn to predict the risk of death. Adding the factor of inhalation injury, the score is then called the revised Baux score. However, there are a number of additional causes that can influence the risk of fatal outcomes that Baux scores do not take into account. Machine learning is a method of data modelling where the system learns to predict outcomes based on previous cases and is a branch of artificial intelligence. In this study we evaluated several machine learning methods for outcome prediction in patients admitted for burn injury. We gathered data on 93 patients at admission to the intensive care unit and our experiments show that machine learning methods can reach an accuracy comparable with Baux scores in calculating the risk of fatal outcomes. This study represents a proof of principle and future studies on larger patient series are required to verify our results as well as to evaluate the methods on patients in real-life situations.Peer reviewe