Applicability of the GAPS Model to Service Quality in Small Firms

Delivering quality is as critical to survival of small service firms as it is to large corporations. Zeithaml, Parasuraman, and Berry (1990) developed a conceptual model of service quality (Gaps) that identified gaps in service quality and suggested measures to close them. The Gaps model has been used in large service corporations, but is yet to be applied to small service firms. Therefore, this paper examines the applicability of the Gaps model to the smaller service firm. Our analysis revealed that the resources and structure of smaller firms significantly affect the types of service quality gaps that occur and the closure measures

What constitutes adequate legal protection for the collection, use and sharing of mobility and location data in health care in South Africa?

Mobile phone technology has been a catalyst that has added an innovative dimension in health care and created new opportunities for digital health services. These digital devices can be viewed as an extension of the person using them due to the deluge of personal information that can be collected and stored on them. Data collected on mobile phones are used extensively in health services and research. Personal, mobility and location data are constantly collected. The unique mobile phone architecture provides for an easy flow of data between various role players such as application developers and phone manufacturers. The collection, storage and sharing of personal information on mobile phones elicit various legal questions relating to the protection of privacy, consent, liability and the accountability of stakeholders such as health insurance providers, hospital groups and national departments of health

Short interval intracortical inhibition: Variability of amplitude and threshold-tracking measurements with 6 or 10 stimuli per point

Reduced short-interval intracortical inhibition (SICI) in motor neuron disease has been demonstrated by amplitude changes (A-SICI) and threshold-tracking (T-SICI) using 10 stimuli per inter-stimulus interval (ISI). To test whether fewer stimuli would suffice, A-SICI and T-SICI were recorded twice from 30 healthy subjects using 6 and 10 stimuli per ISI. Using fewer stimuli increased mean A-SICI variances by 23.8% but the 7.3% increase in T-SICI variance was not significant. We conclude that our new parallel threshold-tracking SICI protocol, with 6 stimuli per ISI, can reduce time and stimulus numbers by 40% without appreciable loss of accuracy

The regulation of health data sharing in Africa:a comparative study

The sharing of health data is an essential component in the provision of healthcare, in medical research, and disease surveillance. Health data sharing is subject to regulatory frameworks that vary across jurisdictions. In Africa, numerous factors complicate the regulation of health data sharing, including technological, motivational, economic, and political barriers, as well as ethical and legal challenges. This comparative study examines the regulation of health data sharing in Africa by comparing and contrasting the legal and policy frameworks of five African countries. The study identifies gaps and inconsistencies in the current regulatory regimes and provides recommendations for improving the regulation of health data sharing in Africa

Conventional and threshold-tracking transcranial magnetic stimulation tests for single-handed operation

Most single-pulse transcranial magnetic stimulation (TMS) parameters (e.g., motor threshold, stimulus-response function, cortical silent period) are used to examine corticospinal excitability. Paired-pulse TMS paradigms (e.g., short-and long-interval intracortical inhibition (SICI/LICI), short-interval intracortical facilitation (SICF), and short-and long-latency afferent inhibition (SAI/LAI)) provide information about intracortical inhibitory and facilitatory networks. This has long been done by the conventional TMS method of measuring changes in the size of the motor-evoked potentials (MEPs) in response to stimuli of constant intensity. An alternative threshold-tracking approach has recently been introduced whereby the stimulus intensity for a target amplitude is tracked. The diagnostic utility of threshold-tracking SICI in amyotrophic lateral sclerosis (ALS) has been shown in previous studies. However, threshold-tracking TMS has only been used in a few centers, in part due to the lack of readily available software but also perhaps due to uncertainty over its relationship to conventional single-and paired-pulse TMS measurements. A menu-driven suite of semi-automatic programs has been developed to facilitate the broader use of threshold-tracking TMS techniques and to enable direct comparisons with conventional amplitude measurements. These have been designed to control three types of magnetic stimulators and allow recording by a single operator of the common single-and paired-pulse TMS protocols. This paper shows how to record a number of single-and paired-pulse TMS protocols on healthy subjects and analyze the recordings. These TMS protocols are fast and easy to perform and can provide useful biomarkers in different neurological disorders, particularly neurodegenerative diseases such as ALS

Data sharing governance in sub-Saharan Africa during public health emergencies:gaps and guidance

While the COVID-19 pandemic has captured the attention of the global community since the end of 2019, deadly health pandemics are not new to Africa. Tuberculosis (TB), malaria and human immunodeficiency virus (HIV) count amongst other serious diseases that have had a catastrophic impact on the African continent. Effective responses to such pandemics require high-quality, comprehensive data sets that can inform policymaking and enhance healthcare decision-making. While data is driving the information economy in the 21st century, the scarcity in Africa of carefully curated, large epidemiologic data sources and analytical capacity to rapidly identify and understand emerging infectious diseases poses a major challenge to mounting a time-sensitive response to unfolding pandemics. Data access, sharing and transfer between countries are crucial to effectively managing current and future health pandemics. Data access and sharing, however, raises questions about personal privacy, the adequacy of governance mechanisms to regulate cross-border data flows, and ethical issues relating to the collection and use of personal data in the interests of public health. Sub-Saharan Africa’s most research-intensive countries are characterised by diverse data management and privacy governance frameworks. Such regional variance can impede time-sensitive data sharing and highlights the need for urgent governance reforms to facilitate effective decision-making in response to rapidly evolving public health threats.</p

Data sharing governance in sub-Saharan Africa during public health emergencies: Gaps and guidance

While the COVID-19 pandemic has captured the attention of the global community since the end of 2019, deadly health pandemics are not new to Africa. Tuberculosis (TB), malaria and human immunodeficiency virus (HIV) count amongst other serious diseases that have had a catastrophic impact on the African continent. Effective responses to such pandemics require high-quality, comprehensive data sets that can inform policymaking and enhance healthcare decision-making. While data is driving the information economy in the 21st century, the scarcity in Africa of carefully curated, large epidemiologic data sources and analytical capacity to rapidly identify and understand emerging infectious diseases poses a major challenge to mounting a time-sensitive response to unfolding pandemics. Data access, sharing and transfer between countries are crucial to effectively managing current and future health pandemics. Data access and sharing, however, raises questions about personal privacy, the adequacy of governance mechanisms to regulate cross-border data flows, and ethical issues relating to the collection and use of personal data in the interests of public health. Sub-Saharan Africa’s most research-intensive countries are characterised by diverse data management and privacy governance frameworks. Such regional variance can impede time-sensitive data sharing and highlights the need for urgent governance reforms to facilitate effective decision-making in response to rapidly evolving public health threats. Significance: We explore governance considerations that ought to apply to the collection, transfer, and use of data in public health emergencies. Specifically, we provide an overview of the prevailing data sharing governance landscape in selected African countries. In doing so, we identify limitations and gaps that impede effective data collation, sharing and analysis. This work could find utility amongst a range of stakeholders, including bioinformaticians, epidemiologists, artificial intelligence coders, and government decision-makers. While this work focuses primarily on an African context, the issues explored are of universal concern and therefore of relevance to a broader international audience

Açık işletmelerde optimum ekipman seçimi

In this study, an expert system for equipment selection in surface mining is developed. Expert system selects the optimum hydraulic excavator and off-highway truck combination, which is widely used in surface mining. A shell, developed by Intellicorp Company, KappaPC is selected for developing Equipment Selection Expert Systems. For KappaPC, rules and databases are prepared. Rules are determined according to equipment selection criteria, which are excavability, operating factors and material properties. Excavability is one of the most important parameter for equipment selection and a new excavability system is developed under the lights of the previously developed systems. Excavability and other parameters such as bearing capacity, bucket fill factor, bucket cycle time, etc. determines the best excavator. Truck selection is based on the remaining empty capacity after the excavator loads the truck with optimum number of bucket numbers (4-6). In this way, the expert system developed, selects the optimum hydraulic excavator and trucks. In order to determine optimum excavator and truck numbers the minimum capital cost of each combination is calculated. A combination, which has the minimum cost, is the optimum. In this fashion, an expert system, which optimally selects the hydraulic excavator and truck combination, employed in surface mining is developed.Keywords: Expert systems, equipment selection, surface miningBu &ccedil;alışmada, a&ccedil;ık işletmelerde son yıllarda kullanımı hızla artan bir &uuml;retim y&ouml;ntemi olan hidrolik ekskavat&ouml;r - kamyon y&ouml;ntemi i&ccedil;in en uygun ekipman se&ccedil;imi yapan bir uzman sistem KappaPC kabuğu kullanılarak geliştirilmiştir. Kazıcı ekipman se&ccedil;imindeki en &ouml;nemli kriterlerden birisi olan kazılabilirliğin belirlenmesi i&ccedil;in, daha &ouml;nce yapılmış olan &ccedil;alışmaların doğrultusunda uzman sistemde kullanılmak &uuml;zere bir kazılabilirlik sınıflaması oluşturulmuştur. Bununla birlikte, zeminin taşıma direnci, kep&ccedil;e dolma fakt&ouml;r&uuml;, kep&ccedil;e periyodu gibi diğer ekipman se&ccedil;imi parametrelerinin de dikkate alınmasıyla hidrolik ekskavat&ouml;r se&ccedil;imi yapılmaktadır. Kamyon kapasitesinin belirlenmesi ise, ekskavat&ouml;r tarafından doldurulan kamyonlarda ekskavat&ouml;r&uuml;n optimum kep&ccedil;e sayısı (4-6) ile doldurduğu toplam hacim ile kamyonun kasa hacmi arasında oluşan farktan dolayı boş kalan kasa hacmine bağlı olarak ger&ccedil;ekleştirilmektedir. Ekskavat&ouml;r ve kamyon satın alma maliyetleri kullanılarak, istenilen &uuml;retimi sağlayan her bir ekskavat&ouml;r ve kamyon kombinasyonunun maliyetleri i&ccedil;inde en d&uuml;ş&uuml;k maliyetin elde edildiği kombinasyon optimum ekskavat&ouml;r ve kamyon sayısı olarak belirlenmiştir.Anahtar Kelimeler: Uzman sistemler, ekipman se&ccedil;imi, a&ccedil;ık işletmeler.&nbsp

Malicious code detection in android : the role of sequence characteristics and disassembling methods

The acceptance and widespread use of the Android operating system drew the attention of both legitimate developers and malware authors, which resulted in a significant number of benign and malicious applications available on various online markets. Since the signature-based methods fall short for detecting malicious software effectively considering the vast number of applications, machine learning techniques in this field have also become widespread. In this context, stating the acquired accuracy values in the contingency tables in malware detection studies has become a popular and efficient method and enabled researchers to evaluate their methodologies comparatively. In this study, we wanted to investigate and emphasize the factors that may affect the accuracy values of the models managed by researchers, particularly the disassembly method and the input data characteristics. Firstly, we developed a model that tackles the malware detection problem from a Natural Language Processing (NLP) perspective using Long Short-Term Memory (LSTM). Then, we experimented with different base units (instruction, basic block, method, and class) and representations of source code obtained from three commonly used disassembling tools (JEB, IDA, and Apktool) and examined the results. Our findings exhibit that the disassembly method and different input representations affect the model results. More specifically, the datasets collected by the Apktool achieved better results compared to the other two disassemblers