Die Generation Y : Arbeitnehmer der Zukunft. Herausforderungen für das Personalmanagement

Die Generation der ab den 1980er Jahren Geborenen, genannt Generation Y, tritt seit einiger Zeit als Arbeitnehmer in die Arbeitsmärkte ein. Im Jahr 2020 wird diese Gruppe die Hälfte der Arbeitnehmer in der Bundesrepublik ausmachen. Lebensstil, Globalisierung, kulturelles und mediales Angebot führen bei dieser Generation auch in der Arbeitswelt zu anderen Erwartungen und Ansprüchen als dies bei vorherigen Generationen der Fall war. In Literatur und Studien wird diese Generation fast ausschließlich als selbstbewusst, ehrgeizig, aufstrebend, sozial engagiert und mobil beschrieben. Vor dem Hintergrund des zu erwartenden Fachkräftemangels widmet sich diese Arbeit unter anderem der Frage, mit welchen Instrumenten das Personalmanagement der Unternehmen auf diese Situation reagieren kann, um bei der Gewinnung und Bindung von Mitarbeitern erfolgreich zu sein

3D Indoor Instance Segmentation in an Open-World

Existing 3D instance segmentation methods typically assume that all semantic classes to be segmented would be available during training and only seen categories are segmented at inference. We argue that such a closed-world assumption is restrictive and explore for the first time 3D indoor instance segmentation in an open-world setting, where the model is allowed to distinguish a set of known classes as well as identify an unknown object as unknown and then later incrementally learning the semantic category of the unknown when the corresponding category labels are available. To this end, we introduce an open-world 3D indoor instance segmentation method, where an auto-labeling scheme is employed to produce pseudo-labels during training and induce separation to separate known and unknown category labels. We further improve the pseudo-labels quality at inference by adjusting the unknown class probability based on the objectness score distribution. We also introduce carefully curated open-world splits leveraging realistic scenarios based on inherent object distribution, region-based indoor scene exploration and randomness aspect of open-world classes. Extensive experiments reveal the efficacy of the proposed contributions leading to promising open-world 3D instance segmentation performance.Comment: Accepted at NeurIPS 202

Chronic inflammatory demyelinating polyneuropathy as a paraneoplastic manifestation of colorectal carcinoma: What do we know?

The pathogenesis of chronic inflammatory demyelinating polyneuropathy (CIDP) remains highly debated among experts. In recent times, literature has divulged a riveting yet plausible association between colorectal carcinoma and CIDP as its paraneoplastic ramification. Initially, research suggested that chronic inflammatory demyelinating polyneuropathy (CIDP) was caused solely by macrophages. However, recent studies have insinuated towards an alternative pathogenesis, one involving autoantibodies against paranodal junction proteins. These two distinct mechanisms are the primary contenders responsible for the development of CIDP, rendering it an elusive paraneoplastic manifestation of colorectal carcinoma.</p

Adopting smart supply chain and smart technologies to improve operational performance in manufacturing industry

The manufacturing industry plays a crucial role in the economy of many countries, but there is a lack of expertise in implementing smart supply chains and smart technologies. This study aims to investigate the relationship between smart supply chains, smart technologies, and operational performance in the manufacturing industry. A survey questionnaire was conducted among registered manufacturing industries, and the results were analyzed using Smart PLS to test 10 hypotheses. Four hypotheses were supported out of 119 responses received through simple random sampling. The study suggests implementing instrumented supply chains using smart technologies can enhance operational performance. The findings provide valuable insights for policymakers, academics, and industry practitioners to improve the competitiveness of the manufacturing industry. This research emphasizes the importance of smart supply chains and smart technologies in achieving operational excellence, and further studies are necessary to address the identified limitations and contribute to a deeper understanding of the role of smart technologies in the supply chain’s digitalization

Digital supply chain transformation: The role of smart technologies on operational performance in manufacturing industry

This study aims to investigate the impact of digital supply chains and smart technology on the operational performance of the manufacturing industry. Due to the lack of knowledge and guidance in this area, the adoption of smart technology throughout the supply chain is limited, leading to poor operational performance. Therefore, the purpose of this study is to investigate how smart technology and digital supply chain transformation can improve operational performance. To test hypotheses and accomplish study goals, the Resource-Based View (RBV) theory was combined with a quantitative research strategy. The study population of companies was obtained from a manufacturing directory, and a minimum sample size of 107 companies was determined using G*Power. Additionally, 600 online surveys were sent to the manufacturing companies, resulting in a response rate of 17.83%. Data analysis was conducted using Smart-PLS 4.0 software, and eight of the 10 hypotheses were supported. The findings showed that smart technologies completely mediate the link between digital transformation and relationship performance, emphasizing the need for manufacturing organizations to focus on incorporating smart technology into their supply chain to enhance operational performance. The study concludes by presenting theoretical and practical implications, limitations, and recommendations

Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models from Fetal Echocardiogram: A Pilot Study

Congenital heart defects (CHDs) are a leading cause of death in infants under 1 year of age. Prenatal intervention can reduce the risk of postnatal serious CHD patients, but current diagnosis is based on qualitative criteria, which can lead to variability in diagnosis between clinicians. Objectives: To detect morphological and temporal changes in cardiac ultrasound (US) videos of fetuses with hypoplastic left heart syndrome (HLHS) using deep learning models. A small cohort of 9 healthy and 13 HLHS patients were enrolled, and ultrasound videos at three gestational time points were collected. The videos were preprocessed and segmented to cardiac cycle videos, and five different deep learning CNN-LSTM models were trained (MobileNetv2, ResNet18, ResNet50, DenseNet121, and GoogleNet). The top-performing three models were used to develop a novel stacking CNN-LSTM model, which was trained using five-fold cross-validation to classify HLHS and healthy patients. The stacking CNN-LSTM model outperformed other pre-trained CNN-LSTM models with the accuracy, precision, sensitivity, F1 score, and specificity of 90.5%, 92.5%, 92.5%, 92.5%, and 85%, respectively for video-wise classification, and with the accuracy, precision, sensitivity, F1 score, and specificity of 90.5%, 92.5%, 92.5%, 92.5%, and 85%, respectively for subject-wise classification using ultrasound videos. This study demonstrates the potential of using deep learning models to classify CHD prenatal patients using ultrasound videos, which can aid in the objective assessment of the disease in a clinical setting.This study was funded by Qatar National Research Fund (QNRF), National Priorities Research Program (NPRP 10-0123-170222). The open access publication of this article was funded by the Qatar National Library

Predictors of Medication Adherence and Blood Pressure Control among Saudi Hypertensive Patients Attending Primary Care Clinics: A Cross-Sectional Study

Purpose To assess the level of medication adherence and to investigate predictors of medication adherence and blood pressure control among hypertensive patients attending primary healthcare clinics in Makkah, Saudi Arabia. Patients and methods Hypertensive patients meeting the eligibility criteria were recruited from eight primary care clinics between January and May 2016 for this study. The patients completed Arabic version of Morisky Medication Adherence Scale (MMAS-8), an eight-item validated, self-reported measure to assess medication adherence. A structured data collection form was used to record patients’ sociodemographic, medical and medication data. Results Two hundred and four patients, of which 71.6% were females, participated in the study. Patients’ mean age was 59.1 (SD 12.2). The mean number of medication used by patients was 4.4 (SD 1.89). More than half (110; 54%) of the patients were non-adherent to their medications (MMAS score 65 years (OR 2.0 [95% CI: 1.0–4.2; P = 0.04]), and being diabetic (OR 0.25 [95% CI: 0.1–0.6; P = 0.04]) were found to be independent predictors of medication adherence. Conclusion Medication adherence is alarmingly low among hypertensive patients attending primary care clinics in Saudi Arabia which may partly explain observed poor blood pressure control. There is a clear need to educate patients about the importance of medication adherence and its impact on improving clinical outcomes. Future research should identify barriers to medication adherence among Saudi hypertensive patients

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Assessment of the prevalence of radix entomolaris and distolingual canal in mandibular first molars in 15 countries: a multinational cross-sectional study with meta-analysis

Aim: The aim of this study was two-folded: i) to assess the prevalence of Distolingual Canal (DLC) and Radix Entomolaris (RE) in Mandibular First Molars (M1Ms), using Cone Beam Computed Tomography (CBCT) images and ii) to assess the impact of sociodemographic factors on the prevalence of these conditions worldwide. Methods: CBCT images were scanned retrospectively and the ones including bilateral M1Ms were included in the study. The evaluation was performed by 1 researcher in each country, trained with CBCT technology. A written and video instruction program explaining the protocol to be followed step-by-step was provided to all observers to calibrate them. The CBCT imaging screening procedure consisted of evaluating axial sections from coronal to apical. The presence of DLC and RE in M1Ms (yes/no) was identified and recorded. Results: Six thousand three hundred four CBCTs, representing 12,608 M1Ms, were evaluated. A significant difference was found between countries regarding the prevalence of both RE and DLC (P .05). Conclusion: The overall prevalence of RE and DLC in M1Ms was 3% and 22%. Additionally, both RE and DLC showed substantial bilaterally. These variations should be considered by endodontic clinicians during endodontic procedures in order to avoid potential complications