Cross-cultural challenges to Expatriates in Finland

Organizations open their subsidiaries in different countries, for this purpose they send their employees overseas as expatriates for effective management and control of their business. Effective performance of expatriates is recognized as a major determinant in the success or failure of organisations but these expatriates face many challenges due to different culture and environment during their cross-cultural adjustment. The purpose of this exploratory, qualitative study was to determine the challenges facing Indian expatriates who are working in Finland and to determine what Factors are affecting their cross-cultural adjustment and job adjustment. Data was obtained through semi- structured interviews and short questioners to seven (7) Indian expatriates working in MNCs in Finland. The empirical Study shows that not a single factor which determines expatriate cross-cultural adjustment. There are many other factors such as adjustment of their family and spouse, Host country Languages skill, Personal qualities, organizational support, job factors and non-work factors affects expatriate cross-cultural adjustment in new environment. This study gives an idea about the improvement of expatriation process and further suggestions to companies to adopts and develop such measures which facilitates expatriates cross-cultural and job adjustment.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Superficial Parotidectomy by Retrograde approach through Marginal Mandibular Nerve Dissection

Background: Parotid gland is most commonly involved in tumors, comprising about 80% of the salivary gland neoplasms. Majority of parotid tumors are benign in nature, the most common being pleomorphic adenoma. Superficial Parotidectomy is the preferred treatment option, using either anterograde or retrograde approach. The objective of this study was to determine the post-operative facial nerve status and other complications following superficial Parotidectomy by retrograde dissection for benign lesions of parotid gland.Material and Methods: This prospective clinical study included a total of 22 patients who had superficial Parotidectomy by retrograde technique involving marginal mandibular nerve dissection. These patients were studied post-operatively for facial nerve status, Frey’s Syndrome, wound infection and salivary fistula.Results: Out of a total of 22 patients, 54.54% developed temporary facial palsy on ipsilateral angle of mouth and all of them recovered by the end of 3 months post-surgery. Two patients (9.09%) developed salivary gland fistula and both of them healed spontaneously within two weeks. Frey’s Syndrome and wound infection were not seen in any of the patients included in the study. Histopathology of these lesions revealed pleomorphic adenoma (n=20) and Warthin’s tumor (n=2), respectively.Conclusion: The use of marginal mandibular nerve as a landmark for retrograde dissection of facial nerve in superficial Parotidectomy is a reliable method to ensure lower percentage of facial nerve injury and associated complications

Artificial intelligence model of fuel blendings as a step toward the zero emissions optimization of a 660 MWe supercritical power plant performance

Accurately predicting fuel blends' lower heating values (LHV) is crucial for optimizing a power plant. In this paper, we employ multiple artificial intelligence (AI) and machine learning-based models for predicting the LHV of various fuel blends. Coal of two different ranks and two types of biomass is used in this study. One was the South African imported bituminous coal, and the other was lignite thar coal extracted from the Thar Coal Block-2 mine by Sind Engro Coal Mining Company, Pakistan. Two types of biomass, that is, sugarcane bagasse and rice husk, were obtained locally from a sugar mill and rice mill located in the vicinity of Sahiwal, Punjab. Bituminous coal mixture with other coal types and both types of biomass are used with 10%, 20%, 30%, 40%, and 50% weight fractions, respectively. The calculation and model development procedure resulted in 91 different AI-based models. The best is the Ridge Regressor, a high-level end-to-end approach for fitting the model. The model can predict the LHV of the bituminous coal with lignite and biomass under a vast share of fuel blends

A rare case of Aeromonas hydrophila catheter related sepsis in a patient with chronic kidney disease receiving steroids and dialysis: a case report and review of Aeromonas infections in chronic kidney disease patients

Aeromonas hydrophila (AH) is an aquatic bacterium. We present a case of fifty-five-year-old gentleman with chronic kidney disease (CKD) due to crescentic IgA nephropathy who presented to us with fever. He was recently pulsed with methyl prednisolone followed by oral prednisolone and discharged on maintenance dialysis through a double lumen dialysis catheter. Blood culture from peripheral vein and double lumen dialysis catheter grew AH. We speculate low immunity due to steroids and uremia along with touch contamination of dialysis catheter by the patient or dialysis nurse could have led to this rare infection. Dialysis catheter related infection by AH is rare. We present our case here and take the opportunity to give a brief review of AH infections in CKD patients

Impact of CSR on Financial Performance of Banks: A Case Study

The aim of current study is to investigate the impact of CSRRI on bank’s financial performance. For this purpose, ROA, EPS and PAT are taken as proxies for measuring bank’s financial performance by using time series and panel data. The time span is from 2004 to 2017. The current study used HBL and MCB bank for analysis. The dependent variables are ROA, EPS and PAT while independent variables are CSRRI and bank size. To estimate the model, the current study used quantitative data to analyse the results by using descriptive analysis, correlation analysis, and multiple regression analysis. The findings of the current study revealed that the slope coefficient of intercept and CSRRI are positive except bank size which is negative in three models. In short, the CSRRI can Further, CSR reporting may provide welfare for both banks and econometric models suggests that socially responsible banks can not only attract large numbers of customers but also increases profitability

Calculating evidence-based renal replacement therapy – Introducing an excel-based calculator to improve prescribing and delivery in renal replacement therapy – A before and after study

Background Transferring the theoretical aspect of continuous renal replacement therapy to the bedside and delivering a given “dose” can be difficult. In research, the “dose” of renal replacement therapy is given as effluent flow rate in ml kg−1 h−1. Unfortunately, most machines require other information when they are initiating therapy, including blood flow rate, pre-blood pump flow rate, dialysate flow rate, etc. This can lead to confusion, resulting in patients receiving inappropriate doses of renal replacement therapy. Our aim was to design an excel calculator which would personalise patient's treatment, deliver an effective, evidence-based dose of renal replacement therapy without large variations in practice and prolong filter life. Our calculator prescribes a haemodialfiltration dose of 25 ml kg−1 h−1 whilst limiting the filtration fraction to 15%. MethodsWe compared the episodes of renal replacement therapy received by a historical group of patients, by retrieving their data stored on the haemofiltration machines, to a group where the calculator was used. In the second group, the data were gathered prospectively. ResultsThe median delivered dose reduced from 41.0 ml kg−1 h−1 to 26.8 ml kg−1 h−1 with reduced variability that was significantly closer to the aim of 25 ml kg−1.h−1 (p < 0.0001). The median treatment time increased from 8.5 h to 22.2 h (p = 0.00001). Conclusion Our calculator significantly reduces variation in prescriptions of continuous veno-venous haemodiafiltration and provides an evidence-based dose. It is easy to use and provides personal care for patients whilst optimizing continuous veno-venous haemodiafiltration delivery and treatment times

Machine learning assisted improved desalination pilot system design and experimentation for the circular economy

Desalination is among the most feasible solutions to supply sustainable and clean drinking water in water scarcity areas. In this regard, Multi-Effect Desalination (MED) systems are particularly preferred for harsh feeds (high temperature and salinity) because of their robust mode of operation for water production. However, maintaining the efficient operation of the MED systems is challenging because of the large system design and variables' interdependencies that are sensitive to the distillate production. Therefore, this research leverages the power of machine learning and optimization to estimate the optimal operating conditions for the maximum distillate production from the MED system. In the first step, detailed experimentation is conducted for distillate production against hot water temperature (HWT) varying from 38 to 70 °C, and feed water temperature (FWT) is changed from 34 to 42 °C. Whereas, the feed flow rate (FFR) is investigated to be varied nearly from 3.6 to 8.7 LPM in the three stages, i.e., FFR-S1, FFR-S2 and FFR-S3. The compiled dataset is used to make the process models of the MED system by three ML-based algorithms, i.e., Artificial Neural Network (ANN), Support Vector Machine (SVM), and Gaussian Process Regression (GPR) under rigorous hyperparameters optimization. GPR exhibited superior predictive performance than those of ANN and SVM on R2 value of 0.99 and RMSE of 0.026 LPM. Monte Carlo technique-based variable significance analysis revealed that the HWT has the highest effect on distillate production with a percentage significance of 95.6 %. Then Genetic Algorithm is used to maximize the distillate production with the GPR model embedded in the optimization problem. The GPR-GA driven maximum distillate production is estimated on HWT = 70 ± 0.5 °C, FWT = 40 ± 2.5 °C, FFR-S1 = 6 ± 2.6 LPM, FFR-S2 = 7 ± 1 LPM and FFR-S3 = 7 ± 1. The ML-GA-based system analysis and optimization of the MED system can boost the distillate production that promotes operation excellence and circular economy from the desalination sector

Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector

Augmentation of energy efficiency in the power generation systems can aid in decarbonizing the energy sector, which is also recognized by the International Energy Agency (IEA) as a solution to attain net-zero from the energy sector. With this reference, this article presents a framework incorporating artificial intelligence (AI) for improving the isentropic efficiency of a high-pressure (HP) steam turbine installed at a supercritical power plant. The data of the operating parameters taken from a supercritical 660 MW coal-fired power plant is well-distributed in the input and output spaces of the operating parameters. Based on hyperparameter tuning, two advanced AI modeling algorithms, i.e., artificial neural network (ANN) and support vector machine (SVM), are trained and, subsequently, validated. ANN, as turned out to be a better-performing model, is utilized to conduct the Monte Carlo technique-based sensitivity analysis toward the high-pressure (HP) turbine efficiency. Subsequently, the ANN model is deployed for evaluating the impact of individual or combination of operating parameters on the HP turbine efficiency under three real-power generation capacities of the power plant. The parametric study and nonlinear programming-based optimization techniques are applied to optimize the HP turbine efficiency. It is estimated that the HP turbine efficiency can be improved by 1.43, 5.09, and 3.40% as compared to that of the average values of input parameters for half-load, mid-load, and full-load power generation modes, respectively. The annual reduction in CO2 measuring 58.3, 123.5, and 70.8 kilo ton/year (kt/y) corresponds to half-load, mid-load, and full load, respectively, and noticeable mitigation of SO2, CH4, N2O, and Hg emissions is estimated for the three power generation modes of the power plant. The AI-based modeling and optimization analysis is conducted to enhance the operation excellence of the industrial-scale steam turbine that promotes higher-energy efficiency and contributes to the net-zero target from the energy sector

Acute kidney injury in lymphoma: a single centre experience

Background. Acute kidney injury (AKI) is a common but least studied complication of lymphoma. Objective. To determine the frequency and predictors of AKI in lymphoma and to study the impact of AKI on hospital stay and mortality. Methods. Retrospective review of medical records of hospitalized lymphoma patients aged ≥14 years between January 2008 and December 2011 was done. Results. Out of 365 patients, AKI was present in 31.8% (116/365). Multivariate logistic regression analysis showed that independent predictors for AKI included sepsis (odds ratio (OR) 3.76; 95% CI 1.83-7.72), aminoglycosides (OR 4.75; 95% CI 1.15-19.52), diuretics (OR 2.96; 95% CI 1.31-6.69), tumor lysis syndrome (OR 3.85; 95% CI 1.54-9.59), and R-CVP regimen (OR 4.70; 95% CI 1.20-18.36). AKI stages 2 and 3 was associated with increased hospital stay (OR 2.01; 95% CI 1.19-3.40). Conclusion. AKI was significantly associated with sepsis, aminoglycoside, diuretics, presence of tumor lysis syndrome, and use of R-CVP regimen. Presence of AKIN (Acute Kidney Injury Network) stages 2 and 3 AKI had increased hospital stay. AKI was also associated with increased mortality

Immense Industrialization And Their Air Prominent Pollutants Effect On Urban Air Quality Index

On the basis of the reported air quality index (API) and air pollutant monitoring data obtained at the Peshawar over the last seven years, the characteristics of air quality prominent pollutants and variation of the average annual concentrations of SO2, NO2total suspended particulate (TSP) fine particulates (PM10) CO and dust fall in Peshawar City were analyzed. Results showed that SO2and NO2were the prominent pollutants in the ambient air environment of Peshawar City. Of the prominent pollutants TSP accounted for nearly 63 % SO2, 32.8 ppb NO2, 147 ppb of CH4 , 13.8 ppb of CO, 94.5µg/m3of MC and 0.60 ppb of O3respectively in 2013. NO2to SO2 comparison ratio initially declined to 39.3 in 2009 and then starts to increase to 42.5 in 2010 while in 2013 reached upto 44.8 and O3 to SO2 ratio in the last year of observation, the ratio drop to 0.01830 µg/m3. Concentrations of air pollutants have shown a upward trend in recent years but they are generally worse than ambient air quality standards for EPA-USA, Pak and EU. SO2and NOx pollution were still serious impling that waste gas pollution from all kinds of vehicles had become a significant problem for environmental protection in Peshawar. The possible causes of worsening air quality were also discussed in this paper