Incorporating topographic factors in nonlinear mixed-effects models for aboveground biomass of natural Simao pine in Yunnan,China

A total of 128 Simao pine trees （Pinus kesiya var. langbianensis） from three regions of Pu＇er City, Yunnan Province, People＇s Republic of China, were destructively sampled to obtain tree aboveground biomass （AGB）. Tree variables such as diameter at breast height and total height, and topographical factors such as altitude, aspect of slope, and degree of slope were recorded. We considered the region and site quality classes as the ran- dom-effects, and the topographic variables as the fixed- effects. We fitted a total of eight models as follows： least- squares nonlinear models （BM）, least-squares nonlinear models with the topographic factors （BMT）, nonlinear mixed-effects models with region as single random-effects （NLME-RE）, nonlinear mixed-effects models with site as single random-effects （NLME-SE）, nonlinear mixed-ef- fects models with the two-level nested region and site random-effects （TLNLME）, NLME-RE with the fixed-ef- fects of topographic factors （NLMET-RE）, NLME-SE with the fixed-effects of topographic factors （NLMET-SE）, and TLNLME with the fixed-effects of topographic factors （TLNLMET）. The eight models were compared by modelfitting and prediction statistics. The results showed： model fitting was improved by considering random-effects of region or site, or both. The models with the fixed-effects of topographic factors had better model fitting. According to AIC and BIC, the model fitting was ranked as TLNLME 〉 NLMET-RE 〉 NLME-RE.〉 NLMET-SE 〉 TLNLMET 〉 NLME-SE 〉 BMT 〉 BM. The differences among these models for model prediction were small. The model pre- diction was ranked as TLNLME 〉 NLME-RE 〉 NLME- SE 〉 NLMET-RE 〉 NLMET-SE 〉 TLNLMET 〉 BMT 〉 BM. However, all eight models had relatively high prediction precision （〉90 %）. Thus, the best model should be chosen based on the available data when using the model to predict individual tree AGB.

Influence of staff number and internal constellation on surgical site infection in an operating room

Prediction of bacteria-carrying particle （BCP） dispersion and particle distribution released from staffmem- bers in an operating room （OR） is very important for creating and sustaining a safe indoor environment. Postoperative wound infections cause significant morbidity and mortality, and contribute to increased hospitalization time. Increasing the number of personnel within the OR disrupts the ventilation airflow pattern and causes enhanced contamination risk in the area of an open wound. Whether the amount of staffwithin the OR influences the BCP distribution in the surgical zone has rarely been investigated. This study was conducted to explore the influence of the number of personnel in the OR on the airflow field and the BCP distribution. This was performed by applying a numerical calculation to map the airflow field and Lagrangian particle tracking （LPT） for the BCP phase. The results are reported both for active sampling and passive monitoring approaches. Not surprisingly, a growing trend in the BCP concentration （cfu/ms） was observed as the amount of staff in the OR increased. Passive sampling shows unpredictable results due to the sedimentation rate, especially for small particles （5-10 i^m）. Risk factors for surgical site infections （SSls） must be well understood to develop more effective prevention programs.