Corporate social responsibility performance and sustainability reporting in SMEs: An analysis of owner-managers' perceptions

Public and private organisations promote corporate social responsibility (CSR) practices in small and medium enterprises (SMEs) to achieve competitive advantages in their relationship with stakeholders. Different studies indicate that SMEs have found benefits in their CSR performance. The aim of the present study is contributing to the knowledge of the perception and motivation of SME managers on the performance of CSR, considering the stakeholder theory, through a qualitative case study in two different economic environments and institutional influences: Spanish and Peruvian. It is found that the values of the owners and managers direct the policies of CSR. In some cases, the demands of employees and consumers are satisfied to obtain benefits; however, in other cases, those demands are satisfied with a non-instrumental approach

Production planning in 3D printing factories

[EN] Production planning in 3D printing factories brings new challenges among which the scheduling of parts to be produced stands out. A main issue is to increase the efficiency of the plant and 3D printers productivity. Planning, scheduling, and nesting in 3D printing are recurrent problems in the search for new techniques to promote the development of this technology. In this work, we address the problem for the suppliers that have to schedule their daily production. This problem is part of the LONJA3D model, a managed 3D printing market where the parts ordered by the customers are reorganized into new batches so that suppliers can optimize their production capacity. In this paper, we propose a method derived from the design of combinatorial auctions to solve the nesting problem in 3D printing. First, we propose the use of a heuristic to create potential manufacturing batches. Then, we compute the expected return for each batch. The selected batch should generate the highest income. Several experiments have been tested to validate the process. This method is a first approach to the planning problem in 3D printing and further research is proposed to improve the procedure.This research has been partially financed by the project: “Lonja de Impresión 3D para la Industria 4.0 y la Empresa Digital (LONJA3D)” funded by the Regional Government of Castile and Leon and the European Regional Development Fund (ERDF, FEDER) with grant VA049P17.De Antón, J.; Senovilla, J.; González, J.; Acebes, F.; Pajares, J. (2020). Production planning in 3D printing factories. International Journal of Production Management and Engineering. 8(2):75-86. https://doi.org/10.4995/ijpme.2020.12944OJS758682Canellidis, V., Giannatsis, J., & Dedoussis, V. (2013). Efficient parts nesting schemes for improving stereolithography utilization. CAD Computer Aided Design, 45(5), 875-886. https://doi.org/10.1016/j.cad.2012.12.002Chergui, A., Hadj-Hamou, K., & Vignat, F. (2018). Production scheduling and nesting in additive manufacturing. Computers and Industrial Engineering, 126(May), 292-301. https://doi.org/10.1016/j.cie.2018.09.048Cui, Y. (2007). An exact algorithm for generating homogenous T-shape cutting patterns. Computers & Operations Research, 34(4), 1107-1120. https://doi.org/https://doi.org/10.1016/j.cor.2005.05.025Dvorak, F., Micali, M., & Mathieu, M. (2018). Planning and scheduling in additive manufacturing. Inteligencia Artificial, 21(62), 40-52. https://doi.org/10.4114/intartif.vol21iss62pp40-52Gogate, A. S., & Pande, S. S. (2008). Intelligent layout planning for rapid prototyping. International Journal of Production Research, 46(20), 5607-5631. https://doi.org/10.1080/00207540701277002Gupta, M. C., & Boyd, L. H. (2008). Theory of constraints: A theory for operations management. International Journal of Operations and Production Management, 28(10), 991-1012. https://doi.org/10.1108/01443570810903122Jakobs, S. (1996). On genetic algorithms for the packing of polygons. European Journal of Operational Research, 88(1), 165-181. https://doi.org/10.1016/0377-2217(94)00166-9Kucukkoc, I. (2019). MILP models to minimise makespan in additive manufacturing machine scheduling problems. Computers and Operations Research, 105, 58-67. https://doi.org/10.1016/j.cor.2019.01.006Kucukkoc, I., Li, Q., & Zhang, D. Z. (2016). Increasing the utilisation of additive manufacturing and 3D printing machines considering order delivery times. In 19th International Working Seminar on Production Economics (pp. 195-201). Innsbruck, Austria.Li, Q., Kucukkoc, I., & Zhang, D. Z. (2017). Production planning in additive manufacturing and 3D printing. Computers and Operations Research, 83, 1339-1351. https://doi.org/10.1016/j.cor.2017.01.013López-Paredes, A., Pajares, J., Martín, N., del Olmo, R., & Castillo, S. (2018). Application of combinatorial auctions to create a 3Dprinting market. Advancing in Engineering Network, Castro and Gimenez Eds. Lecture Notes in Management and Industrial Engineering (In Press), 12-13.Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B., Vosooghnia, A., Emamian, S. S., & Gisario, A. (2019). The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review. Applied Sciences, 9(18), 3865. https://doi.org/10.3390/app9183865Piili, H., Happonen, A., Väistö, T., Venkataramanan, V., Partanen, J., & Salminen, A. (2015). Cost Estimation of Laser Additive Manufacturing of Stainless Steel. Physics Procedia, 78(August), 388-396. https://doi.org/10.1016/j.phpro.2015.11.053Shaffer, S., Yang, K., Vargas, J., Di Prima, M. A., & Voit, W. (2014). On reducing anisotropy in 3D printed polymers via ionizing radiation. Polymer, 55(23), 5969-5979. https://doi.org/10.1016/j.polymer.2014.07.054Singhal, S. K., Pandey, A. P., Pandey, P. M., & Nagpal, A. K. (2005). Optimum Part Deposition Orientation in Stereolithography. Computer-Aided Design and Applications, 2(1-4), 319-328. https://doi.org/10.1080/16864360.2005.10738380Sung‐Hoon, A. (2002). Anisotropic material properties of fused deposition modeling ABS. Rapid Prototyping Journal, 8(4), 248-257. https://doi.org/10.1108/13552540210441166Thomas, D. S., & Gilbert, S. W. (2015). Costs and cost effectiveness of additive manufacturing: A literature review and discussion. Additive Manufacturing: Costs, Cost Effectiveness and Industry Economics, 1-96. https://doi.org/10.6028/NIST.SP.1176Toro, E., Garces, A., & Ruiz, H. (2008). Two dimensional packing problem using a hybrid constructive algorithm of variable neighborhood search and simulated annealing. Revista Facultad de Ingeniería Universidad de Antioquia, 119-131.Toro, E., & Granada-Echeverri, M. (2007). Problema de empaquetamiento rectangular bidimensional tipo guillotina resuelto por algoritmos genéticos. Scientia Et Technica.Wang, Y., Zheng, P., Xu, X., Yang, H., & Zou, J. (2019). Production planning for cloud-based additive manufacturing-A computer vision-based approach. Robotics and Computer-Integrated Manufacturing, 58(March), 145-157. https://doi.org/10.1016/j.rcim.2019.03.003Wodziak, J. R., Fadel, G. M., & Kirschman, C. (1994). A Genetic Algorithm for Optimizing Multiple Part Placement to Reduce Build Time. Proceedings of the Fifth International Conference on Rapid Prototyping., (May), 201,210.Zhang, Y., Gupta, R. K., & Bernard, A. (2016). Two-dimensional placement optimization for multi-parts production in additive manufacturing. Robotics and Computer-Integrated Manufacturing, 38, 102-117. https://doi.org/10.1016/j.rcim.2015.11.003Zhao, Z., Zhang, L., & Cui, J. (2018). A 3D printing task packing algorithm based on rectangle packing in cloud manufacturing. Lecture Notes in Electrical Engineering, 460, 21-31. https://doi.org/10.1007/978-981-10-6499-9_3Zhou, L., Zhang, L., Laili, Y., Zhao, C., & Xiao, Y. (2018). Multi-task scheduling of distributed 3D printing services in cloud manufacturing. International Journal of Advanced Manufacturing Technology, 96(9-12), 3003-3017. https://doi.org/10.1007/s00170-017-1543-zZhou, L., Zhang, L., & Xu, Y. (2016). Research on the relationships of customized service attributes in cloud manufacturing. ASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016, 2, 1-8. https://doi.org/10.1115/MSEC2016-853

Mejoras para el ajuste de controladores robustos mediante optimización multiobjetivo en procesos con incertidumbre

[ES] Breve descripción de algunas de las ideas sobre las que está trabajando el grupo de investigación CPOH del Instituto de Automática e Informática Industrial (ai2) de la Universitat Politècnica de Valéncia (UPV). En particular, mostramos las ideas básicas correspondientes a una de las líneas de trabajo para el ajuste mediante optimización multiobjetivo de controladores robustos para todo tipo de proceso que presenten incertidumbre paramétrica en su modelo. El objetivo fundamental que se se persigue es conseguir una metodología y algoritmos que permitan el ajuste robusto de controladores con un coste computacional viable. Las ideas que se describen usan soluciones casi-óptimas para mejorar la exploración de soluciones robustas sin sacrificar el coste computacional.[EN] Brief description of some of the ideas on which the CPOH research group of the Instituto de Automática e Informática Industrial (ai2) of the Universitat Politécnica de Valéncia (UPV) is working. In particular, we show the basic ideas corresponding to one of the research lines for the tuning using multiobjective optimization of robust controllers for all types of processes that present parametric uncertainty in their model. The main objective is to achieve a methodology and algorithms that allow the robust tuning of controllers with a viable computational cost. The ideas described use nearly optimal solutions to improve the exploration of robust solutions without sacrificing computational cost

Automatic detection of crop rows in maize fields with high weeds pressure

This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu’s method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper

Development of a novel splice array platform and its application in the identification of alternative splice variants in lung cancer

Abstract Background Microarrays strategies, which allow for the characterization of thousands of alternative splice forms in a single test, can be applied to identify differential alternative splicing events. In this study, a novel splice array approach was developed, including the design of a high-density oligonucleotide array, a labeling procedure, and an algorithm to identify splice events. Results The array consisted of exon probes and thermodynamically balanced junction probes. Suboptimal probes were tagged and considered in the final analysis. An unbiased labeling protocol was developed using random primers. The algorithm used to distinguish changes in expression from changes in splicing was calibrated using internal non-spliced control sequences. The performance of this splice array was validated with artificial constructs for CDC6, VEGF, and PCBP4 isoforms. The platform was then applied to the analysis of differential splice forms in lung cancer samples compared to matched normal lung tissue. Overexpression of splice isoforms was identified for genes encoding CEACAM1, FHL-1, MLPH, and SUSD2. None of these splicing isoforms had been previously associated with lung cancer. Conclusions This methodology enables the detection of alternative splicing events in complex biological samples, providing a powerful tool to identify novel diagnostic and prognostic biomarkers for cancer and other pathologies

Betaine-homocysteine S-methyltransferase deficiency causes increased susceptibility to noise-induced hearing loss associated with plasma hyperhomocysteinemia

Betaine-homocysteine S-methyltransferases (BHMTs) are methionine cycle enzymes that remethylate homocysteine; hence, their malfunction leads to hyperhomocysteinemia. Epidemiologic and experimental studies have revealed a correlation between hyperhomocysteinemia and hearing loss. Here, we have studied the expression of methionine cycle genes in the mouse cochlea and the impact of knocking out the Bhmt gene in the auditory receptor. We evaluated age-related changes in mouse hearing by recording auditory brainstem responses before and following exposure to noise. Also, we measured cochlear cytoarchitecture, gene expression by RNA-arrays and quantitative RT-PCR, and metabolite levels in liver and plasma by HPLC. Our results indicate that there is an age-dependent strain-specific expression of methionine cycle genes in the mouse cochlea and a further regulation during the response to noise damage. Loss of Bhmt did not cause an evident impact in the hearing acuity of young mice, but it produced higher threshold shifts and poorer recovery following noise challenge. Hearing loss was associated with increased cochlear injury, outer hair cell loss, altered expression of cochlear methionine cycle genes, and hyperhomocysteinemia. Our results suggest that BHMT plays a central role in the homeostasis of cochlear methionine metabolism and that Bhmt2 up-regulation could carry out a compensatory role in cochlear protection against noise injury in the absence of BHMT

Strategies to design clinical studies to identify predictive biomarkers in cancer research

The discovery of reliable biomarkers to predict efficacy and toxicity of anticancer drugs remains one of the key challenges in cancer research. Despite its relevance, no efficient study designs to identify promising candidate biomarkers have been established. This has led to the proliferation of a myriad of exploratory studies using dissimilar strategies, most of which fail to identify any promising targets and are seldom validated. The lack of a proper methodology also determines that many anti-cancer drugs are developed below their potential, due to failure to identify predictive biomarkers. While some drugs will be systematically administered to many patients who will not benefit from them, leading to unnecessary toxicities and costs, others will never reach registration due to our inability to identify the specific patient population in which they are active. Despite these drawbacks, a limited number of outstanding predictive biomarkers have been successfully identified and validated, and have changed the standard practice of oncology. In this manuscript, a multidisciplinary panel reviews how those key biomarkers were identified and, based on those experiences, proposes a methodological framework—the DESIGN guidelines—to standardize the clinical design of biomarker identification studies and to develop future research in this pivotal field

European registry on helicobacter pylori management: Effectiveness of first and second-line treatment in Spain

The management of Helicobacter pylori infection has to rely on previous local effectiveness due to the geographical variability of antibiotic resistance. The aim of this study was to evaluate the effectiveness of first and second-line H. pylori treatment in Spain, where the empirical prescription is recommended. A multicentre prospective non-interventional registry of the clinical practice of European gastroenterologists concerning H. pylori infection (Hp-EuReg) was developed, including patients from 2013 until June 2019. Effectiveness was evaluated descriptively and through a multivariate analysis concerning age, gender, presence of ulcer, proton-pump in-hibitor (PPI) dose, therapy duration and compliance. Overall, 53 Spanish hospitals were included, and 10, 267 patients received a first-line therapy. The best results were obtained with the 10-day bismuth single-capsule therapy (95% cure rate by intention-to-treat) and with both the 14-day bismuth-clarithromycin quadruple (PPI-bismuth-clarithromycin-amoxicillin, 91%) and the 14-day non-bismuth quadruple concomitant (PPI-clarithromycin-amoxicillin-metronidazole, 92%) therapies. Second-line therapies were prescribed to 2448 patients, with most-effective therapies being the triple quinolone (PPI-amoxicillin-levofloxacin/moxifloxacin) and the bismuth-levofloxacin quadruple schemes (PPI-bismuth-levofloxacin-amoxicillin) prescribed for 14 days (92%, 89% and 90% effective-ness, respectively), and the bismuth single-capsule (10 days, 88.5%). Compliance, longer duration and higher acid inhibition were associated with higher effectiveness. “Optimized” H. pylori therapies achieve over 90% success in Spain

YES1 drives lung cancer growth and progression and predicts sensitivity to dasatinib

Rationale: The characterization of new genetic alterations is essential to assign effective personalized therapies in non–small cell lung cancer (NSCLC). Furthermore, finding stratification biomarkers is essential for successful personalized therapies. Molecular alterations of YES1, a member of the SRC (proto-oncogene tyrosine-protein kinase Src) family kinases (SFKs), can be found in a significant subset of patients with lung cancer. Objectives: To evaluate YES1 (v-YES-1 Yamaguchi sarcoma viral oncogene homolog 1) genetic alteration as a therapeutic target and predictive biomarker of response to dasatinib in NSCLC. Methods: Functional significance was evaluated by in vivo models of NSCLC and metastasis and patient-derived xenografts. The efficacy of pharmacological and genetic (CRISPR [clustered regularly interspaced short palindromic repeats]/Cas9 [CRISPR-associated protein 9]) YES1 abrogation was also evaluated. In vitro functional assays for signaling, survival, and invasion were also performed. The association between YES1 alterations and prognosis was evaluated in clinical samples. Measurements and Main Results: We demonstrated that YES1 is essential for NSCLC carcinogenesis. Furthermore, YES1 overexpression induced metastatic spread in preclinical in vivo models. YES1 genetic depletion by CRISPR/Cas9 technology significantly reduced tumor growth and metastasis. YES1 effects were mainly driven by mTOR (mammalian target of rapamycin) signaling. Interestingly, cell lines and patient-derived xenograft models with YES1 gene amplifications presented a high sensitivity to dasatinib, an SFK inhibitor, pointing out YES1 status as a stratification biomarker for dasatinib response. Moreover, high YES1 protein expression was an independent predictor for poor prognosis in patients with lung cancer. Conclusions: YES1 is a promising therapeutic target in lung cancer. Our results provide support for the clinical evaluation of dasatinib treatment in a selected subset of patients using YES1 status as predictive biomarker for therapy

Genomic characterization of individuals presenting extreme phenotypes of high and low risk to develop tobacco-induced lung cancer

Single nucleotide polymorphisms (SNPs) may modulate individual susceptibility to carcinogens. We designed a genome-wide association study to characterize individuals presenting extreme phenotypes of high and low risk to develop tobacco-induced non-small cell lung cancer (NSCLC), and we validated our results. We hypothesized that this strategy would enrich the frequencies of the alleles that contribute to the observed traits. We genotyped 2.37 million SNPs in 95 extreme phenotype individuals, that is: heavy smokers that either developed NSCLC at an early age (extreme cases); or did not present NSCLC at an advanced age (extreme controls), selected from a discovery set (n=3631). We validated significant SNPs in 133 additional subjects with extreme phenotypes selected from databases including >39,000 individuals. Two SNPs were validated: rs12660420 (p(combined)=5.66x10(-5); ORcombined=2.80), mapping to a noncoding transcript exon of PDE10A; and rs6835978 (p(combined)=1.02x10(-4); ORcombined=2.57), an intronic variant in ATP10D. We assessed the relevance of both proteins in early-stage NSCLC. PDE10A and ATP10D mRNA expressions correlated with survival in 821 stage I-II NSCLC patients (p=0.01 and p<0.0001). PDE10A protein expression correlated with survival in 149 patients with stage I-II NSCLC (p=0.002). In conclusion, we validated two variants associated with extreme phenotypes of high and low risk of developing tobacco-induced NSCLC. Our findings may allow to identify individuals presenting high and low risk to develop tobacco-induced NSCLC and to characterize molecular mechanisms of carcinogenesis and resistance to develop NSCLC