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The article provides an overview of the Open Process Automation (OPA) Standard and its implementation in the industry. OPA aims to transform the industrial control-system market by providing open, secure, and interoperable systems based on a standards-based automation architecture. The top three business drivers for end-user companies are increasing operational benefits, improving cybersecurity, and reducing capital and lifecycle costs. OPA primarily targets the chemical process industries and is based on the Open Process Automation Forum's industry standard. The article also discusses the OPA reference architecture, conformance certification, and indicators of industry adoption. It highlights the progress and adoption of the OPA standard, including the emergence of new entrants in the industrial control system business ecosystem and the issuance of Requests for Proposals (RFPs) that require O- PAS conformance. The Coalition for Open Process Automation (COPA) is introduced as a group of companies offering an entry-point to OPA through the COPA QuickStart system and training program. The article concludes by mentioning the release of the O- PAS Version 2.1 Final and the ongoing certification of products for conformance to the standard. [Extracted from the article]

Feed intake and feed efficiency are economically important traits in beef cattle because feed is the greatest variable cost in production. Feed efficiency can be measured as feed conversion ratio (FCR, intake per unit gain) or residual feed intake (RFI, measured as DMI corrected for BW and growth rate, and sometimes a measure of body composition, usually carcass fatness, RF[I.sub.bf]). The goal of this study was to fine map QTL for these traits in beef cattle using 2,194 markers on 24 autosomes. The animals used were from 20 half-sib families originating from Angus, Charolais, and University of Alberta Hybrid bulls. A mixed model with random sire and fixed QTL effect nested within sire was used to test each location (cM) along the chromosomes. Threshold levels were determined at the chromosome and genome levels using 20,000 permutations. In total, 4 QTL exceeded the genome-wise threshold of P < 0.001, 3 exceeded at P < 0.01, 17 at P < 0.05, and 30 achieved significance at the chromosome-wise threshold level (at least P < 0.05). No QTL were detected on BTA 8, 16, and 27 above the 5% chromosome-wise significance threshold for any of the traits. Nineteen chromosomes contained RFI QTL significant at the chromosome-wise level. The RF[I.sub.bf] QTL results were generally similar to those of RFI, the positions being similar, but occasionally differing in the level of significance. Compared with RFI, fewer QTL were detected for both FCR and DMI, 12 and 4 QTL, respectively, at the genome-wise thresholds. Some chromosomes contained FCR QTL, but not RFI QTL, but all DMI QTL were on chromosomes where RFI QTL were detected. The most significant QTL for RFI was located on BTA 3 at 82 cM (P = 7.60 x [10.sup.-5]), for FCR on BTA 24 at 59 cM (P = 0.0002), and for DMI on BTA 7 at 54 cM (P = 1.38 x [10.sup.-5]). The RFI QTL that showed the most consistent results with previous RFI QTL mapping studies were on BTA 1, 7, 18, and 19. The identification of these QTL provides a starting point to identify genes affecting feed intake and efficiency for use in marker-assisted selection and management. Key words: beef cattle, feed efficiency, feed intake, quantitative trait loci, residual feed intake

Feed intake and feed efficiency of beef cattle are economically relevant traits. The study was conducted to identify QTL for feed intake and feed efficiency of beef cattle by using genotype information from 100 microsatellite markers and 355 SNP genotyped across 400 progeny of 20 Angus, Charolais, or Alberta Hybrid bulls. Traits analyzed include feedlot ADG, daily DMI, feed-to-gain ratio [F:G, which is the reciprocal of the efficiency of gain (G:F)], and residual feed intake (RFI). A mixed model with sire as random and QTL effects as fixed was used to generate an F-statistic profile across and within families for each trait along each chromosome, followed by empirical permutation tests to determine significance thresholds for QTL detection. Putative QTL for ADG (chromosome-wise P < 0.05) were detected across families on chromosomes 5 (130 cM), 6 (42 cM), 7 (84 cM), 11 (20 cM), 14 (74 cM), 16 (22 cM), 17 (9 cM), 18 (46 cM), 19 (53 cM), and 28 (23 cM). For DMI, putative QTL that exceeded the chromosome-wise P < 0.05 threshold were detected on chromosomes 1 (93 cM), 3 (123 cM), 15 (31 cM), 17 (81 cM), 18 (49 cM), 20 (56 cM), and 26 (69 cM) in the across-family analyses. Putative across-family QTL influencing F:G that exceeded the chromosome-wise P < 0.05 threshold were detected on chromosomes 3 (62 cM), 5 (129 cM), 7 (27 cM), 11 (16 cM), 16 (30 cM), 17 (81 cM), 22 (72 cM), 24 (55 cM), and 28 (24 cM). Putative QTL influencing RFI that exceeded the chromosome-wise P < 0.05 threshold were detected on chromosomes 1 (90 cM), 5 (129 cM), 7 (22 cM), 8 (80 cM), 12 (89 cM), 16 (41 cM), 17 (19 cM), and 26 (48 cM) in the across-family analyses. In addition, a total of 4, 6, 1, and 8 chromosomes showed suggestive evidence (chromosome-wise, P < 0.10) for putative ADG, DMI, F:G, and RFI QTL, respectively. Most of the QTL detected across families were also detected within families, although the locations across families were not necessarily the locations within families, which is likely because of differences among families in marker informativeness for the different linkage groups. The locations and direction of some of the QTL effects reported in this study suggest potentially favorable pleiotropic effects for the underlying genes. Further studies will be required to confirm these QTL in other populations so that they can be fine-mapped for potential applications in marker-assisted selection and management of beef cattle. Key words: beef cattle, feed intake, feed efficiency, quantitative trait loci

Describes some of the features of Lehigh University's (Pennsylvania) process modeling and control program. Highlights the creation and operation of the Chemical Process Modeling and Control Center (PMC). Outlines the program's philosophy, faculty, technical program, current research projects, and facilities. (TW)