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his research examines supply chain collaboration effects on organizational performance in global value chain (GVC) infrastructure by focusing on GVC disaggregation, market turbulence, inequality, market globalization, product diversity, exploitation, and technological breakthroughs. The research strives to develop a better understanding of global value chains through relational view, behavioral, and contingency theories along with institutional and stakeholder theories of supply chains. Based on conflicting insights from these theories, this research investigates how relationships and operational outcomes of collaboration fare when market turbulence is present. Data is obtained and analyzed from focal firms that are engaged in doing business in emerging markets (e.g., India), and headquartered in the United States. We investigate relational outcomes (e.g., trust, credibility, mutual respect, and relationship commitment) among supply chain partners, and found that these relational outcomes result in better operational outcomes (e.g., profitability, market share increase, revenue generation, etc.). From managerial standpoint, supply chain managers should focus on relational outcomes that can strengthen operational outcomes in GVCs resulting in stronger organizational performance. The research offers valuable insights for theory and practice of global value chains by focusing on the GVC disaggregation through the measurement of market turbulence, playing a key role in the success of collaborative buyer–supplier relationships (with a focus on US companies doing business in India) leading to an overall improved firm performance. 

The study examines the relationship between the big five personality traits (extroversion, agreeableness, conscientiousness, neuroticism, and openness) and robot likeability and successful HRI implementation in varying human-robot interaction (HRI) situations. Further, this research investigates the influence of human-like attributes in robots (a.k.a. robotic anthropomorphism) on the likeability of robots. The research found that robotic anthropomorphism positively influences the relationship between human personality variables (e.g., extraversion and agreeableness) and robot likeability in human interaction with social robots. Further, anthropomorphism positively influences extraversion and robot likeability during industrial robotic interactions with humans. Extraversion, agreeableness, and neuroticism were found to play a significant role. This research bridges the gap by providing an in-depth understanding of the big five human personality traits, robotic anthropomorphism, and robot likeability in social-collaborative robotics. 

This paper aims to examines the moderating effect of small vs large supply base size on the relationship between strategic sustainable purchasing (SSP) and organizational sustainability performance (OSP). SSP is conceptualized as a dynamic capability consisting of strategic purchasing and environmental purchasing. Environmental collaboration is conceptualized as a mediator between SSP and OSP. Extant research has not examined the effect of the size of the supply base on the relationship between SSP and OSP. 

Personalized learning environments requiring the elicitation of a student’s knowledge state have inspired researchers to propose distinct models to understand that knowledge state. Recently, the spotlight has shone on comparisons between traditional, interpretable models such as Bayesian Knowledge Tracing (BKT) and complex, opaque neural network models such as Deep Knowledge Tracing (DKT). Although DKT appears to be a powerful predictive model, little effort has been expended to dissect the source of its strength. We begin with the observation that DKT differs from BKT along three dimensions: (1) DKT is a neural network with many free parameters, whereas BKT is a probabilistic model with few free parameters; (2) a single instance of DKT is used to model all skills in a domain, whereas a separate instance of BKT is constructed for each skill; and (3) the input to DKT interlaces practice from multiple skills, whereas the input to BKT is separated by skill. We tease apart these three dimensions by constructing versions of DKT which are trained on single skills and which are trained on sequences separated by skill. Exploration of three data sets reveals that dimensions (1) and (3) are critical; dimension (2) is not. Our investigation gives us insight into the structural regularities in the data that DKT is able to exploit but that BKT cannot. 

A first search for beyond the standard model physics in jet multiplicity patterns of multilepton events is presented, using a data sample corresponding to an integrated luminosity of $138{\mathrm{fb}}^{-1}$of 13 TeV proton-proton collisions recorded by the CMS detector at the LHC. The search uses observed jet multiplicity distributions in one-, two-, and four-lepton events to explore possible enhancements in jet production rate in three-lepton events with and without bottom quarks. The data are found to be consistent with the standard model expectation. The results are interpreted in terms of supersymmetric production of electroweak chargino-neutralino superpartners with cascade decays terminating in prompt hadronic $R$-parity violating interactions. 

A search for the rare decay $D^0\to {\mu }^{+}{\mu }^{-}$is reported using proton-proton collision events at $\sqrt{s}=13.6\mathrm{TeV}$collected by the CMS detector in 2022–2023, corresponding to an integrated luminosity of $64.5{\mathrm{fb}}^{-1}$. This is the first analysis to use a newly developed inclusive dimuon trigger, expanding the scope of the CMS flavor physics program. The search uses $D^0$mesons obtained from $D^{*+}\to D^0{\pi }^{+}$decays. No significant excess is observed. A limit on the branching fraction of $\mathcal{B}(D^0\to {\mu }^{+}{\mu }^{-})<2.4\times {10}^{-9}$at 95% confidence level is set. This is the most stringent upper limit set on any flavor changing neutral current decay in the charm sector. 

A<sc>bstract</sc> A search for a heavy pseudoscalar Higgs boson, A, decaying to a 125 GeV Higgs boson h and a Z boson is presented. The h boson is identified via its decay to a pair of tau leptons, while the Z boson is identified via its decay to a pair of electrons or muons. The search targets the production of the A boson via the gluon-gluon fusion process, gg → A, and in association with bottom quarks,$$\text{b}\overline{\text{b}}\text{A }$$. The analysis uses a data sample corresponding to an integrated luminosity of 138 fb⁻¹collected with the CMS detector at the CERN LHC in proton-proton collisions at a centre-of-mass energy of$$\sqrt{s}=13$$TeV. Constraints are set on the product of the cross sections of the A production mechanisms and the A → Zh decay branching fraction. The observed (expected) upper limit at 95% confidence level ranges from 0.049 (0.060) pb to 1.02 (0.79) pb for the gg → A process and from 0.053 (0.059) pb to 0.79 (0.61) pb for the$$\text{b}\overline{\text{b}}\text{A }$$process in the probed range of the A boson mass,m_A, from 225 GeV to 1 TeV. The results of the search are used to constrain parameters within the$${\text{M}}_{\text{h},\text{EFT}}^{125}$$benchmark scenario of the minimal supersymmetric extension of the standard model. Values of tanβbelow 2.2 are excluded in this scenario at 95% confidence level for allm_Avalues in the range from 225 to 350 GeV.